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Data-driven design for sustainablebehavior

A case study in using data and conversational interfaces to

influence corporate settlement

Joakim Ljungren

August, 2017Master’s Thesis in Interaction Technology and Design, 30 credits

Supervisor at Umea University: Hakan GullikssonSupervisor at Daresay: Robert Holma

Examiner: Thomas Mejtoft

Umea UniversityDepartment of Applied Physics and Electronics

SE-901 87 UMEASWEDEN

Abstract

Interaction with digital products and interfaces concern more and more of human decision-makingand the problems regarding environmental, financial and social sustainability are consequencesmuch due to our behavior. The issues and goals of sustainable development therefore implieshow we have to think differently about digital design. In this paper, we examine the adequacyof influencing sustainable behavior with a data-driven design approach, applying a conversationaluser interface. A case study regarding the United Nation’s goals of technological developmentand economic distribution was conducted, to see if a hypothetical business with a proof-of-conceptdigital product could be effective in influencing where companies base their operations. The testresults showed a lack of usability and influence, but still suggested a potential with language-basedinterfaces. Even though the results could not prove anything, we argue that leveraging data analysisto design for sustainable behavior could be a very valuable strategy. A data-driven approach couldenable ambitions of profit and user experience to coincide with those of sustainability, within abusiness organization.

Contents

1 Introduction 61.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.3 Justification of case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3.1 Addressing the private sector . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.3.2 What sustainability goals to promote? . . . . . . . . . . . . . . . . . . . . . . 91.3.3 Target group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2 Background 112.1 About Daresay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.1.1 Working towards the global goals . . . . . . . . . . . . . . . . . . . . . . . . . 112.1.2 Investigating new technologies . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Theoretical framework 133.1 Designing for sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1.1 Human behavior and sustainability . . . . . . . . . . . . . . . . . . . . . . . . 143.1.2 Designing for sustainable behavior . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2 Big data and data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2.1 What is Big data? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2.2 Problems and opportunities with big data . . . . . . . . . . . . . . . . . . . . 183.2.3 The data supply chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2.4 Big data applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2.5 Linked data and the semantic web . . . . . . . . . . . . . . . . . . . . . . . . 223.2.6 Data-driven design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2.7 Artificial intelligence for decision-making . . . . . . . . . . . . . . . . . . . . 233.2.8 Data analysis for sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.3 Persuasive design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3.1 What is persuasive technology and design? . . . . . . . . . . . . . . . . . . . 253.3.2 How to perform persuasive design . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4 Conversational User Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.4.1 What are conversational user interfaces? . . . . . . . . . . . . . . . . . . . . . 283.4.2 Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.4.3 Why do people like messaging? . . . . . . . . . . . . . . . . . . . . . . . . . . 29

1

CONTENTS 2

3.4.4 CUI Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.5 Goals of sustainable development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.5.1 United Nations Global Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.5.2 Urbanization in general . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.5.3 Urbanization in Sweden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.5.4 Technological development and innovation . . . . . . . . . . . . . . . . . . . . 343.5.5 The role of sustainable cities . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4 Methods 364.1 Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.1.1 Literature studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.1.2 User research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.2 Prototype design and implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 384.2.1 System architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.2.2 Prototype design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.3.1 Evaluation framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.3.2 Test procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.3.3 Test transparency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.3.4 Result analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5 User research 535.1 Responses from entrepreneur interviews . . . . . . . . . . . . . . . . . . . . . . . . . 535.2 Regional versus national drivers of economic growth . . . . . . . . . . . . . . . . . . 54

6 Design rationale 566.1 Designing for sustainable behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566.2 A data-driven approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

6.2.1 Why data-driven? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 576.2.2 Data-driven how? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596.2.3 Interdisciplinary collaboration and linked data . . . . . . . . . . . . . . . . . 616.2.4 The gist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

6.3 A data-driven approach in practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636.3.1 Data strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636.3.2 Data implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646.3.3 Chatbot design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

7 Results 707.1 Results of influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707.2 Results of usability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717.3 General feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

8 Discussion 738.1 Result analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

8.2.1 Not enough training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 748.2.2 Inadequate simulation of the business scenario . . . . . . . . . . . . . . . . . 74

CONTENTS 3

8.2.3 Transparency trade-off . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 758.2.4 Survey response bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 758.2.5 Platform limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

8.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 778.4 Main learnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 778.5 A futuristic perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

9 Future work 809.1 Theory validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809.2 Business applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809.3 A global perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819.4 Interdisciplinary collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819.5 Conversational interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

10 Acknowledgements 82

A Pilot study: interview manuscript 86A.1 Entrepreneur interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

A.1.1 Company priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86A.1.2 Project explanation and value proposition . . . . . . . . . . . . . . . . . . . . 86

A.2 Municipal leader interview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

B Main study: online survey 88B.1 User experience – Part 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88B.2 User experience – Part 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89B.3 General questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

C Main study: ratings of influence 90

List of Figures

1.1 How the research is addressing sustainability in a general sense. . . . . . . . . . . . . 6

3.1 A pyramid of behavioral categories that illustrates the difficulty and profoundness ofchanging those behaviors [19]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2 The increase of data as internet traffic over time in trillion bytes per month and thefollowing expected trend [27]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3 How data flows through the cycle of collection, analysis, actions and feedback withina business [37]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.4 A visualization of the Fogg Behavior Model [21]. . . . . . . . . . . . . . . . . . . . . 273.5 An overview of the current bot market with the largest players and their inherence

[36]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.1 The chatbot avatar and Slack app description. . . . . . . . . . . . . . . . . . . . . . 384.2 A simplified visualization of the system components and communication between them. 394.3 The intent classification and response selection process. . . . . . . . . . . . . . . . . 414.4 The user interface is a simple text message conversation. Here shown on two different

devices, Desktop and a smartphone. . . . . . . . . . . . . . . . . . . . . . . . . . . . 424.5 Excerpts from the onboarding process. Example user interactions when starting a

conversation for the first time. Reading from left to right . . . . . . . . . . . . . . . 434.6 Screen captures from user interactions with Mio, trying to find a suitable office. . . . 434.7 Screen captures from user interactions with Mio, trying to find a suitable office. . . . 444.8 The chatbot is capable of some general conversation with the user, beyond finding

an office. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.9 A chart of different user research methods and in what scenarios that they are ap-

plicable [44]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.10 A statement in regards to the persuasive principle of raising ability, and how the

response alternatives was showed in the rating scale. . . . . . . . . . . . . . . . . . . 504.11 The scoring range of SUS with different factors of estimating the usability [2]. . . . . 514.12 The scoring range of SUS by percentile rankings [49]. . . . . . . . . . . . . . . . . . . 52

6.1 Three equally important drivers for a company from a holistic point of view. Adata-driven approach can make it easier to align them into one way forward. . . . . 58

6.2 A visualization of how the approach of data-driven sustainable design would work ina business logic with the data supply chain. . . . . . . . . . . . . . . . . . . . . . . . 60

4

LIST OF FIGURES 5

6.3 How the system architecture of the prototype could hypothetically work with anactual integrated business data analysis. See figure 4.2 and 6.2 for reference. . . . . . 66

7.1 Results from user ratings of the stand-alone statement in general influence. . . . . . 70

C.1 Rating of statement addressing the persuasive principle of ability/simplicity. . . . . . 90C.2 Rating of statement addressing the persuasive principle of motivation. . . . . . . . . 90C.3 Rating of statement addressing the persuasive principle of triggers. . . . . . . . . . . 91C.4 Rating of statement addressing the persuasive principle of physical attractiveness. . 91C.5 Rating of statement addressing the persuasive principle of psychology. . . . . . . . . 91C.6 Rating of statement addressing the persuasive principle of language. . . . . . . . . . 92C.7 Rating of statement addressing the persuasive principle of social dynamics. . . . . . 92C.8 Rating of statement addressing the persuasive principle of social role. . . . . . . . . . 92

Chapter 1

Introduction

Sustainable development is one of the most significant challenges of the modern era, which is aboutdevelopment that meets present and future needs together. The problem space of sustainability iswide and intangible as it addresses all aspects of reality in our social, environmental and financialsituations. The issues regarding sustainable development implicates a great deal to how we haveto think about design differently, and that definitely regards the design of digital products aswell.

This research evaluates an approach of using data-driven sustainable design strategies within abusiness to influence other companies decisions of where they base their operations. This is inaccordance with the United Nation’s sustainability goals of a balanced distribution in economicgrowth and innovation, as well as promoting a technological development in general. These areboth vital parts of establishing a sustainable development [53, 52]. Hence, the research is anattempt of fostering sustainable development through the design of digital products.

The specific case study of this work is to create a proof-of-concept prototype that can give adaptedsuggestions of locations for a company to open office. This prototype provides a target group witha personalized service and helps them grow as a business within a community. Concurrently, itserves the sustainability purpose also by trying to find optimal office locations elsewhere than themost urban areas. The experiment represents an effort in showing how to design for sustainablebehavior without compromising on the customer experience or business results. This is done byproposing an approach of utilizing data analysis and modern technology to impact a narrow targetgroup in favor of sustainability, with the aim of bringing insight to the bigger picture.

Figure 1.1: How the research is addressing sustainability in a general sense.

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7

There is an ongoing exponential increase of the amount of data collected in the world; everythingfrom individual footprints like internet activity to contextual information like environmental mea-surements [37, 18, 25]. Previous work suggests that society could leverage on the potential of dataanalysis in a larger extent, and that data-driven design has the potential to promote sustainabledevelopment [37, 8]. There exists a vast collection of frameworks and ideas for how to design sus-tainable digital products that are more or less advantageous to different scenarios [47, 58]. Westate in this work that if relevant data sources could be used to influence people and companies intaking more sustainable decisions, whatever the business scenario, that could be a valuable designprinciple to consider. It has the potential to serve not only individual profit but also public benefit.In short, it would mean a universal approach of designing for sustainable behavior, referred to infigure 1.1.

It can be difficult to see how the design of digital products can be used to combat sustainabilityissues. It is however heavily linked together with sustainable development in many ways. It isfor example in a direct sense affecting the physical world as people put digital products to use.Network communication, screen time and data storage in global proportions use great amounts ofnon-renewable energy and materials, which causes high levels of emissions. There are equally directcounteractions to this that can be taken as a business, like converting to green hosting and opti-mizing software for energy efficiency [22]. These actions however exclusively address environmentaland financial perspectives of sustainability.

The design of digital products is also very closely linked to peoples everyday behavior and decision-making. Technology is today all around us, constantly influencing our decisions [43]. This ”sec-ondary” footprint of how technology use affect our behavior is harder to measure, but it is inevitablethat people have to change behavior if we are to move towards a sustainable society [19]. The re-search of what role the human-computer interface can serve in the context of sustainability istherefore important to further reflect upon. This paper addresses the matter as in influencing sus-tainable user decisions by utilizing data insight and persuasive design principles in a user interface(UI).

Conversational user interfaces (CUI) is at the time of writing a hot topic in the design and technologycommunity. The notion concerns a certain breed of UIs, where the user can use natural languageto interact with a system. Instead of using conventional interaction design elements like inputforms and other graphical representations, a CUI is a way to communicate on more human termswhen using a computer, by text or speech. Although this interaction paradigm demands a lotmore underlying functionality from the interface than usual, it comes with a lot of opportunities.Alongside the recent advances in machine learning and more specifically natural language processing(NLP), CUIs have been rising in popularity as new ways of providing services. The prototypedeveloped for the case study includes a conversational interface, to evaluate the potential withnatural conversation in CUIs for influencing sustainable behavior.

Literature studies were concluded in the subjects of sustainability, sustainable design, data sci-ence, persuasive design and conversational interfaces. Early user research regarding the case studytarget group was also issued to better understand the experiment. A prototype was then devel-oped following the insights from the interpretation phase, and an evaluation of the prototype wasconducted.

1.1. Problem Statement 8

1.1 Problem Statement

There are many complex problems surrounding sustainability and the complications are spreadamong different areas. The United Nations (UN) has identified characteristics of the main issuesand address them by 17 goals, each with their specific targets. The goals are called UN’s global goalsof sustainable development. Part of the case study is to see if it can be possible to help companiesreach some of these goals by applying a data-driven approach to a proof-of-concept prototype, asan inspiration of how to design for sustainable development.

As mentioned, a way to design for sustainability could be to influence the decision-making withdesign and data insight. Part of the issue is the data analysis and how to find what is beneficialbehavior in each situation. But, the question is also how to craft this design and what a UI for thispurpose would optimally look like. With reference to how the potential of CUIs could amount to amore flexible and natural interaction pattern, it could be a suitable medium to practice design prin-ciples for influencing sustainable behavior. User interaction with a conversational interface meansinterpreting language, which could give more insight about the user context and their behavior.Possibly, it could furthermore be a better adapted medium for influencing an intended behavior,also with language. The focus of the case study is therefore on the effectiveness of a data-drivenconversational interface as a mean to influence.

In general terms, the evaluation of this study is to find how a CUI is fit to influence user decisions.As a case for this investigation, it is to see if a CUI is an effective way to influence where companieschoose to locate their office. The main research question is therefore:

How effective is a data-driven conversational user interface in influencing corporate settlementdecisions?

1.2 Objectives

The research project strives towards the following objectives:

1. Find a model of sustainable design that could be used in digital applications or future research,mainly within the scope of the case study.

2. Encourage businesses to incorporate a sustainability perspective in their business strategy toeasier meet goals of sustainable development.

3. Evaluate new aspects of an upcoming interaction pattern, as in conversational interfaces.Explore if this paradigm of user interaction can be used for the purpose of sustainable devel-opment.

1.3 Justification of case study

This section gives the motivations and reasons behind the specific case study, including a rationaleof how the specific target group for the experiment was chosen.

1.3. Justification of case study 9

1.3.1 Addressing the private sector

Influencing sustainable behavior can be done in many different contexts. There are individualdecisions and actions in daily life that everybody can take for sustainability. But, to accomplishsignificant change, appropriate decisions also have to come from leaders in the public and privatesectors. That means governmental policy makers and company executives or entrepreneurs [24, 39,52].

Technological development is a vital part of reaching a sustainable development, in several aspects[52]. Technology in the private sector has a tendency to develop more quickly than in the publicsector. Businesses also have a large impact on sustainability, regardless if it is positive or negative.There is high responsibility in their actions, whatever the field and the different impacts that theiractions implicates [39]. For those reasons, private companies emerges as a potent target group toinfluence with the aim to accomplish sustainable change.

The biggest challenge is how to link the quick technological development in the private sectorwith specific measures of sustainability [52]. Businesses are often driven by profit and growth.Perhaps with the right methods it can be possible to include a sustainability perspective in businessoperations without negative impacts on the financial results or customer experience. Perhaps thisthen in turn can have an impact also on other businesses, through business-to-business customerrelations, partnerships or other stakeholder associations.

1.3.2 What sustainability goals to promote?

There are problems of sustainable development in more developed countries, like Sweden, that aredifferent to those in less developed countries, but still connected to many of the UN’s global goalsof sustainable development1. Innovation and technological development definitely matters to sus-tainability [52]. An hypothesis of the case study is also that it matters in Sweden where innovationand economic development occurs and that this location aspect has an effect on sustainable de-velopment. The main evaluation of the study was also to be performed in Sweden where relevantsources of information would be easier to access.

We claim that companies who deliver software, digital design or technology solutions, regardless ifthey offer a service or product, can be less dependant on the local region compared to other typesof associations, but still contribute to the growth of this region. Software and technology compa-nies also have big potential to make improvements to the societal system with optimization andnew innovative solutions. Additionally, it is already today not uncommon with remote employeesworking at technology companies. Not everyone wants to live in a urban city and everyone hasdifferent preferences of their personal lifestyle. It is valuable to be able to work in a region that fitpersonal needs and still have the same career opportunities.

Henceforth, the drivers of the case study came to be focused on promoting economic and technolog-ical development and finding balance in the geographical distribution of that development.

1http://www.globalgoals.org/#the-goals A list of the UN 17 global goals for sustainable development withaccording explanations, accessed 2017-03-17

1.4. Thesis Outline 10

1.3.3 Target group

The resulting target group for the case study prototype is with respect to the discussed parametersSwedish companies working with digital design, software or technology in one way or another. Theexperiment portrays a business-to-business customer relation where this specific target group holdsthe role of the customer. The hypothetical business and prototype will in other words providea service that targets software and technology companies, and try to influence their sustainablefootprint. In this way, also improving the sustainable footprint of the prototype itself.

1.4 Thesis Outline

Chapter 2 - BackgroundPresents information about the collaborator Daresay and their ambitions in the subject.

Chapter 3 - Theoretical frameworkContains all theoretical material from literature studies that has been considered in the re-search.

Chapter 4 - MethodsThe work process of the research project and an overview of the case study prototype.

Chapter 5 - User researchResults from the introductory interviews.

Chapter 6 - Design rationaleA thorough rationale of how the data-driven approach could work according to the theoreticalframework and how the design decisions of the prototype were made.

Chapter 7 - ResultsResults from the main user test and survey evaluation.

Chapter 8 - DiscussionAnalysis of the case study and conclusions of the research.

Chapter 9 - Future WorkWhat could be the ongoing focus with this area of sustainable design in future work.

Chapter 10 - AcknowledgementsAppreciation to contributors of the research.

Chapter 2

Background

This paper is a master thesis that was done in collaboration with Daresay Digital Agency. Thischapter presents information about the agency and what the motive is from the company’s per-spective.

2.1 About Daresay

Daresay is a design and digital transformation agency based in Stockholm, Sweden. The foundationof their work is based on user-centered design in creating long-term valuable digital solutions. Thecompany strives towards using design and modern technology to help their clients meet the needsof their end users.

2.1.1 Working towards the global goals

Daresay has initiated an ambition to work towards the UN’s 17 Global Goals for SustainableDevelopment. It is part of their values that prioritizing sustainability is a necessity to be able to stayrelevant in a quickly advancing industry, and that a business should reflect on what responsibilityit has in the sustainability perspective.

It is challenging to pinpoint specific actions for a digital design agency that could improve sus-tainability related to their operations. This is something the firm are currently working on, bothregarding their own footprint as well as in working with customers. Part of the ambition with thisresearch is to contribute in the matter.

2.1.2 Investigating new technologies

To be active in the most recent industry developments, Daresay frequently consider new opportuni-ties of design strategies and technology that appear on the community radar. Another part of the

11

2.1. About Daresay 12

company’s interest with the thesis is therefore to investigate the potential of rising technologies anddesign trends. The thesis aims to consider the forefront of user experience design methodologiesand what modern interaction patterns that could be relevant to the research context. An up andcoming interaction pattern much related to behavioral influence is conversational interfaces, whichalso is a central part of the thesis.

Chapter 3

Theoretical framework

This chapter contains the relevant findings from the literature study that aims to bring clarityregarding design for sustainability, big data, influencing behavior, conversational interfaces andspecific sustainability goals. How it all fits together will hopefully seem intuitive, but this will beexplained in detail in the following chapters.

3.1 Designing for sustainability

This section begins with a brief definition of sustainable development. It then covers earlier ideasand research about how to design for sustainability and sustainable behavior. Different perspectivesand views are brought up to give an adequate overview of previous work in the field.

What is sustainability?

Sustainability is a notion that concerns a situation where tenable conditions of life is maintainedat a certain rate or level. The sustainability of our global society is often said to be dependanton three pillars, which are environmental, social and economic sustainability. These pillars addressdifferent parts of sustainability, equally important and braided with each other in many differentways [12, 15].

What is sustainable development?

Sustainable development is simply put how the development of society should follow the drivers ofsustainability to strive towards such a future. A better definition comes from the United Nation’sBrundtland commission [12]:

”Sustainable development is development that meets the needs of the present without compromisingthe ability of future generations to meet their own needs. It contains within it two key concepts:

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3.1. Designing for sustainability 14

the concept of needs, in particular the essential needs of the world’s poor, to which overridingpriority should be given; and the idea of limitations imposed by the state of technology and socialorganization on the environment’s ability to meet present and future needs.”

Sustainability as an aspect of design

In his book ”Design is the problem”, Nathan Shedroff claims that sustainability itself can be seen asan aspect to design and development that summarily focuses on the three factors of sustainability,mentioned above. He claims that as a designer, it is important to understand the breadth ofsustainability and to use strategies for developing sustainable solutions [47].

Shedroff provides a large collection of strategies and principles for how designers can relate tosustainability. These strategies cover a tremendously wide area that are derived from an extensiveselection of earlier sustainable design frameworks. The author’s own resulting framework in thebook is an effort to address the common denominators of many previously distinguished sustainabledesign frameworks like Natural Capitalism, Cradle to Cradle, Biomimicry, Life Cycle Analysis,Social Return on Investment, et.c.

An important part of designing for sustainability is according to both the Brundtland commissionand Shedroff to apply a systems-thinking approach. This means to view the world as a systemand to understand how everything is connected. It concerns the system called Earth, but also thehuman system and every aspect of our global society [12, 47].

3.1.1 Human behavior and sustainability

It is no mystery that human behavior is part of what has caused an untenable situation in theworld. It is also no mystery that most concurrent human activity in the world is not parallel withsustainable ambitions. Fischer et. al. argues that the biggest obstacle to sustainable behaviorno longer is lack of knowledge about the problems, but rather to act on existing knowledge towork towards a sustainable future. In the article ”Human behavior and sustainability”, Fischer et.al. founded a categorization of human behavior, see figure 3.1. In this model, there are types ofbehavior and societal change that is quick and easy to implement at the top and types that areprofound and difficult to implement at the bottom [19].


Figure 3.1: A pyramid of behavioral categories that illustrates the difficulty and profoundness ofchanging those behaviors [19].

3.1.2 Designing for sustainable behavior

The idea of using design to influence people into more sustainable behavior is not new. It is in thispoint of view commonly argued that the role of the designer should be to envision how a productcan be used and to apply the sustainability perspective by customizing it to encourage widespreadsustainable behavior. In other words to design with specific intent [48, 34].

Blevis argues that sustainability can and definitely should be a central focus of interaction design.He proceeds to claim that in a sustainability perspective, the definition of design is to choosebetween, or informing about future ways of being [4].

Stegall’s definition of ”Intentional design” for sustainability seems to reach beyond the meaningof persuasive design. In this interpretation, it is about considering the social and cultural impactof inventions to its fullest extent. Many products can for example in designer and consumer eyesseem to have a sustainable impact, but which from an overall perspective in fact contributes toan increased negative footprint. Thus, it has negative influence on peoples behavior. The authorhowever agrees in the claim that designers in the modern world unintentionally focus mostly on thephysical issues surrounding a new product or technology, when in fact they should be consideringhow human behavior is affected [48].

”No designer ever intentionally suggested that people should value sloth over their ownhealth, that economic gain outweighs environmental destruction, or that convenience ismore important than competence, but today we can look back on a sea of products andservices that encourage these beliefs.” [48]

The author’s quote captures what the problem is about and highlights the significance of designinfluence. Design is an art of using elements of communication to induce people into beliefs andactions. This influential power is a responsibility that if neglected or forgotten, can lead to unin-tentional consequences on how people live their lifes [48].


In summary, the addressed research of sustainable design in this section approaches the sustain-ability problems as in integrating factors of sustainability into technology production. This so thatthe technology becomes more sustainable by influencing sustainable user behavior. However, Itcan also be argued that having a sustainability perspective, as in putting sustainability at the coreof designing technologies, is a narrowed focus of conducting value-centered design. Pereira et. al.states that sustainability is based on values, and designing for sustainability should rather be tocreate technology so that the behavior that the design promotes reflects the values of the peopleit is intended for, not the values of their designers. User values should therefore be at the core oftechnology production and not a direct sustainability perspective [40].

Earlier examples of designing for sustainable behavior

Explicit case studies of how to design for sustainable behavior has also been done before. DebraLilley concluded from her research that designers and engineers can positively influence productuse if decisions are made at a strategic level prior to design and development. However, she alsosaw a significant limitation in that a business must have a compelling reason to implement suchstrategies. Her research showed that sustainable strategies would most likely not be introducedunless it would also give a clear competitive or marketable advantage [32].

Like Shedroff, Lilley assembled a collection of strategic principles in her case study of how to designfor sustainability, although more specifically for affecting behavior [47, 32]. She came to anotherconclusion, that the level of intervention in user behavior and the measurements of consequences inintervening is very context-dependant. She claims that each case has to be individually customizedaccording to the user’s level of compliance, the gravity of the consequences of actions taken and thecontext in which the interaction takes place [32].

Building on these previous insights, Lilley et. al. later conducted additional investigation with twoproduct case studies. The conclusions here further expands on the importance of designer awareness[3].

Another case study emphasises the importance of user-centred design when aiming at influencingsustainable behavior. It compared three specific frameworks of designing for sustainable behaviorand found one of them more applicable for the product in the case study. An important conclusionhighlights how the effectiveness of these different frameworks will differ from product to productand consequently vary between different business scenarios [58].

3.2. Big data and data analysis 17

3.2 Big data and data analysis

This section explains the notion of big data, why it is important to consider and how industriesactually work with data within a business. It is also explained what data analysis could potentiallycontribute to sustainability and how this has been done before.

3.2.1 What is Big data?

A lot of companies create and rely more and more on data of their customers and their ownoperations. Social networks and other platforms accumulates and distributes more information.More types of devices get connected to the internet and more sensory data is captured all aroundus, referring to the related term Internet of Things (IoT). Information is then duplicated andmultiplied. This all adds up to the fact that there are huge amounts of information today and thatthere will be even more to handle in the future [37, 18].

Cisco has been tracking internet traffic over time and created a forecast of how the continuousincrease could develop, see figure 3.2. The report also anticipates that the number of devicesconnected to IP networks will be more than three times the global population by 2020 [27].

Figure 3.2: The increase of data as internet traffic over time in trillion bytes per month and thefollowing expected trend [27].

This situation can be addressed as big data. It is a generic term that is defined as when there aregreat volumes of data, data is moving fast and that data has high variety in type and structure.It is a phenomenon referring to how everything becomes different from before humanity started toexperience information overload. A big data situation creates clusters of data too complex to handlewith previous methods, like manual database management tools or traditional data processingapplications [37].

Organizations around the world, mostly from the private sector, has found that efficiently extractingrelevant information from big data sources can introduce new value and competitive advantage. Itwill further on also become more of a necessity to utilize data in all domains, since exploitingdata can give the upper hand, regardless of what logic or purpose it is used for. This course ofdevelopment is predicted to continue and as well as there are problems surrounding big data, it


also offers a lot of opportunity. Hence, it gets all the more important to find strategies in how tohandle data in business operations [37].

3.2.2 Problems and opportunities with big data

The footprint of data and technology

There is no doubt that using data as a part of technology can enable sustainable growth [52, 16].The organization called Global e-Sustainable Initiative (GeSI) conducted a study regarding howmuch that could be gained from digitizing operations across all business sectors with informationand communication technology. The study concludes that it would be possible to save 12 gigatonsof emissions in carbon dioxide by 2030, which is 11 times the amount of emissions saved by the EUin the last 25 years. They calculated that this would consequently also mean cost savings and newrevenues of 11.4 trillion US dollars, matching China’s total GDP at the year of 2015 [16]. Frickhowever, claims that this conclusion is incomplete due to how much consumption of non-renewableenergy resources this digitization would implicate [22].

The exponential increase of storing and passing data in our connected world is an environmentalsustainability problem in itself. It has a direct and significant impact on the environment as inrequiring energy resources for building and maintaining data centers and moving data around theirentire life-cycles. Most connected devices are driven by electricity, and they all request and senddata over the internet which in turn requires more electricity. A growing demand for energy isin turn partly due to the rapid increase in demand of data storage and network communicationcapacity [5]. The total footprint in energy demand of cloud computing was in 2011 estimated toabout 522 billion kWh, which equals the demand of the entire country of Germany during the sameyear. In his book ”Designing for sustainability”, Frick points out that since a large percentage of theenergy assets powering the worlds technology are not from renewable sources, this has a significantimpact on the environment [22].

There are contradictions to different perspectives of sustainability, but to stop collecting informationwould arguably be an irrational step backwards and probably even infeasible to carry out. Storingand transmitting data around is a big part of the development in science, innovation and technologyand that is as determined by the United Nations a vital key to all of the goals for sustainabledevelopment (this will be further expanded upon in section 3.5.4) [52]. A viable priority for softwarecompanies is however to build fast, long-term energy-efficient applications on both the back-end andfront-end and prioritize sources of renewable energy in all business operations, such as data storage[22]. Besides decreasing the environmental footprint, Shedroff continues that designing and buildingtechnology for efficient use of energy and materials will always make economic sense[47].

Transforming the world’s energy supply to renewable sources is in these perspectives infinitelyimportant, but conceivably a completely different part of promoting sustainable development thansustainable design.

There is also of course social and ethical issues of using big data strategies. It can for example beargued if it is acceptable to use and analyze data of people not knowing they are part of it [18].It is also especially difficult to measure the social impact of digital products and life-cycles of data[47].


Data-driven decisions

Data analysis is about being able to collect and analyze information, and to act based on thatinformation. The better the analysis and the faster the decisions can be made, the more valuethere is. We use the same looping process in everything we cognitively do as humans. Empiricalinformation is perceptively analyzed in our mind and we respond with reactions to it. Along withthe digitization of society there is constantly more abundant information around for people toconsume. A lot of our attention has moved from atoms to bits. This means that the habitualprocess around the loop occurs in higher speed than ever and it continues to speed up. We cannotkeep up with the accelerating flow of data and we require improved methods of how to orientour decision-making, whether it concerns planning a dinner or resolving global conflicts. Makingdecisions based on trustworthy, recent and relevant information concurrently opens up plenty ofopportunities in making the right decisions, which will be further explained later in the paper[37, 26].

Adopting big data strategies is perhaps not always a better approach than how data conventionallyis managed, and in a general sense, it can be argued unnecessary to separate the value of big dataanalysis from regular data analysis. It depends on each case whether it is appropriate to apply a bigdata strategy or if conventional methods are sufficient, like if the data is noisy or unrepresentative.The value of analysis in itself remains the same, although in different degrees [18].

3.2.3 The data supply chain

Algorithms of a company’s data analysis is much like the loop discussed in section 3.2.2, with variedobjectives. In the book ”Planning for Big Data”, Croll states that the process of managing bigdata within a business could be described in terms of a supply chain as shown in figure 3.3 below[37].

Figure 3.3: How data flows through the cycle of collection, analysis, actions and feedback withina business [37].

As technology can significantly increase performance of data analysis, it is still important to under-


stand that the way we address the analytic process of data described in the supply chain of figure3.3 is that it is done by both humans and machines. It concerns all aspects of handling data andmaking decisions within business operations. Sometimes, the loop occurs in high speed and in highrates without direct human intervention, and at other times or in parallel, it is about people makingdecisions based on data regarding for example user experience, conversion, marketing or softwareoptimization.

Figure 3.3 describes the different steps of when data travels around the cycle of a company’s businesslogic. First is the data collection. Information comes from a wide variety of sources and can beboth private or public. In this phase, the challenge is mainly to handle the volume and speed ofthe data. It can however also be a legislation issue. The task of collection is heavily dependant onregulatory circumstances, if data is open or closed. Next is the ingestion of the data. To structureand optimize the way the data is stored to be able to extract relevant fields.

Data that has been ingested can then be analyzed. This work needs to be distributed between bothpeople and machines, and shared between many nodes. It takes infrastructure to mine data, suchas utilizing virtualization, cloud computing and networks. It is is also achieved using platforms byeither breaking data into chunks and have a parallel process, or to optimize each step of a pipeliningprocess. Big data is often more about getting quick results rather than just data categorization,and this is because of two reasons:

1 Big data analysis is very much related to UIs. Delivering personal suggestions, adapted searchresults, et.c. requires a trip around the loop as fast as a page load. The way to accomplishthis is to distribute computational power and spread the task on multiple nodes.

2 Unstructured data is analyzed iteratively. When a dataset is first explored, it is unknown whatparameters that matters. It cannot be prepared for all future cases. If a user is requestinga specific dimension from a big dataset, like sorting by price, filter by type or somethingelse, the data has to be analyzed with respect to this. The analysis of the unstructured datatherefore has to be very fast.

Machine learning is a subset of Artificial Intelligence (AI) where statistical or logical models areused to train computers for a purpose, often with high volumes of data. It is a vital and ubiquitousutility in multiple steps of the data supply change. In the data analysis, it can give the ability toidentify patterns or key points from large and unstructured datasets that would not be possiblewithout this technology. Another example can be how machine learning and natural languageprocessing (NLP) can help at ingestion and analysis; parsing different types of data like raw textor sound data into more semantic and usable forms.

In spite of all the technology, there is still no substitute for human exploration as a part of thechain. Tools can help to find significant patterns, but it is then in many cases important to havehuman-like insight of this data to know what to do with it.

Big data requires lots of storage, in all steps of the data supply chain. Since the data needs tobe accessible fast from anywhere, it is divided between many nodes. This multiplies to even higherdemands of data storage.

The whole purpose of this loop around the data supply chain is being able to share and act basedon the analysis of the data. A business has a lot to gain from data insights, both from theirown traffic and from other relevant sources. It could be used in everything from human resources


decisions, to strategic planning, to market positioning, to product or service design. Unfortunately,sharing information is not always allowed or prioritized. As with data collection, it involves politics,legislation and willingness. Acting on it is nonetheless paramount.

The data supply chain describes how to act based on data, but it does not work without measuringresults and collecting feedback. Collecting and analyzing data will not do any good unlessintentions and strategies are planned and results are observed and interpreted, to then collect newdata or perform the analysis differently [37].

”It’s a process of continuous optimization that affects every facet of a business.” [37]

Summarily, Croll talks about the notion of a feedback economy. A global and social domain wherethe value is not about information on its own, but about how the iterative feedback of the loophelps improving every aspect of society.

3.2.4 Big data applications

There are many ways to apply big data. Some ways are to optimize operations or individualdecisions and some are to deliver insights of how to facilitate decision-making of others [18, 37].Some examples of how big data is applied to create value today are described below[18]:

• Business: customer personalization, search optimization.

• Technology: reducing process time from hours to seconds.

• Health: mining DNA of each person, to discover, monitor and improve health aspects.

• Urban development: Smart cities focused on sustainable economic development and highquality of life, with wise management of natural resources.

The last example is a complex problem that depends on many factors. According to a definition,building a smart city means to integrate technology with a clear strategy to improve sustainabledevelopment, citizen well-being and economic development [55]. To accomplish this development,it is required that decision-makers takes the initiative of investing. A smart city needs to bean ecosystem with a combined self-sustainable business model, dependant on a well establishedInternet of Things infrastructure (IoT) and big data strategy [55, 24]. There are today needs forthis development in social, industrial and political perspectives [24]. The reason why it is notprioritized could be that governmental funding is fragmented onto many different parts of citydevelopment and the sustainable perspective requires a more holistic view. For the same reason,policies that are required can in short-term be unfavorable to the economic growth. There is alsoin many cases limited capital for this sort of initiatives [55].

Big data today is driven by different objectives. The primary ambition can generally be focusedat either accountability or profit. The private sector is driven by the latter, while accountabilityas in research for public pragmatism is mainly conducted by public and non-profit organizations.There are however still many common denominators in organizational values between the publicand private sector, such as reliability, effectiveness and efficiency [54].


3.2.5 Linked data and the semantic web

The amount of data on the web is as mentioned growing very fast. Among the new methodologiesto deal with and exploit this growth is the concept of linking data. It is a simplified implementationof the semantic web vision, which in a sense refers to when all data on the web knows about otherdata. This means that each datapoint in a dataset has a reference to what that data is, in turnreferencing to that data. This is achieved either by providing all data with interlinked metadata,or by linking together a complete network of cross-referential data.

The potential with the concept of semantic web is that all data would be highly accessible. Collectionand analysis from extended sources of data could easier be accessed and used for whatever purpose.The problems surrounding it is with scalability and usability. How, for example, could the context-dependant quality and trustworthiness be decided from random data that is referenced in hundredsor thousands of joints?

In a big data situation, there is a clear need for better exchange and integration. Due not only tothe scale but also due to how data changes quickly. This has led to a frequent adaptation of linkeddata in academic as well as commercial domains [26].

3.2.6 Data-driven design

The term data-driven design refers to when design decisions are driven by data insights. It canas discussed in the previous section for example be when a big data strategy is applied with thepurpose of doing digital product design.

The authors of the book ”Designing with data” separates the term data-driven from their alternativeconcepts of data-informed and data-aware design, which are expressed to be not as direct wheninterpreting conclusions from the data. Rather, they point to higher abstraction levels of generalreflection on the data, which could be better suited for experimental user-centered design. In thefield of user experience (UX) regarding digital products, it is however a necessity today to use somelevel of data-informed strategies. Tracking user behavior and doing data analysis is also somethingthat has been done for a long time in this domain. Digital products enables the possibility to logand track user activity dynamically. Modern tech and design companies understands that dataanalysis is crucial for them to understand user needs and behavior [8].

Some claim that the problem is not anymore regarding access to data, but about the data qualityand methods of how to truly understand the meaning of a collected dataset. The ease of datacollection has made companies lazy in their analysis and risk to make the wrong conclusions. Agood data analysis is about acknowledging the right data, in the right way, from various perspectives[8].

If going by the definitions of data-driven, data-informed and data-aware design, choosing a moregeneral data-aware design strategy stresses the importance of measuring and interpreting bothqualitative and quantitative data. Without numbers, qualitative research might be misleadingand without qualitative research, it might be difficult what the measured numbers really mean[56, 8].

Tan et. al. believes that using a data-aware design strategy can help in aligning targets of user


experience with business goals. Being data-aware and having a user focus creates meaningfulbusiness goals. They also state that data-driven design is a great tool when trying to influencespecific behavior [8].

3.2.7 Artificial intelligence for decision-making

Resources of data is getting all the more relevant with the ongoing explosion of AI. Machine learningis as mentioned concurrently an important part of effective data analysis, but the role of AI canalso be discussed individually in the scope of the effect it has on society.

The authors of ”How AI Will Change the Way We Make Decisions” claims that a value of AIis to help the world in making good decisions. This is dependant on two inputs; prediction andjudgment. They refer to prediction not only as predicting the future, but as the general process oftaking abstract data to generate useful data. Judgment is the ability to decipher the benefits andcosts of different decisions in different situations. Prediction is something that machines do well.Judgment is on the other hand something that humans usually do better [1].

It is predicted that AI will replace parts of the human workforce in the near future, as thesemachines can do many work tasks cheaper and more efficiently. What types of jobs will be affectedthe most and what skills will be most in demand?

Agrawal et. al. thinks that tasks which require judgment will increase in demand as more machineswill take over more tasks that involve prediction. From an holistic standpoint, one can also arguethat even if AI could be trained to have better judgment as well, that training process would in turnhave to be based on human judgment. Thus, human judgment becomes all the more important andMachine intelligence will consequently improve our ability to make decisions [1].

3.2.8 Data analysis for sustainability

Data-driven innovation expert Alistair Croll claims that the incorporation of big data into oursociety enables opportunity for something far more important than business efficiency. The futureof big data and data analysis could mean a new era for humanity where the loop around thedata supply chain will become a normality for businesses and governments. Data is no longer anadvantage on its own. The key to progress will be about feedback. About how efficient the iterativeprocess around the chain is applied and how well feedback is put to use [37].

Technology expert Pete Markiewicz also claim that a sustainable future must be very analytics-driven. He states that all design will have analytics that estimates cost versus benefit in everycustomer and end user deliverance. Everyone will use analytics to drive their design, from earlystages of concept development to late-stage development issues [22].

There is already emphasis that big data can be used in favor of sustainability and there are severalways to apply a big data strategy in different contexts that can be linked to sustainable development[25, 18]. One clear example is the United Nations Global Pulse initiative, which aims to helpdecision-makers understand how a crisis affects vulnerable populations in developing countries, byusing real-time data analysis and data visualization. The effort strives to improve access to betterinformation faster, to help people be able to make decisions that strengthen resilience and protect


exposed groups of people. In this example, the purpose of the operation itself is a sustainableinitiative and it comes from the public sector. It is however a good example of how using datainsight and design can influence decision-making with great impact [42].

A very advantageous step forward of to using data for sustainability would be if the public andprivate sector could start working more together. This effect is thought to be especially significantif there is cooperation between organizations internationally [25]. But for businesses, whether thespecific application is to improve the user experience or optimize some algorithm, it comes downto generating financial profit and the willingness of public procurement overshadowed by otheralternatives [37].

3.3. Persuasive design 25

3.3 Persuasive design

This section goes through previous work on persuasive technology and design. It describes what itmeans and how it can be applied.

3.3.1 What is persuasive technology and design?

Persuasive design and technology is the design of products that are intended to result in certain userbehavior. It is design intended to persuade users into decisions that gives specific outcomes. Thefield is closely related to the concept of designing with sustainable intentions, as in accomplishingbehavioral change [34].

All design can in one way or another be argued to be persuasive. Designing technology directlyconcerns issues of rhetorics and persuasion. From that standpoint, Redstrom claims that persuasionalways should be a conscious perspective in design. Not necessarily to persuade, but to understandthe persuasive dimension when creating interaction between human and machine [43].

3.3.2 How to perform persuasive design

Social cues for influence

To accomplish a persuasive and motivating factor with a computational system, it helps if thesystem possesses a social presence in some sense. Fogg explored the role of computing products aspersuasive social actors and found five primary types of social cues or principles. Thus, cues thatgive computers a social impression, which then in turn gives them higher tendency to influencebehavior [20]:

1 Physical: Face, eyes, body, movement.

2 Psychological: User preferences, humor, personality, feelings and/or empathy.

3 Language: Interactive language use, spoken language, written language. recognition

4 Social dynamics: Turn taking, cooperation, praise for good work, answering questions,reciprocity.

5 Social roles: Doctor, teammate, opponent, teacher, pet, guide.

These five types of social cues can all in different ways create a social presence of computers and thispresence enables more opportunity to influence the user behavior. The impact of a system simplyhaving physical characteristics is according to Fogg’s experiments one way to construct a conveyingeffect. But he also suggests that a more attractive appearance will have greater persuasive powerthan an unattractive one.

A system can also acquire persuasive power on users by adopting psychological aspects like usinghumor, empathy and by expressing feelings. The more alike a computer system’s personality isbeing perceived in comparison to the user, the more influential it becomes.


Using language to persuade is an interactive aspect of persuasive design that can be used in justas many different ways as language itself can be used in conversation or text. It is a very commonway of marketing a brand, increasing conversion or encouraging certain user activity. Language isa clear way of influencing a target audience to a specific mindset or action.

Social dynamics is the fourth principle that points to cultural behavior and using things likepraise and cooperation to cause a persuasive effect. A powerful tool with this principle is thatit can attract user reciprocity, making them feel like they have some sort of obligation to thesystem.

The fifth and final principle of using a system as a social actor is for it to embrace a social role inrelation to the user. Depending on the context and use case, playing this role can make the userdraw parallels to other social roles of their common surroundings. This makes the user feel specialor important and is then more easily persuaded.

Fogg is suggesting that a digital system will be more influential if it mimics human attributes andadopting these traits in a system can thus accomplish an increased user willingness to follow designintents [20].

The Fogg Behavioral Model

Fogg also presents a model for understanding human behavior called Fogg Behavior Model (FBM).It states that a target behavior is a product of three factors [21]:

A person must:

1 Have the ability to perform the behavior.

2 Be motivated to perform the behavior.

3 Be triggered to perform the behavior.

Each of these factors then consist of sub-components. The model can be useful as a systematicapproach in the design of persuasive technologies. The model can be visualized as follows in figure3.4.


Figure 3.4: A visualization of the Fogg Behavior Model [21].

As motivation and ability each represent an axis in the graph, these two factors can be a trade-offin reaching a certain target behavior. One of the two must however most likely be non-zero if thebehavior is to happen. Higher levels on their respective axis increases the likeliness of a person toperform the intended behavior. The ability to do something can be facilitated by making thingseasy, to optimize an interface for simplicity [21]. Designing for simplicity is very much related toguidelines of usability and how to do user experience design.

The motivation of doing something can be built up by using so called elements of motivation, suchas pleasure, hope and acceptance[21]. Designing for motivation is also a wide area of research thatcan follow many different frameworks alone. Examples are to use aspects of intrinsic motivationlike giving a clear purpose, or aspects of extrinsic motivation like gamification. There are alsoframeworks of how to induce addictive behavior, like the Hook model.

So as the likeliness of performing a behavior or making a decision is built up by ability and motiva-tion, a crucial part is also that the right trigger is used to execute on that likeliness. A trigger cantake many forms in both text, sound or something else and it is important to have the right timing.It can be in the purpose of a final push in motivation, an effective facilitator to raise ability or asa simple reminder. In design terminology, common triggers could be to have a clear call-to-actionor using push notifications [21].

This model can be used as guidelines, or to identify what could be a lacking aspect in a design withintent for a specific behavior. The target behavior could be something as simple as to sign up to anewsletter, or it could concern a bigger decision like purchasing a house. These intended behaviorsrely on the same factors but probably require different levels of motivation and ability as well asthe right triggers [21].

3.4. Conversational User Interfaces 28

3.4 Conversational User Interfaces

This section explains what conversational user interfaces are and what they are good for. Fur-thermore, it is elaborated on how chatbots fit into the picture and why messaging is so popular.An overview is then given on the current ecosystem of relevant tools and platforms for buildingconversational interfaces.

3.4.1 What are conversational user interfaces?

A conversational user interface (CUI) or conversational bot is a type of UI that imitates a normalhuman conversation. It is software driven by AI that can parse natural language to structureddata, more specifically called natural language processing (NLP). It can recognize user intentsfrom dynamic queries in text or speech and churn out useful responses. In this way, computerscan interact with humans on human terms. It has become a popular and promising interactionparadigm along with the rise of NLP and cross-platform services [30].

These interfaces represent a dynamic pattern of providing a service, but they could also possess thepotential of automating dubious tasks that humans previously were required to do. In some usecases, it can be important to keep a human in the process and a bot could then instead act as afilter or assistant in augmenting the human capability. It is referred to as ”human in the loop” andthis approach is meant to enforce human productivity rather than to be a replacement [30]. Here,we are not referring to the same loop as in section 3.2.2.

CUIs could have a large potential to influence user behavior, since it meets many of the socialaspects of a human counterpart. Already in the 1960’s, one of the first CUIs called ELIZA wasshowed to possess an influential impact on human decision-making [57].

3.4.2 Chatbots

A chatbot is a name for a type of CUI that is text-based and which resembles a typical messagingservice. Interaction is done through typing but could potentially also be used by speech. There areseveral reasons why this concept is deemed promising that will be discussed in this section. First ofall, it introduces a highly accessible way to provide a service. Instead of users having to downloadand install a new application, a new contact can be added in a preferred platform much like if itwas a person dedicated to a purpose, like an assistant. Or even more accessible, if the interface wasintegrated within an ubiquitous environment, such as a smart home. The interaction can be donethrough a variety of devices and users can be reached anywhere [30].

The concept of chatbots is becoming all the more interesting for businesses today since the mobileapp economy is stagnating. Getting through to users with a new mobile app is increasingly difficultdue to competition and changed user habits [30]. Moreover, the most used category of mobile appstoday is messaging, measured in monthly users. It has previously been equal with social networkingand entertainment but is now according to statistics the number one category globally [13, 11, 28].In many countries, the usage of messaging apps is nevertheless still increasing [31]. In China, themost popular messaging service WeChat has over 700 million monthly users. This app has becomemore than a messaging platform. It is used for all types of purposes, like e-commerce, entertainment,


browsing, voice calls and more. It has grown into more of a mobile operating system rather thanjust an application. There are speculations that the development in messaging platforms in the restof the world is going to follow in these footsteps [17].

A chatbot service could be applied on basically any problem where software is applicable, but thereare situations that can be argued better and worse suited to chatbots. Valid use cases could beto augment human workers for productivity, or to simply provide a service for end users. Twoexamples of areas that these services might be especially applicable is customer management andmarketing.

Chatbots should not be used to simply replace mobile apps or web-based apps. It is not in allscenarios a better tool and should instead be benefited differently. It should address real issues in adifferent way [51, 35, 30]. According to experts, some situations when chatbots could be applicableare the following [51]:

• A bot can help the user define specific problems and provide solutions that are not obvious.The user might not know what they want and unlike a graphical UI, a chatbot is more dynamicin adapting to user needs.

• A bot can deliver different value that would not be possible with a regular mobile app.

• A bot can take part in a group conversation and add value to more than one person simulta-neously.

Many chatbots on the market at this time gives a questionable user experience. They either do notfollow any guidelines or are frankly not intelligent enough to benefit the conversation [30]. Anotherimportant factor when creating a chatbot is that brand matters. A chatbot personality, whether itportrays a company, person or neither, needs to have a declared consistency when interacting in aconversation [35].

3.4.3 Why do people like messaging?

Text messaging has been around for a long time and mobile text messaging has been popular eversince SMS. However, just like language, our ways of expressing ourselves are evolving. The reasonfor how messaging apps has grown into becoming the most used is simply because people like it.But why do people like it?

Non-confirmed speculations around this question ends in six compelling arguments that could havesome substance to why communication by messaging is so popular. It can also give insight to howchatbots should be designed [14]:

1 Asynchronous: Text messaging enables synchronous direct messaging, but it also allowspeople to communicate without being available at the same time.

2 History: A conversation in text messaging gives a timeline of history, with each interactionin the conversation in chronological order, so even when the conversation is asynchronous itis easy to get a quick recap or scroll back to remember something.

3 Easy consumable: Text messages are often short and concise and can be quickly consumedfrom a lock screen or with a glance.


4 Informal: It is less formal than for example email and does not require proper salutationsor language.

5 Portable: Messaging platforms are accessible from smartphones and many more devices andthis makes it easy to access a conversation from anywhere at anytime.

6 Expressive: Text messaging has become a language of its own, with emojis, links, gifs,hashtags, visual cues of read messages, et.c.

3.4.4 CUI Implementation

Today, there are many tools and platforms for building conversational interfaces that serve differentneeds. There are text-based CUIs and speech-based CUIs (voice user interfaces), both sharing manycommon attributes.

When it comes to text-based CUIs, chatbots has become the common ground. In summary, it couldbe stated that there are two kinds of implementations of chatbots; there are rule-based chatbotsfunctioning as a decision-tree, and the intelligent chatbots that use natural language processing(NLP) [46]. The first mentioned gives suggested replies for every interaction and has a hard timeunderstanding free language inputs. According to Mauro, it is crucial to use NLP to be able tosupply a useful conversation with a chatbot. The reason for this is that people like to type messages,as briefed in section 3.4.3. People seem to perceive time differently when typing and seem to preferit instead of pressing generated responses, even if it means slower interaction. Therefore, Maurothinks the goal should be to build intelligent chatbots, not the rule-based kind that in a sense isnothing more than a graphical user interface (GUI) that looks like a chat [46, 35].

There are many tools for creating, deploying and consuming conversational interfaces. As thelandscape is still shaping, the following image shows some of the most eminent software that isavailable:


Figure 3.5: An overview of the current bot market with the largest players and their inherence[36].

The upper half of the landscape in figure 3.5 regards conversational bot platforms of messaging orvoice interaction, while the lower half are tools to create and distribute conversational bots.

3.5. Goals of sustainable development 32

3.5 Goals of sustainable development

This section covers materials in line with the purpose of the research in a sustainability perspective.It anchors the thesis case study to official policy goals for sustainable development. An analysiswas made regarding the surrounding circumstances of local and global urban development, and alsoaspects of technological development. This branch of the theoretical framework lays the foundationof why the experiment of influencing corporate settlements is meaningful.

3.5.1 United Nations Global Goals

At September 2015, the United Nations (UN) together with 193 countries declared 17 global goalsfor sustainable development1. The goals address different aspects of sustainability that aims to bereached by 2030. Each goal has associated targets.

This research aims to contribute alongside the ambitions of these goals and the thesis mainlyconcerns goal number 8, 11 and 17 :

Goal 8: ”Promote sustained, inclusive and sustainable economic growth, full and productive em-ployment and decent work for all”.

This goal addresses a sensible economic growth and employment for all. Subsequently, itmeans to eliminate factors that has a constricting impact on this prosperity. Below are theofficial targets (sub-goals) of Goal 8 that has been especially considered in finding the rationalein terms of sustainability for this research:

• ”Sustain per capita economic growth in accordance with national circumstances and, inparticular, at least 7 per cent gross domestic product growth per annum in the leastdeveloped countries.”

• ”Achieve higher levels of economic productivity through diversification, technological up-grading and innovation, including through a focus on high-value added and labour-intensivesectors.”

• ”Promote development-oriented policies that support productive activities, decent job cre-ation, entrepreneurship, creativity and innovation, and encourage the formalization andgrowth of micro-, small- and medium-sized enterprises, including through access to fi-nancial services”.

• ”By 2030, devise and implement policies to promote sustainable tourism that creates jobsand promotes local culture and products.”.

Goal 11: ”Make cities and human settlements inclusive, safe, resilient and sustainable”.

This goal is about making all people have good and sustainable standards in housing andinfrastructure. It addresses how to handle urbanization and city planning. This goal alsocontains several more specific targets, where those that are most relevant to the experimentare listed below:

1http://www.globalgoals.org/#the-goals A list of the UN 17 global goals for sustainable development withaccording explanations, accessed 2017-03-17


• ”By 2030, enhance inclusive and sustainable urbanization and capacity for participatory,integrated and sustainable human settlement planning and management in all countries”

• ”Support positive economic, social and environmental links between urban, peri-urbanand rural areas by strengthening national and regional development planning”.

Goal 17: ”Strengthen the means of implementation and revitalize the global partnership for sus-tainable development”.

This goal is about partnerships and embeds the importance of technological development andinnovation. The following targets from the goal specification is addressed:

• ”Enhance North-South, South-South and triangular regional and international coopera-tion on and access to science, technology and innovation and enhance knowledge shar-ing on mutually agreed terms, including through improved coordination among existingmechanisms, in particular at the United Nations level, and through a global technologyfacilitation mechanism”

• ”Fully operationalize the technology bank and science, technology and innovation capacity-building mechanism for least developed countries by 2017 and enhance the use of enablingtechnology, in particular information and communications technology”

3.5.2 Urbanization in general

Urbanization is a natural process in the course of a society’s development. More people want accessto improved standards of living that are available in cities. Hence, people move from rural areas towhere there is opportunity of higher quality of life [41].

Urbanization often has positive effects on society, if accompanied with high productivity. Studiesshow that urbanization has a strong correlation to an increase in technological development andeconomic growth [53, p.18-29]. However, when the process intensifies and a lack of economicgrowth is apparent, too many people end up sharing a limited space. This is called overcrowding.Overcrowding then often lead to negative effects like unemployment, housing shortage, pollution,poverty, crime and health risks [41, 53, 38].

Even if a healthy urbanization rate is sustained and a prosperous economic development is main-tained, there is still a challenge with reaching equity in distribution of this growth. In the case ofunequal distribution, it will induce a marginalization between rich and poor which means a down-turn of growth in local and national communities. Also, it contributes in creating social segregationin society. These effects can exclude the very advantages that people seek when moving to citiesand it can slow down the overall development [53, p.52-81].

Urbanization does historically mean a large negative environmental impact, but cities also holds alot of opportunity to create a sustainable system. There is a need for smart ways to meet humanneeds with small footprints and increased quality of life. This also requires investments and moreaggressive policies. With cities works towards this, sustainable city development in line with apromising rate of urbanization can give good results [24].


3.5.3 Urbanization in Sweden

In Sweden today, people living in urban areas constitutes about 85% of the total population.Urbanization has been a palpable process in Sweden during the past 200 years [9]. However, duringthe last thirty years or so, the rate of urbanization has been declining. Today, rural areas areno longer getting depopulated at a considerable scale. The big cities are though still growingconsiderably in population, but this is more due to immigration and an increased number of births[50].

Much of Sweden’s economic growth comes from resources outside urban areas. Some examples ofthese assets are forestry, mining and hydroelectric energy [10]. Cultural heritage and a marketfor tourism are other factors in the matter [33]. It is therefore important to maintain a healthydevelopment of these regions, but the way countryside is addressed in national policy as a normalityis neglecting this reality, which inflicts more problem to the situation [45].

A city is dependant on a frequent rate of import and export in material and energy [10, 24]. Thisinvolves an exchange between rural and urban areas as well as international trade. Minimizingdistance with local supply and decreasing geographical dependency is a way to optimize flows ofexchange. Hence, a procedure of localized urban development can bring several ecological, economicand social benefits. It is a part of planning sustainable urban development as well as improvinglocal and global relations [24].

There is a lot of factors playing a part in creating a sustainable system. There are different social,economic and ecological implications to all aspects of sustainable city development. However,the optimal vision of an efficient and sustainable situation in Sweden seems to mean a clusterof sustainable urban areas, evenly distributed between all regions. A cluster where each nodecontribute to the regional and national development and where resources are shared equally withinand between regions [24, 53].

3.5.4 Technological development and innovation

The private sector has a big responsibility in terms of sustainability, due to the scale of theircollective footprint and investment capabilities [39]. Since companies forge innovation and growth,they also hold part of the vital key to sustainable development [52]. According to the formerSecretary-General of the United Nations Ban Ki-moon, the development in science, innovation andtechnology is the number one most important aspect of sustainable development. This aspect isalso stated as part of goal 8 and 17 above (3.5.1). Technological development does however carrythe potential to accomplish significant progress in regards to all global goals. The greatest challengeis to link progress of science, innovation and technology with initiatives of sustainable development,to align their combined purpose with the global goals [52].

3.5.5 The role of sustainable cities

Urbanization and city development is complex and the circumstances is not simply black and white.Rapid urbanization increases the environmental impact, as cities account for around 75% of theworlds energy use and 70% of carbon dioxide emissions [24]. However, cities also holds the potential


to create smart and sustainable solutions to minimize footprints while still increasing quality of life.Urban areas can also speed up the technological development and economic growth [24]. The WorldWildlife Fund (WWF) believes that the role of cities in sustainable development must be informedby [24]:

• A global perspective, especially in terms of cities’ role in tackling global challenges such asclimate change and poverty.

• A systems perspective that links urban with rural areas, and local with international systemsof production and consumption.

• Balancing and integrating social, cultural, economic and ecological perspectives.

Summarily, initiatives and drivers for sustainable city development needs to acknowledge bothrural and urban areas, while simultaneously counteracting local, national and global issues. Allperspectives of sustainability must be accounted for in this development [24].

Chapter 4

Methods

This chapter describes the phases of the project and what methodologies the work included. Thetheme for this thesis was design for sustainable development. An early analysis of the problem spacebrought the project towards how to influence behavior with data and through design. An hypothesiswas that conversational interfaces could potentially be well adapted to influencing behavior. Asthis approach needed to be investigated, the specification of the project led to the research beingstructured into three phases:

1 Interpretation: Extensive literature studies was issued in related fields of sustainability,sustainable design, data science, persuasive design and conversational interfaces. This toidentify a solid theoretical framework, so that we could determine and validate a conclusiveapproach of designing for sustainable behavior. This part also involved early user research,conducting interviews with target users and field experts, relevant to the case study.

2 Implementation: Building the prototype for the case study, using insights from the theo-retical framework and user research to create a sufficient proof-of-concept.

3 Evaluation: Testing the prototype on the target group and analyzing the outcome from theexperiment. Finally uncovering a conclusion and discussing achievements as well as futurework.

4.1 Interpretation

4.1.1 Literature studies

An holistic approach to the problem was needed to find a way to perform a reality-based experiment.An analysis of a wide range of topics was completed to gain better understanding of each separatearea as well as how they are connected. It was decisive in determining the important problems athand and how to address them.

36

4.1. Interpretation 37

Cited work was found by browsing university databases of academic research and other sources onthe web. Because of the quick development in modern fields like machine learning and conversationalinterface design, as relevant to this thesis, there was not much academic work yet done regardingthe most recent circumstances. In reason of this, some sources of information regarding these topicshad to be found elsewhere. The status of unconfirmed ideas was taken into account.

The literature study started with reading up on the UN’s global goals and deciding what aspect ofsustainability to address. The aim was to find a way to conduct a unique and relevant case study.Problems regarding urbanization led us into reflecting on the potential of influencing corporatesettlement. Then it was about observing the state of the situation in Sweden, in reference to theresearch being performed in Sweden with limited resources. The target group of entrepreneurs intechnology or design was from the concurrent research deemed appropriate and this sustainabilityperspective became the scope for the thesis case study.

An investigation regarding earlier work on designing for sustainability and persuasive design prin-ciples was taken into account when identifying the problems in depth. Data analysis as a tool forsustainable development became a central aspect as the literature research progressed.

The literature study gave an extended view of the interconnected problems. It resulted in a collectivemodel of how to design for sustainable behavior that was later tested in the case study. Theobjectives and main thesis research question were at this point formed.

4.1.2 User research

The purpose of the early user research was to find out what kind of data that was relevant to use forthe experiment and to understand more of the explicit use case. Therefore, it was needed to find outwhat requirements companies have when searching for a region and working environment.

Six introductory depth interviews with entrepreneurs in English or Swedish was conducted early inthe project. The interviews were open discussions with a manuscript of questions that served astalking points. The complete manuscript can be seen in appendix A.

A municipal representative was also interviewed regarding the regional ambitions versus the nationalambitions in economic development and growth. This was to better understand the drivers ofeconomic distribution.

The results from the user research are disclosed in chapter 5.

4.2. Prototype design and implementation 38

4.2 Prototype design and implementation

This section showcases the prototype for the thesis case study and the general methods of creatingit. But, more details and the reasons behind the bot design will be covered in the design rationale,chapter 6.

The case study of this research was a test of implementing a data-driven approach with a sustainableperspective on an example business, by influencing user behavior. This example business helpscompanies find personalized office space through a digital service.

The prototype used in the case study was a real-time communication system with a conversationaluser interface (CUI) built for the business-oriented messaging platform Slack3. The CUI in thesystem was thus a text-based chatbot, a technique explained in section 3.4.2.

The users could in a direct message conversation talk with the bot about their company and personalneeds. The system finds adapted suggestions for them to start office, on which they could chooseto comment or continue searching until they find something they liked. The office suggestions inthe prototype was of example character and not dynamically retrieved. This means that therewas no extensive data analysis to personalize results, this functionality was instead resembled asa proof-of-concept. The interactions were supposed to mimic a personalized experience, where theexample suggestions were chosen with reference to the user research. The chatbot aims to givea good user experience and simultaneously try to influence users to contact offices in less urbanareas. The focus with the prototype was on seeing if the conversational interface was effective ininfluencing user decisions.

The design and implementation of the prototype was an iterative process, following the learningsfrom the theoretical framework and user research. It started with deciding what communicationand bot platforms to use and creating a basic layout of how the system architecture and flow ofinformation would work.

We analyzed the results from the user interviews and created a draft of the chatbot identity, what itwould behave like and its characteristics. During the development process, the design was iterativelyimproved. The system was tested by third parties through out the design and development processand perfected along the learnings from these tests. Precautions regarding system reliability andintegrity was carried out to make sure the system would be accessible at all times and that userinformation was secure. The chatbot came to be called Mio.

Figure 4.1: The chatbot avatar and Slack app description.

3https://slack.com/ Website of the messenger platform, accessed 2017-04-17


4.2.1 System architecture

The different tools mainly used for the implementation of the prototype was the machine learningplatform Api.ai4, the run-time environment Node.js5, the open source database PostgreSQL6 andthe cloud-application platform Heroku7. Git was used for version control8. The platforms and toolswere used out of appropriateness to the target group and scope of the project.

Figure 4.2: A simplified visualization of the system components and communication betweenthem.

The user communicates with the system through any device where the Slack messaging applicationis available. For every triggered user event, the Slack application programming interface (API)sends a request to the connected node.js cloud service. That could for example be when a new user

4https://api.ai/ Website of Api.ai, accessed 2017-06-125https://nodejs.org/ Website of Node.js, accessed 2017-06-126https://www.postgresql.org/ Website of PostgreSQL, accessed 2017-06-127https://heroku.com/ Website of Heroku, accessed 2017-06-128https://git-scm.com/ Website of Git, accessed 2017-06-12


has joined the Slack team or when a new user message is sent to Mio. User messages are passed onto intent classification, which means that a user intention is extracted from the message with helpfrom a trained AI. This intention could be a question regarding something, a statement, or basicallyanything that the user intends to express with a message. When the message intent is identified,an action matching that intent is issued. If this action is an independent response, a text messageis sent directly back from Api.ai through the web-service to Slack. If the intent is dependant on aspecific context or user profile, a fulfillment request is passed to a webhook server, which retrievesthe context-dependant information from the database and responds with a custom message. Theuser profile, current chat context and latest chat session is concurrently updated in the databasefor every request.

The two node.js web services in figure 4.2 is in fact the same web service. The different endpointson the server are visually separated in the diagram to better illustrate the difference in purpose forhandling event requests and fulfillment requests.

NLP architecture

The natural language processing (NLP) makes it possible to interpret user input and match userintents to the right responses. This part of the system was done with Api.ai, a platform to createand train custom conversational bots. It meant constructing and classifying context-specific userintents and mapping them to specific actions and responses. This was done by manually typing allkinds of user input that could match specific intents. It was also possible in the platform to trainMio by letting other people converse with it and then point out to Mio were it made mistakes. Moregeneral conversation intents was also imported from previously trained open-source bots.

In this way, several thousands of text queries were created to build Mio’s natural language under-standing. Mio was tested and trained throughout the whole implementation process and grew tobe more solid in understanding user inputs for each iteration. To put further constraints to theconversation, things like context and follow-up intents was used extensively. Follow-up intents areintents that can only be matched following a previous intent, like when user wants clarifications orasks follow-up questions. Contexts work in a similar way, but can apply over a number of intentsin a row and can be manipulated for each new message. Contexts helps Mio recognize what thecurrent conversation topic is about and how it changes for each user message.

User-specific contexts of current talking points were stored and dynamically updated as sessionsin the database. This enabled the users to pick up a conversation were they last ended a session.Mio could fetch the latest context and continue. The architecture for how the natural languageunderstanding hooks into the overall system is showed in figure 4.3.


Figure 4.3: The intent classification and response selection process.

As visualized in figure 4.3 above, when a user message is matched with an intent, something calledentities can simultaneously be extracted from the message. Entities are pieces of structured datathat the user supplies in a message. For example if a user message contains: ”Is there something inStockholm?”. Then the intent of searching an office is found, with an entity of location-specific data,as in ”Stockholm”. This entity recognition was part of the proof-of-concept implementation to provehow data extraction and personalization in a conversational interface can be done. Location-specificdata like this was as declared not part of any geographical data analysis.

Mio has about one hundred intents and can recognize about fifteen types of entities. The entitiesmostly represents characteristics and information about the user and their company.

4.2.2 Prototype design

Designing conversational technology can in a sense mean to design a personality rather than an inter-face, considering how almost all interaction between the user and system is through language.

The chatbot used in this study was called Mio, as a commercially applicable name for the examplebusiness. With insights from the theoretical framework and user research, we established four mainkeywords that would define the chatbot personality:

• Trustworthy

• Confident


• Collaborative

• Informal

The process of creating the chatbot personality was through visualizing and trying conversationmockups. The behavior should be consistent, context-adapted and as such building a good userexperience. This was done by mocking up an example conversation tree, for the most crucialinteraction flow in the conversation. As building the language understanding and broadening theconversation capacity, the user interaction with the bot would eventually become more dynamicwith the possibility for the user to jump in and out of this main conversation tree at any stage.

The chatbot avatar was designed with respect to the keywords above and the results from the targetgroup interviews. The user interface is graphically a typical message chat, available on differenttypes of devices, where the user can communicate with Mio.

Figure 4.4: The user interface is a simple text message conversation. Here shown on two differentdevices, Desktop and a smartphone.

User onboarding

The first time a user starts a conversation with Mio, they get greeted by a sort of introduction.Mio tells the purpose of itself and instructs about what topics it is mainly capable of taking partin. Mio also invites the user to start the conversation about their company profile.

The introduction is transparent with the fact that this is just a prototype and that the given resultsare of example character. It was needed to keep this fact open so that the test participants wereaware of all the circumstances.


Figure 4.5: Excerpts from the onboarding process. Example user interactions when starting aconversation for the first time. Reading from left to right

When the user is given an office suggestion for the first time, they are reminded again about howthe suggestions are not real adaptations to their company profile. The intended purpose of theprototype is instead to evaluate aspects of the interface and conversation. Tips are also given forwhat the user can comment on or ask about.

Main conversation possibilities

Continuing on the conversation from figure 4.5, the user can proceed in any way they want. Theyare however encouraged to stay within the relevant context. Thus, they get prompted to tell Miowhat they think about the current suggestion, more information or how they would like it to bedifferent. A typical way to proceed the conversation is showcased in the figures below, where screencaptures are taken from the same chat session.

Figure 4.6: Screen captures from user interactions with Mio, trying to find a suitable office.


As seen in the first screen caption of figure 4.6, the user gets momentary feedback that their sentmessage has been acknowledge and that Mio is currently typing a response. This happens for everyuser input where an imminent message awaits and Mio is in progress of interpreting the message.When the time to respond takes longer, for example when loading a new office suggestion, Mio letsthe user know that it is working on it by giving some brief feedback like ”Hang on, I’ll check...”,seen in the second picture of figure 4.6.

User messages in the second and third screenshots of figure 4.6 are examples of how the usercan interact on the current conversation context. These user messages with information aboutpreference can then continue to build on the user’s personalized company profile and Mio can thengive better results.

Figure 4.7: Screen captures from user interactions with Mio, trying to find a suitable office.

The user can continue to look for alternatives and step by step relatively improve Mio’s under-standing of the company profile and user needs. When users seem to appreciate a proposal, Miotriggers them into getting in touch with that current office provider, as seen in the second captureof figure 4.7. This is an example of how a user is influenced to take action.

When a user comes back at another occasion, the user can simply pick up where they left off andcontinue the conversation and the previous chat session is then restored from the database. If aknown user comes back and greets Mio, it responds with a message relevant to the latest discussedoffice context, see last screen in figure 4.7

Interactive buttons

For each office suggestion that Mio supplies to the user, there are three buttons, as can be seen forexample in figure 4.7. These buttons supply an alternative way of interacting with the interface.The first is a trigger for the user to contact the office in question, where as the second gives moreinformation and the third is a quick way to search for a new option. The buttons are not crucial


to the conversation but can help in giving examples of concise actions that the user can make foreach new suggestion and drive the user to also commit to these actions. It simplifies the action ofgetting in touch with an office.

Smalltalk

It can be important to show some capacity of general conversation to gain users will of reciprocation.Mio is capable of interacting on a wide range of other topics. Examples of these are surroundingquestions about the research project, how the system works, the purpose of the bot or otherthings about Mio. Furthermore, Mio also has hundreds of other user intents that match differentresponses on topics like acquaintance, praise, state of mind, occupation and so forth, see figure4.8. The language understanding and intent recognition of these talking points was imported frompreviously trained open-source agents, where most corresponding responses was edited to go wellwith Mio’s personality. When the timing is appropriate, Mio tries to lead the user back to the mainconversation it was meant for.

Figure 4.8: The chatbot is capable of some general conversation with the user, beyond finding anoffice.

More details and specific reasons behind the overall bot design and implementation will as mentionedbe covered in the design rationale, chapter 6.

4.3. Evaluation 46

4.3 Evaluation

This section explains the materials used for an evaluation framework and how the evaluation wasexecuted. The framework for the evaluation was decided from how to measure the effectiveness ofpersuasive technologies and thus how to best try the research question:

How effective is a conversational user interface in influencing corporate settlement decisions?

4.3.1 Evaluation framework

There are a wide range of available research methods in the field of user experience. It is importantto choose one that fits the research purpose and capacity [44].

Figure 4.9: A chart of different user research methods and in what scenarios that they areapplicable [44].

Figure 4.9 above visualizes a landscape of user research methods and proposes a way of decidingwhat approach that would be appropriate for different scenarios. The dimensions of the graphdistinguishes the appropriateness of user research methods in different proportions [44]:

• Behavioral vs. attitudinal

• Qualitative vs. quantitative

• Context of use

4.3. Evaluation 47

The distinction between behavioral and attitudinal methods can be expressed as the differencein what people do and what people say, which can vary a great deal. Most usability studies shouldrely on behavior, but attitudinal methods can also be useful for designers when trying to discovernew issues or trying new concepts.

The horizontal axis of the graph signifies the distinction between qualitative and quantitativemethods, which refers to direct observations of behavior or attitudes as opposed to indirect. Rohrerdescribes the values that sets them apart as:

”Due to the nature of their differences, qualitative methods are much better suited for answeringquestions about why or how to fix a problem, whereas quantitative methods do a much better jobanswering how many and how much types of questions” [44].

Insights from quantitative methods are often derived from a mathematical analysis, whereas quali-tative methods are not.

The context of use refers to the situation of the study and if the participants are using the productin a natural, near-natural or scripted fashion. It could also be by not using the product at all orsomething in between.

Rohrer also goes on to explain some of the research methods seen in figure 4.9. Two of which thatare relevant to this study [44]:

• Concept testing: Testing an approximation of a product or service with a clear valueproposition of a new concept to see if it meets the needs of a target group.

• Customer feedback: Collecting information from a sample target group, for example witha survey.

Matching a method

The framework so far gave input on how and what type of research method that could be suitedto this research scenario. On one hand, the case study is very conceptual, which would suit amore attitudinal approach. On the other hand, the results are very dependant on user behavior,as the research is about influencing behavior. Other than analyzing the saved user conversationsand interpreting their behavior from the text, it was with this test difficult to measure the actualbehavior. Moreover, no test user would in fact commit to the final behavior since they were awareof the conceptual nature of the test. Mostly due to the conceptual nature of the prototype, andlack of possibility to measure the actual behavior, a more attitudinal approach was chosen.

Comparing the appropriateness in regards to the horizontal distinction of the chart, it seems likea more quantitative approach fits the research question best, as in being a question of type ”Howmuch”. An indirect evaluation method would thus give better results in regards to the researchquestion.

Rating scales and items

What remained at this point was to compose a method that meets the priorities above and decidehow to specifically test the prototype. Exclusively asking the test users how effective the prototype

4.3. Evaluation 48

was in influencing their decisions would inflict a lot of bias, so the method would preferably be relianton a collection of aspects that are linked with persuasiveness. No existing model of measuring theinfluence or persuasiveness of a system was found. But this evaluation could be done by examininghow well the prototype performed with regards to the persuasive principles in section 3.3.2.

A rating scale is a way for users to rate something within a range. A particular kind of ratingscale is called a Likert scale. Here users rate how much they agree to a number of statements,called items, by choosing one response category out of several, ordered in hierarchical order withlabels representing the level of hierarchy [23, 49]. Results from multiple statements then add upto a scale on which the overall performance can become graded [23]. Friedman et. al. identifiedseveral potential issues of creating a rating scale. To avoid bias and make sure that the resultsfrom the study can be reliable, we analyzed these problems and aspired to the following principles[23]:

1. Connotations of category labels: Keep balanced and equal-sized gradiations between thepoints on the scale.

2. Effect of response alternatives: Do not affect the interpretation of the question with theresponse alternatives

3. Implicit assumptions of the question: Avoid bias by not using words that assume adegree of agreement.

4. Forcing a choice: Keep the possibility of a neutral answer.

5. Unbalanced rating scales: An equal number of favorable an unfavorable response choices.

6. Order effects in rating scales: Avoid bias of ordering the desired response on the left side.

7. Direction of comparison: Avoid bias of tilted comparisons.

8. Number of points: Use enough points to have a reliable scale.

9. Context effects: Avoid bias by keeping to the same context.

10. Type of overall evaluation question: Don’t use the same type of question in all items ofthe scale.

The System Usability Scale

Considering the experimental nature and demanding functionality of a conversational interface, itcould be the case that the usability of the prototype was generally too low. If so, the results ofinfluence would without doubt be affected. To also get an indication of how the prototype performedin terms of usability, another Likert scale called the System Usability Scale (SUS) was added tothe evaluation. This scale is a quick and proven method of estimating the overall usability for acomputer system [7, 6].

4.3. Evaluation 49

4.3.2 Test procedure

It would be very demanding to measure and evaluate the full effect of using a model of data-drivendesign with a sustainability perspective, seeing that this would require a very complete businessscenario with an actively used digital product. This case study did not have the time or resourcesavailable to try such a scenario. Rather, we studied the potential to apply a sustainable perspectivein data-driven business strategies, referencing earlier work, and argued what effect it could have onthe human interaction with a system.

Evaluation of the influential aspect with the data-driven sustainable design approach was the re-maining effort to evaluate. When applying a conversational interface in the case study scenario,which regards the human interaction with such a system, is it effective in influencing user behaviorwithin that scenario?

The guidelines from the evaluation framework were followed to the best of ability and applicability.The evaluation of the prototype was done by giving 10 test users free access to the prototype fora period of time. All test users were representatives from the target group, as in entrepreneurs orleaders of companies in technology or design.

The participants received instructions of what the purpose of the test was and how it would work.They were asked to picture themselves in the scenario of their own company being in need ofa new office location and as if the office suggestions were actually real proposals based on theirprofile.

The test users were given access to free use of the prototype. They could chat with Mio whenever,wherever, how often and for how long they wanted. Mio was a completely independent and dis-tributed service. The instructions however also contained five tasks that they were required to atleast try, to be able to properly answer the awaiting questionnaire. These tasks were:

• Ask something about an office suggestion

• Tell Mio how you would prefer an office to be different, if not happy with the suggestion

• Continue to search for at least two different office suggestions

• Try the different interactive buttons

• Chat on two different occasions (preferably 1 hour in between)

Online survey

The goal of the evaluation was that the resulting data would give indication of the influentialimpact that the prototype had on the user behavior. Following the guidelines from the evaluationframework above, it was decided that an online questionnaire would be sufficient to evaluate theprototype. When the test participants had completed the tasks and were ready to submit theirexperience, they responded to the survey. The survey was created with Google Forms and theresults were later analyzed with Google Sheets. The complete contents of the online survey can beseen at appendix B.3.

4.3. Evaluation 50

The survey consisted of three parts. The first part was a Likert scale, for measuring the influentialimpact of the prototype. It contained eight statements where the users ranked how much theyagreed or disagreed to each statement. The statements were based on the persuasive principlesin the theoretical framework. Each principle was in turn counted as an influential aspect of Mioand thus the scale could give an overall measurement of how the prototype performed in terms ofinfluence. The statements signified both an individual and a collective focus of influence for theLikert scale. These principles were ability, motivation, triggers, physicality, psychology, language,social dynamics and social role.

Another standalone statement regarding general influence was also added to the survey, but thiswas not part of the influence scale. It was instead used as an individual measurement that latercould be compared with the score from the influence scale.

The second part of the survey was the SUS, estimating the usability of the prototype. It was createdin the same way as the scale of influence, but had 10 statements instead of 8.

Each statement was ranked from 1 to 5 with labels of strongly disagree to strongly agree. See anexample in figure 4.10 below.

Figure 4.10: A statement in regards to the persuasive principle of raising ability, and how theresponse alternatives was showed in the rating scale.

To answer the research question and understand surrounding aspects of the user experience, moreopen questions other than the scale ratings should also be acknowledged. The third and final part ofthe survey therefore had some general questions about the user interaction with Mio and what theythought of this interaction paradigm. These questions would give some more qualitative insightcompared to the scale results.

The Api.ai platform makes it possible to observe user’s previous conversations. It is hard to interpretuser experience from just observing this history of message interactions, but it is possible to makeout where Mio clearly misunderstood a user message and led the conversation to be less effective.These moments are also clear indications of when user frustration occured. Analysis of the testparticipants conversation history was therefore also weighed into the analysis of the open questionsof the survey.

4.3.3 Test transparency

For the sake of user awareness in this experiment, Mio is totally transparent with the fact that itwas just a concept prototype, and that the office suggestions are not actually personalized. The

4.3. Evaluation 51

users were also told beforehand in the test instructions that the office suggestions are just staticexamples, where it was clarified that the purpose instead is to evaluate this type of interface in thepresent context.

The test instructions as well as Mio itself were also open with the fact that it serves sustainability byprioritizing offices in less urban areas. This so that the users could better understand why certainlocations were suggested.

4.3.4 Result analysis

User rankings in Likert scales are actually closer to a qualitative dataset rather than a quantitativeone. The statistical level of measurement in Likert scales falls into the category of ordinal data. Itis normally not appropriate to calculate mean and standard deviation on such data, since it canbe costly to assume equal interval lengths between the data points. However, it has long beencommon practice to treat Likert scale results as types of interval or ratio data [29]. It has alsobeen proven that this assumption is actually reasonable in terms of result reliability, as long as asufficient sample size of at least 5-10 observations per group is used [49].

There is even compelling evidence that parametric analysis of ordinal data is more robust comparedto non-parametric analysis. It gives a more objective answer, even if rules regarding statisticalassumptions like that of a normal distribution are gravely violated [49].

The test results from the Likert scales were therefore mathematically treated as interval level ofmeasurements in the analysis of the survey results. The responses was however also taken intoaccount as individual qualitative measurements.

The SUS result was retrieved along the common practices of this specific Likert scale, as in cal-culating a usability score from the survey data. This was done by summarizing the rankings andconverting the sum to be a score between 0 and 100. For this scale, 68 is the average of the span. Ascore lower than 68 is considered bad and lower than around 55 is not at all acceptable, see figure4.11 [2]. This range does not give a percentile rank, but Sauro created a model for how to expressthe SUS score also by percentile rankings [49]. Thus, a score of 68 represents the middle percentile,as seen in figure 4.12.

Figure 4.11: The scoring range of SUS with different factors of estimating the usability [2].

4.3. Evaluation 52

Figure 4.12: The scoring range of SUS by percentile rankings [49].

This scoring approach of SUS is considered to give quite a rigorous estimate of the general usabilityfrom a small amount of rated statements, with an intuitive range from 0 to 100 [6]. To keep theresults consistent and intuitive, the same scoring range was used when analyzing the results fromthe Likert scale measuring influence, with the exception that this score signifies overall influenceinstead of overall usability.

When conducting a statistical analysis of quantitative results, it is needed to compare the resultswith some other data for it to gain meaning. For SUS, the overall scoring range seen in figure4.11 has been found by countless comparisons between previous usability data. In the case of theinfluence scale, there was no other data to compare the results with. It was therefore difficult toknow what the score amounts to. The scoring range used for SUS could not be applied to theresults from the influence scale, since it refers to completely different things. The range of 0 to 100could nonetheless give a rough estimate of influence in similar proportions.

One way to put a value to the resulting influence score was also to correlate it with the qualitativeaspects from the ratings, which had some meaning on their own. The user ratings of each individualstatement gave qualitative insight to how well the prototype performed in regards to each persuasiveprinciple. The influence score was therefore weighed against the results in influence from eachrated statement. The score could then together with the qualitative analysis signify some levelof influence. Bangor also recommends this approach when interpreting the overall usability fromSUS, as in considering the results from each single rating complementary to the total SUS score[2].

The open user feedback from the survey and observations of the user conversation history wasdocumented and summarized. The full analysis of all results then led to conclusions of the casestudy, as well as bringing insight to the overall discussion of the research.

Chapter 5

User research

This chapter shows the general results from the user research with entrepreneurs. Hence, it givesinformation of what is important for a technology company and what requirements there are whenchoosing office space. A municipal leader was also questioned regarding the same question points,about what the regional and national ambitions of growth and development are. The insights willbe addressed in the following design rationale, chapter 6. The full interview manuscript can be seenin appendix A.2.

5.1 Responses from entrepreneur interviews

What was the biggest problems with the surrounding work during your start-up?

The most significant difficulties for companies in the startup phase was of two different types:

1. Defining business model and strategies.

2. Expanding network: Finding investors, partners, customers and related competence.

Was it troublesome to find an office?Simply finding office space was in majority not considered to be a problem for the interviewedstartup companies. The problem was rather to find a place where there is people and other com-panies, potential customers, collaborators and other stakeholders that could be relevant to con-nect with. It was however in the beginning expressed as problematic to find a long-term suitableplace.

What did the process look like when you were searching for an office?The methods were spread among hiring brokers, looking at online market sites or getting in contactwith people who had common interests.

What are your requirements for a region and working environment?When it comes to what is important for a working environment and region, the second of theproblem types in the first question was the overwhelming priority. It was expressed that acquiring

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5.2. Regional versus national drivers of economic growth 54

connections and establishing an exchange of competence and experience valued high. The resultsfrom the discussion on this question shows a clear need for companies to have near access to arelevant social community.

Do you think it is important to be located in or near a big city as a company? Why?It was considered important to be in or close to a big city, since it is there easier to find peoplefor hire as well as other stakeholders when the company expands. Comments hint the willingnessof hiring people on remote, but when it comes to customer relationships, it is important to haverepresentatives face to face.

What administrative tasks for your company would you appreciate that a digital servicehelped you with?Several suggested a service that could find relevant contacts. That it could automate personalrecommendations, not just people in related fields. It could be to find connections for recruitment,or just social exchange.

Summarized general feedback It seems that having contacts nearby as a company is a centralconcern and finding what actors that exist in an area is important in deciding if it is a goodlocation. When looking for an office, practical parameters like price, size and location setting isalso of interest.

There is also a demand for recruiting competent people when growing and those people are ofteneasier to find in larger cities.

If addressing an international market directly, it is not as important to be in proximity to a largecity when it comes to finding customers, but it is still preferred for finding new employees. Also,due to the globalization and digitalization, the interviewed entrepreneurs think that the actuallocation people work at will get less important with time. Physical meetings and communicationcan perhaps come to be replaced by virtual meetings or other technology, when it becomes sufficientenough.

5.2 Regional versus national drivers of economic growth

An interview was conducted with the executive of a municipal organization in Sweden responsiblefor the development of regional innovation and entrepreneurship.

The results showed that the ambition for every municipality in Sweden is to grow in population,since that entails regional economic growth. This effect is pursued by attracting competence, whichwas said to be the only thing that really matters in today’s economy.

The expressed circumstances was that there is a situation of competition between municipalitiesto grow and prosper. State support is given to those regions that show less successful resultsin terms of population and economic growth, but this is an insignificant compensation. Mattersof establishment are always municipal initiatives, not national. They are free to make their owndecisions, but there are also laws regarding the means that can be taken to attract enterprise.

The ways that most regions try to attract companies and private actors is by investing in cultureand housing, which is often very long-term policy work. Other concrete measures is to launch

5.2. Regional versus national drivers of economic growth 55

organizations like incubators and other initiatives of entrepreneurship, which aims to also create asocial community to promote an exchange of ideas and learnings.

Chapter 6

Design rationale

This chapter connects back to the theoretical framework and the pilot study results as a foundationof why and how a data-driven design approach works, in perspectives of sustainability, business anduser experience. It connects back to the overview of the prototype in section 4.2 and why it wasimplemented in the way it was. Hence, we give a concrete example of how a data-driven designapproach could be applied.

6.1 Designing for sustainable behavior

Considering how the field of designing for sustainable behavior has been addressed in previous work,as discussed in section 3.1.2, there seems to be an overall consensus that designing for sustainabilitycould be achieved by influencing behavior. Design nevertheless already has a persuasive effect byits own definition. It also seems that influencing human behavior could be a very profound and thuseffective way of making a difference for sustainability, as discussed in section 3.1.1 and visualizedin figure 3.1.

So why is there no widespread use of influencing sustainable behavior with design?

There are several definitions for how designers can relate to these concepts and specific ways of howto approach design with a sustainable mindset, as seen in part 3.1.2 of the theoretical framework.There are even more frameworks and principles of how to find specific sustainable design decisions.Furthermore, there are also many ways to design for persuasion to influence behavior, as seen insection 3.3.2.

This is all important and valuable research, but the main problem is consistently that the sustainableresponsibility in these guidelines exclusively lies too much with the designer, which indirectly meansthe business responsibility.

We argue that it is not realistic to expect businesses to increase efforts and change values in theirdesign strategies simply because of good will and public pressure. This was also a conclusion from

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6.2. A data-driven approach 57

Lilley’s research, discussed in section 3.1.2, where it was showed how businesses would most likelynot adopt sustainable strategies unless it also enables a competitive or marketable advantage.

We think a more adaptable approach would imply to see how companies work today and how itwill evolve in the future to find overall more durable methodologies of how design can benefit bothindividual and collective needs together. It needs to avoid compromise on the ambitions of profitand customer experience but at the same time strive towards sustainable development.

Another part of the problem is how you for every industry and every business would know whichdesign decisions that leads to sustainable behavior. Since each individual scenario represents a verycomplex situation, it is difficult to find how a company can concretely go about it in each case, evenif aware about the holistic context and applying a systems-thinking approach. A designer can neverreally know beforehand what the sustainable impact of a product will be, regardless of how muchreflection and user research that is put in, as argued by Stegall [48]. Shedroff distinctly addressthe same problem, especially referring to the difficulty of measuring a product’s impact in terms ofsocial sustainability [47].

From the literature studies, we therefore interpret the fundamental problems of why businesses donot design for sustainable behavior as:

1 Willingness: To justify a sustainable initiative.

2 Awareness: To find what particular actions that are sustainable.

6.2 A data-driven approach

As elaborated above, the identified problems of applying a sustainable design perspective for abusiness is about 1; not seeing any benefits and 2; not knowing how to apply it for each case. Weargue that a useful strategy to apply in the perspective of these two problems is to use a data-drivenapproach. Consequently, it expands on the effort of how to design for sustainability.

Using data insight for sustainable decisions is definitely possible, as discussed in section 3.2.8. Wealso know that the role of data will be more and more important in a business perspective, asstated in section 3.2.2. Furthermore, we have covered how AI and data can help people make gooddecisions, in section 3.2.7. Therefore, it should be further explored how data can be used in aperspective of influencing sustainable behavior.

The data-driven approach suggested in this thesis refers to the collection, analysis and use ofdata to influence sustainable behavior through a dynamic interface. Overall, it builds on thesystematic perspective and overall principles of designing for sustainability, discussed in section 3.1.Below, we further explain the advantages of the data-driven approach in reference to the theoreticalframework.

6.2.1 Why data-driven?

We claim that a systems-thinking approach is not enough on it’s own in designing for sustainablebehavior, as it requires too much time-consuming and speculative work of choosing and following


appropriate frameworks/strategies which could still result in uncertain outcomes. We suggest witha data-driven approach to shift the responsibility from the shoulders of the designers to be more onthe ”shoulders” of the strategies themselves. Perhaps, this could be regarded as a more modern andflexible approach, as it would theoretically be easier to apply and easier to use. Moreover, manycompanies already rely on data-driven strategies and systems, and the sustainable perspective wouldespecially in those scenarios be easier to implement.

A purpose of using data-driven strategies within a business is to find exactly how design is affectingbehavior and how behavior in turn is affecting something else. Accordingly, it can be found whatspecific actions or behavior that would be beneficial for each context. It is used in the businesslogic, going through a sort of supply chain, see theory section 3.2.3. If a sustainability perspectiveis applied in this supply chain, it could be possible to find solutions of what actions or behaviorthat would be advantageous for sustainable development in that very context, just like data con-ventionally can give insight of business profit. If these two factors overlap, using data analysis canhave a positive impact in both sustainability and business perspectives. Companies would then alsobe more willing to execute such a change (1. Willingness). Simultaneously, data-informed designis also confirmed to be effective in creating great user-satisfactory products, as discussed in section3.2.6. So, the data-driven approach could be a way to align ambitions of sustainability with thoseof business and user experience, see figure 6.1 below. This alignment, as mentioned in section 3.5.4,has also been addressed by the former Secretary-General of the United Nations Ban Ki-Moon asthe most important aspect of sustainable development [52].

Figure 6.1: Three equally important drivers for a company from a holistic point of view. Adata-driven approach can make it easier to align them into one way forward.

Data-driven design can also be the solution to the problem that designers don’t realize the impactof their products, as discussed in 3.1.2. In a data-driven, ubiquitous and connected society, they canbetter know the effect of their design and they can design for the optimal intents, with respect to


profit, sustainability and user experience. Hence, this approach address also the awareness problemof applying a sustainable design perspective (2. Awareness).

As stated in section 3.5.4 and 3.2.4 of the theoretical framework, it takes several things like policy,investments, innovation, initiatives and responsibility to strive towards sustainability. But, if alluser interaction with digital systems in society is data-driven with an integrated sustainabilityperspective, this could all come more naturally (figure 1.1). This vision will be further explainedin the final discussion of the paper.

As discussed in section 3.1.2, Pereira argues that sustainable values in product design should alwaysreflect the target user’s values, not the values of the product designers [40]. It is a valid argument,but we believe that a target group’s behavioral values can in fact differ from what would be themost sustainable behavior in that context. We see the data-driven design approach as a way tofind a balanced design in-between, where sustainable values can coincide with user-centered values.This would align a good user experience along with sustainable development, facilitating humanjudgment and avoiding designer bias.

6.2.2 Data-driven how?

Using capable and intelligent algorithms together with human exploration, we assert that analyzingaccessible data and tracking user behavior can deliver patterns and accordingly find specific detailsfor how behavior could be changed in favor of sustainability, related to the use of a service orproduct. This would be done by learning from feedback within the data supply chain and iterativelymeasure outcomes of how user decisions is affecting the surroundings, see figure 3.3. Interpreting thefeedback from a sustainable perspective and business perspective requires multifaceted optimizationalgorithms and work strategies. The resulting measures in design that could influence sustainableand profitable user behavior can then be correlated and again further optimized, going through thelooping process of the data supply chain.

For many companies today, the accessible sources of user tracking and other data types might belimited when it comes to analyzing behavioral outcomes and understanding the holistic impact. Itcould therefore also be needed to expand data collection to additional sources, to know more aboutthe impact of specific behavior. This could mean to utilize open data and collaborate with publicorganizations or other companies. The reason of expanding data collection does not have to beonly because of sustainability, since having access to more data can evidently lead to better insightsand in turn more growth, see section 3.2.2. Looking at the current trends of development in bigdata and internet of things, as discussed in section 3.2.1, it is apparent that data will generallycontinue to grow in explosive rates. Fittingly, as opportunity of data exploitation grows and morecollaborations initiates, this data-driven model of sustainable design gets even more effective.


Figure 6.2: A visualization of how the approach of data-driven sustainable design would work ina business logic with the data supply chain.

This data-driven approach does not neglect previous guidelines of sustainable design like using asystems-thinking perspective and following specific frameworks, discussed at section 3.1. Ratherthe opposite. Data-driven strategies still heavily builds on the systematic perspective, consideringthe complete impact of a product or service. Since people are still involved in the data-drivenstrategies, using frameworks and principles of sustainable design can also still be relevant, especiallyin the beginning of applying a sustainable perspective in the data supply chain. The purpose ofdata-driven systems and strategies is however to become less and less dependant on these typesof frameworks, since better and more automated data analysis would supply more adapted anddetailed insights.

An influential interface

The user interface in this context is an instrument to influence a person or company into theextracted behavioral change. Without data analysis, the purpose of a persuasive interface cannotbe generally applied in terms of sustainability. Without an interface, the data insights cannot affectbehavior. There has to be a link in between for potent change in behavior.

As visualized in figure 6.2, user tracking and other types of data can give feedback of the behavioraleffect and businesses can then do actions based on the information of what behavior that shouldbe beneficial for this context. The interface represents a channel to persuade a person or company


into this specific behavior. Hence, the interface needs to be flexible enough to be compatible withdifferent situations, so that the data insights can be used to influence behavior with versatile intents.Machine learning, which is part of conversational technology, could significantly help designers,engineers, et.c. in making decisions based on all this contextual information and benefit more fromthe capacity in human judgment, which was learned in section 3.2.7.

A conversational interface could here be a more resourceful medium to interpret behavioral data,compared to other elements of GUIs. With sufficient machine learning, you can potentially with lessuser effort extract more information about a user context from natural language, such as opinions,tone of voice, intentions and so forth than you can from user interactions with a GUI.

We also believe that a conversational interface has more potential to influence behavior comparedto a GUI. This due to their ubiquitous and accessible nature and how it uses language to interactwith a user. This approach is absolutely not about manipulating users. It is about finding waysto combine the values of user experience and sustainability in the design, and not excluding thebusiness ambitions.

Of all the ways humans communicate, texting might be the most direct. Text carries less redundantinformation than other ways of sending information. With text, there are no voice intonations todecipher or accents to understand, no facial gestures to interpret, and no body language to translate.Text is something computers can understand rather easy and process rather quickly, and that iswhy messaging is a great place for AI to serve human needs. Furthermore, there is also the economicargument of how messaging apps constitutes a substantial market to meet customer needs (section3.4.2). Voice user interfaces are perhaps the most accessible and ubiquitous, but a chatbot interfacemight be a better adapted type of conversational interface in the context of the case study.

The validity of all these beliefs regarding conversational interfaces is what the case study will tryto prove.

6.2.3 Interdisciplinary collaboration and linked data

In the situation ofa business not having enough data to draw conclusions from an analysis, a problemis knowing for each case what kind of additional data sources that could be beneficial, how to findthem and how to access them.

Thinking about the nature of big data and how more information will transition to be open, linkedand semantic in the future, as discussed in section 3.2.5, standardized linked data algorithms willlikely be able to deal with this problem too. As data becomes more cross-referential, accessibleand trustworthy, the process of finding the right data source has the potential of also being moreautomated. However, until this standard becomes mainstream, alternative methods of data andmetadata aggregation with helpful tools to find appropriate sources is probably necessary. Again,it is as such still going to be necessary to apply manual sustainable design frameworks, particularlybefore more automated systems and strategies have sufficient sources of data.

From the literature studies, it is clear that big data can be used for different purposes. The privatesector, which is the target of this research, is driven by profit, while the public sector is driven byresponsibility. They do however have much in common, in reference to section 3.2.4, like reliability,effectiveness and efficiency. Part of the aim with the research was as mentioned to put focus


on if businesses can include drivers of sustainability in their overall activity, and thus shouldermore responsibility, without compromising on the profit and user experience. If public and privatecompanies were to collaborate more, sharing data and insights, it could conceivably construct a win-win situation. Companies gain more insight to their business and could easier apply a sustainabilityperspective at the same time. Public organizations and research institutes could earn more insightand increase their impact as well. There is of course obstacles surrounding the ideology of thepublic and private sector working together, such as matters of politics and regulations, as discussedin section 3.2.3. However, applying a data-driven approach of sustainable design within a businessis not dependant on collaboration with the public sector. A data-driven sustainable approach isstill viable if there are collaborations and partnerships regarding data access exclusively betweenactors within the private sector, or no collaboration at all.

6.2.4 The gist

A clear data and design strategy could evidently improve results of profit and customer satisfaction.However, applying a sustainable perspective in these strategies still implies some risk to contradictthe business goals. The decision of applying a sustainability perspective in a data-driven modelstill has to be a conscious business initiative, so why would anyone want to mix in a sustainableperspective that could be suspected to risk interference with results of profit?

The reason we can see this data-driven approach to be successful is because of how incorporating asustainable perspective into the data supply chain would minimize both the required effort of suchan initiative and the risk of it having a contradicting effect.

The idea is that this could more effectively solve for the identified problems, in section 6.1, ofwillingness; not seeing any benefits, and awareness; not knowing how to apply the sustainabilityperspective for each individual business case.

As Shedroff also claim in his book about sustainable design, there is never going to be a perfectsolution to sustainability issues. When it comes to measuring and deciding sustainable methods,there are only trade-offs in better or worse [47]. Data-driven sustainable strategies could be wherethe optimal trade-offs are found in every situation.

Ambitions from different perspectives (sustainable, profitable, user satisfactory) could in fact meanmore data insight, and if more detailed data insight consequently means better results, then adopt-ing the sustainability perspective could prove to be a predominantly advantageous decision. If notapplying a sustainability perspective, a business might not actually get as much feedback to basetheir actions upon and their results end up comparatively less advantageous. This argument mightbe somewhat generalizing, but part of our theory is that sustainable drivers in data-driven businessstrategies could actually retain or even improve business results.

One might at this point ask, what exactly is this sustainability perspective and how exactly is itincorporated into a data-driven business scenario?

This is very dependant on each individual scenario, where the actual concrete measures wouldemerge from the iterative data analysis itself. It will be explained more concretely in the followingprototype rationale for the case study, where the data-driven approach is applied as an example,see next section (6.3).

6.3. A data-driven approach in practice 63

6.3 A data-driven approach in practice

So, we have argued for many advantages with the data-driven approach of influencing behaviorfor sustainable development. Here, we have applied these ideas in a narrow context, as in imple-menting a hypothetical business case and proof-of-concept prototype. This prototype represents adigital product helping other companies in finding personalized locations to start office (corporatesettlement). Thus, a business-to-business type of customer relationship.

We will not cover the actual business model for this digital product, as this was deemed excessivefor the conceptualization.

The prototype is a cloud application with a conversational interface, or more simply put, a chatbot.This section covers the reasons behind the specific prototype design and implementation, withreference to the above elaborated data-driven approach, literature studies and the pilot studyresults.

6.3.1 Data strategy

The data-driven approach in this example refers partly to the methods and strategies in design andimplementation of a digital product, but it also consequently refers to how the concept productitself would use data in serving user needs and influencing sustainable behavior.

As should be clear at this point, the figurative expression of data going around the data supply chain,refers to how a complete business is infinitely and iteratively working with data. As explained in thetheoretical framework at section 3.2.3, this is defined by the holistic data and design strategies thatare being used within that business. Not the technical implementations nor the human explorationon its own, but the combined process of interpreting data and acting on the feedback from previousturn.

In the case of developing this prototype, the loop of data has only properly gone around this supplychain once or twice. The more times, the more parallel and the quicker this looping process wereto proceed, the more pro-environmental, profitable and socially valuable aspects would emerge.Corresponding actions in design and implementation would then also be more easily applied. Thus,the design and product outcome would improve, for all three perspectives in the diagram at figure6.1, profitability, sustainability and user satisfaction.

Hence, the data insights that came from the introductory collection in this research is just thevery first phase of the sustainable development process. The actions taken from this user feedbackis therefore just a first draft of the product, where there is no real autonomous circulatory dataanalysis. The prototype serves as a proof-of-concept.

It was however made sure that it is possible to extract contextual information from conversationsand dynamically build a user profile to give better adapted results. It was also deemed possible toimplement an automated data analysis in the context of browsing geographic and demographic datato find suitable options to a user, using data from previously mentioned available sources above.In this way, it is not only the process of creating the product that would be data-driven in reality,but again, also the product itself.


6.3.2 Data implementation

What data would the data supply chain in this specific business example consist of?

The part of the data supply chain that refers to how the product itself uses data is about analyzingand classifying the user context, such as identifying individual needs and location-specific data, tosee what practical details for offices and what area that could be relevant. This can be done throughuser tracking and collecting information. Of course, this business context also require a source ofgeographical data to actually find matched office results, which is another part of the data supplychain. An option here is to find a company or organization, who sits on this type of data, andinitiate a cooperation with them as a collaborator. Another perhaps more efficient approach couldbe to use open data, as long as the terms of use are applicable. Possible alternatives of this type inSweden are open access APIs like Google Maps API9, Eniro API10 and Hitta.se API11. Practicaldetails like price and space could be retreived from some partner service, their each individualwebsite or also from some source of public information. This data collection might or might nothave required manual aggregation, depending on the available data supply.

Using these sources in this way is a sufficient data strategy for the business context, as it could givesuggestions of offices that are relevant to the user in price, place and other practicalities. But, ifwe apply our sustainability perspective, more data collection becomes necessary. So first, we needto define what the specific sustainability perspective is in this context.

A sustainability perspective

As declared in chapter 3.5, innovation and technological development matters to sustainable devel-opment. It also matters where innovation and technological development occurs, and how popula-tion and economic growth is distributed. The mainly addressed UN global targets for sustainabledevelopment are about promoting an inclusive economic productivity and growth (section 3.5.1),which comes from fair distribution of resources in rural and urban areas, thorough city planningwith local, national and global perspectives, et.c. All this while promoting technological innovationand sustaining local cultures, see section 3.5.5.

Equally distributing economic growth in all of Sweden could improve quality of life for the wholepopulation, preserve cultural value and prevent social segregation, and most importantly contributeto the technological development and innovation. This is partly how the experiment aims to pro-mote sustainability. Endorsing technological development and innovation is the most importantfactor of promoting sustainable development, as stated in section 3.5.4. This is the other part,since the prototype supports technological development by bringing people together and buildingcommunities of innovation. The experiment of this research is an endeavor towards these targetsby trying to influence companies in choosing sustainable settlements that also helps them in theirgrowth.

9https://developers.google.com/maps/ Google Maps APIs, accessed 2017-07-0510https://api.eniro.com/getting-started Eniro API, accessed 2017-07-0511https://www.hitta.se/api Hitta.se API, accessed 2017-07-05


Putting the pieces together

So as we then applied our sustainable perspective on the business context, we found that it becamerequired to also include data regarding regional demographics and social context for each companyusing the service. The initial user research and literature study of this thesis, which is a form of dataanalysis (although exclusively qualitative), identified the need of these new data points; social anddemographic dynamics. It came from applying a sustainable perspective with a systems-thinkingmindset and analyzing the user needs.

This data would not have been necessary to the business context before, but with this sustainabilityperspective, it came to be needed as a part of the data supply chain. We accordingly argued thatincorporating this data into the data supply chain could improve not only the sustainable factor ofthis example business, but both the user experience and results of profit as well.

So, we hypothetically included data about regional social context in the data supply chain of thisprototype, such as where there are other actors relevant to a company’s development. By doingso, we benefit sustainable development by bringing people together and building a community,which drives innovation and technological development. This data could for example also havebeen retrieved from Google Maps API9, alternatively from social networks like LinkedIn12 andFacebook13 or other similar sources.

We also hypothetically included data of national demographics, like population density. This givesmore insight of locations that would be better options in terms of economic distribution, by findingplaces that are in more need of growth than others. This could help in linking development ofrural and urban areas along local, national and global ambitions, which is part of the UNs globalgoals, showed at section 3.5.1. It can also help in balancing the distribution of growth betweencompetitive regions, which was the outspoken case by a municipal leader in Sweden reflecting onthe current situation (section 5.2). This data could for example have been retrieved freely fromgovernmental organizations like SCB14 or international access points like SEDAC15.

12https://www.linkedin.com/ LinkedIn, accessed 2017-07-0913https://www.facebook.com/ Facebook, accessed 2017-07-0914http://www.scb.se/sv_/Hitta-statistik/Regional-statistik-och-kartor/Geodata/Oppna-geodata/# SCB

(Central bureau of statistics in Sweden), accessed 2017-07-0915http://sedac.ciesin.columbia.edu/data/collection/gpw-v3/population-estimation-service NASA

SEDAC (NASA Socioeconomic Data and Applications Center), accessed 2017-07-09


Figure 6.3: How the system architecture of the prototype could hypothetically work with anactual integrated business data analysis. See figure 4.2 and 6.2 for reference.

Required data for the business perspective:

• User metrics: User-specific details.

• Geo-data: Locations of offices and their practical details.

Additional data for the sustainability perspective:

• Social data: Information about regional markets and communities.

• Demographic data: Population dynamics like density, distribution, et.c.

We suspect that this additional data insight could in fact not only improve the sustainable impactof the product, but also increase the performance and level of personalization for the users ofthe service. The company representatives get suggestions based on where their operations fit inand where the market is well conformed to their purpose. Improving the office suggestions gives abetter user experience. This would lead to more traffic and consequently better profit, whatever thebusiness model would be for this hypothetical business. Hence, with help from this additional datainsight, the ambitions of all different drivers can be aligned and also utilized, see figure 6.1.

Addressing the two problems of applying a sustainability perspective, defined early in this chapter(section 6.1), it seems that applying a sustainable perspective in this context could give advantagesin profit and user experience. This solves for the willingness problem, and as data goes around thesupply chain, the awareness of where companies could settle to promote sustainability would becomemore clear as well, since the data-driven process would extract better and better alternatives.


6.3.3 Chatbot design

So as we had a specific idea of how to implement a data strategy for this digital product, whatremained was to actually create a user interface that would influence users to act on the officesuggestions, and thus adopt sustainable decisions.

As discussed in section 3.3.2 on how to create persuasive technologies, an effective methodologycan be to to include social cues in the design. Such as using aspects of appearance, personality,language, social dynamics and taking social roles. These social attributes are all very applicable toimplement with conversational interfaces. It also generally seems from the theoretical frameworkthat a conversational interface has the potential of being quite influential. The prototype wastherefore built as a chatbot, to test if this can be an effective medium of influencing sustainablebehavior.

Developing a brand

In section 3.4.2 we covered that consistently reflecting a brand is important when developing achatbot. Developing the personality of Mio therefore meant the same thing as developing thebrand of the example business, so this process was interlinked.

The interviews with the target group in chapter 5 suggested that the most important thing whenfinding an office and region to work in is the social network and community. We so thought thatthis should be a center aspect of the brand. The bot personality and brand, as disclosed in section4.2.2, were as such derived partly from the target group research. Beyond representing a businessidentity, the characteristics of Mio was also created with influence in mind, following the social cuesof persuasion, discussed in section 3.3.2.

Four keywords to define the brand and bot personality was developed, seen in section 4.2.2. Allof these words were supposed to saturate the social cues and match the target user needs andexpectations.

The first two keywords, trustworthy and confident, were particularly part of expressing the socialrole of Mio to the user, as a reliable assistant. The collaborative trait of Mio was especiallyto attract user reciprocation, and thus adopting social dynamics. The informal aspect was tohighlight a fundamental value of messaging in itself, as covered in section 3.4.3. This last word,more than the rest, also express the psychological aspect of having a positive emotional attitude,but still maintaining a reasonable level of reservation. This also by trying to make the user feelspecial and important in the conversation. The language principle was basically tailored for achatbot context, where the focus of Mio was to use engaging language and an encouraging attitudein the overall conversation.

Name and Avatar

The name ”Mio” was supposed to serve as an example of a commercially applicable name, whichdid not remind of one gender, and which also would be easy to remember. The service could alsohave been called something like ”office-finder”. But this would have significantly discounted theimpression of Mio as a social actor. The name was found in reference to an old tale from the


Swedish author Astrid Lindgren, where a boy travels to a land far away to fight off a great threat,just like the companies and the sustainability issues.

From the theory in section 3.3.2, it also says how attractive visual appearance can increase thepersuasive power of a computer system. However, the social principle of physical attractivenessis biased. What is considered attractive is very different for people. Creating a human-like visualappearance also opens up the risk of ending up in uncanny valley16, giving the opposite effect.There is perhaps value in anthropomorphism, but when it comes to intelligent assistants, we thinkthat not mixing in too much physical attributes fits the profile of the brand and bot personalitybetter. A company representative would value credibility before attractiveness. It was thereforedecided to avoid giving a face to the bot and instead create an avatar and logotype that expressesthe networking and community-binding aspect of the brand. See figure 4.1. This decision wasalso in line with the contemporary trend in appearance of the most widely used conversationalassistants, like Siri, Alexa, Google Assistant and Cortana.

Context

The appliances of chatbots are highly dependant on how wide context the bot should be able tounderstand and ultimately how complex user intents can be. It is therefore important to constrainthe conversation to a limited scope. For Mio, the scope is clarified to the user during the onboardingprocess displayed in figure 4.5. The user would then not expect more than what is promised in theintroduction.

When a user sends something that Mio cannot understand, it responds with a so called fallbackmessage. This message asks the user to rephrase their intent.

Conversation

The goal behavior from the conversation with Mio was that a user would contact an office provider.This by reason of how the test participants were actually not in the direct need of an office andsince the suggestions were not based on reality. It can be argued that in a real case scenario, a morepotent behavior aiming to influence would perhaps be to close the deal with an office directly inthe bot conversation. On the other hand, trusting a bot to that degree might be a barrier to manypeople. You could therefore say that this approach is also an implementation of the augmentedhuman approach, increasing the productiveness of human workers by doing the tasks that a bottypically does well and then hand over the process where the largest barriers appear. This wasmentioned in the theoretical framework at section 3.4.2.

Designing the language and conversation was about creating text content that reflected Mio’s per-sonality. The main conversation flow was also aimed to follow the persuasive principles of Fogg’sBehavioral model (FBM), figure 3.4.

Firstly, it was important to simplify the interaction and consequently raise the ability for a userto find and contact a suitable office. All conversation was therefore optimized for simplicity, bymodeling a simple and intuitive conversation. Besides adopting general usability principles to the

16https://en.wikipedia.org/wiki/Uncanny_valley Explanation of Uncanny Valley, accessed 2017-07-11


interface, the aim in this aspect was to reduce cognitive load and barriers of decision-making.The conversation is also optimized for the user’s time required to act; A user always choose forthemselves how ambitious of an answer that they want to send and how much time they want tospend on a decision.

The first time a user starts a conversation, just one reply is enough to get Mio started. This firstquestion is about what field that the user’s company acts within. The answer to this questionextracts fields and domains, and also what type of company it is. If Mio cannot recognize this inthe response, it uses follow-up questions to fill the gaps, see figure 4.5. Interactive buttons was alsoadded to each office suggestion to simplify the interactions and make the user aware of what typeof interaction opportunities that were prioritized.

The motivation of committing to the intended behavior is the second principle of FBM. In conver-sation with Mio, the user is motivated by information about the office context and its values. Themotivation is also intended to be raised by the engaging attitude and language used by Mio.

The last aspect of FBM is to sufficiently trigger motivated users to commit to the intended behavior.These triggers are in the form of a clear call-to-action to contact an office provider. This is partly inthe form of a button for every office suggestion. The same sort of trigger is given as a clear promptof contacting an office, when a user acknowledges or gives positive feedback to an office suggestion.See the last screen capture of figure 4.6.

A characteristic of conversations is also that it happens over time. For this reason and for Mio tobe able to learn about the specific users, the conversation was designed to happen over a period oftime and not rush into one final transaction. Also, as discussed in the framework at section 3.4.3,a benefit of text messaging is the possibility of asynchronicity. Due to this, the conversation wascreated so that users can come and leave as they like. Mio remembers the name of users and thelatest discussed office context, even if the user was away for a long time. See an example of this inthe last screen capture of figure 4.7.

Chapter 7

Results

This chapter displays the results from the main thesis evaluation. A mathematical analysis wascarried out to see if the quantitative results from the online survey gives any significant results.Qualitative aspects of the survey data is also summarized, to understand more about the generaluser experience.

7.1 Results of influence

On the range from 0 to 100, the total score of the prototype for the influence scale was 61,88 witha standard deviation of 12,23. See the scale in part 1 of appendix B.

The stand-alone statement of general influence, seen below on a scale from 1 to 5, was in average3,1 with standard deviation of 1,12. This corresponds to 52,5 on a range from 0 to 100, with thestandard deviation of 29,9.

Figure 7.1: Results from user ratings of the stand-alone statement in general influence.

The individual ratings of the statements regarding persuasive principles on the influence scale can

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7.2. Results of usability 71

be seen in appendix C. The qualitative aspects of these results, observing them objectively, suggeststhat the general performance of influence could be estimated to around average, perhaps just above.It coordinates pretty well with the overall grade of influence in the stand-alone statement, whichwas 52,5 (figure 7.1). It also correlates to a reasonable degree with the score from the influence scale,should it be following the same proportions of the SUS grading range, with a grade of 61,88.

The user ratings of the statements in the scale are leaning around average, while most are justabove. The statement that stood out the most was probably the one regarding the persuasiveprinciple of language, having an average of 4,1 in the range of 1 to 5. See figure C.6.

7.2 Results of usability

The statements rated for the System Usability Scale (SUS) can be seen in part 2 of appendixB.

The results from this scale gave a score of 64,25 in overall usability, with a standard deviation of13,95. This score is not significantly lower than an average score of 68, given the probability valueof 0,21. Using a percentile ranking system of SUS, seen in figure 4.12, this result means that theusability performance of the prototype was right below 40%.

7.3 General feedback

The results from the general questions in the end of the survey showed some complementary insightregarding the prototype as well as the overall research context. As most respondents were alsosomewhat experts in the field, either as entrepreneurs or as specialists in design and softwaredevelopment, they gave insightful and valuable feedback to the questions.

7 out of 10 respondents said that Mio satisfied their need of finding an office, where 1 said they didnot. The remaining 2 expressed that they would need more context-specific knowledge from Mioto have their need properly satisfied.

On the question whether they thought a conversational interface like this is effective in influencinghuman behaviour, 9 answered yes. However, if they were to compare the capacity of influence tothat of a graphical user interface (GUI), the views were scattered. Here, 3 voted that a CUI wouldbe more influential and 2 voted that a GUI would. 2 commented that a combination would be best.Another 2 thought that a CUI would be more influential only on the condition that it could givea very personalized experience. The rest pointed out how this is very dependant on the use case.If the respondents then imagined a typical website that served the exact same purpose as Mio, 4would prefer the website and 2 would prefer Mio, while some was not sure or would prefer a mixturebetween the two.

The fact that Mio would prioritize sustainable options over other alternatives encouraged 5 anddiscouraged none in considering the office suggestions. The rest said it did not have an impact ontheir decision-making or simply that they did not take notice.

7.3. General feedback 72

Other general opinions pointed to how an intelligent assistant must be supported by strong AIand good data to be relevant. When it came to Mio, several of the respondents remarked on itslack of ability to understand messages. Observing the conversation history from the user tests alsoclearly showed that Mio sometimes misunderstood or lost context of what the user was saying andresponded with something misplaced. This forced them to rephrase or repeat their message andsometimes even start over from the beginning of a topic. These mistakes from Mio caused obvioususer frustration.

Some respondents also thought that the use case of finding an office might require more of anoverview of available offices than Mio was able to supply.

Chapter 8

Discussion

In this chapter, we discuss the outcome of the thesis evaluation and give a conclusion to theresearch question. We cover overall limitations in the project and what has been learned from theresearch.

8.1 Result analysis

The results from the influence and usability scales do not quite seem to follow a normal distribution,but according to references, it does not matter to the results in this case that the sample is a bitskewed [49].

It is difficult to put a value on what the influence score amounts to, but the number gives a roughestimate. Comparing it to the individual qualitative aspects of the responses suggests that theinfluence performance of Mio was not so good. We consider the overall performance of influence tobe around average, with the possibility of leaning either below or above. Regardless, the result issignificantly lower than what was hoped for.

The usability score of around 64 is according to the SUS scoring grade in figure 4.11 an indicationthat the usability of the prototype is insufficient. Even though it is not significantly lower than theaverage, It is a low score that implies problems with the usability. According to the percentile rankof 40%, the same conclusion can be made. The bad usability performance of the prototype coulddefinitely cause complications.

Most test participants thought that Mio did meet their need of finding an office. Yet, the test scorestogether with overall opinions, remarks and observations reveals that Mio was not that effective ininfluencing their behavior.

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8.2. Limitations 74

8.2 Limitations

8.2.1 Not enough training

Even though the design and implementation of the prototype was a laborious effort, the prototypedid not live up to our hopes of an influential impact. The user experience proved to be quite bad,and we think that this is the main reason also for why the influential performance was low. Thelacking performance of Mio could also be noticed by simply observing parts of the user conversationhistory. The reason clearly was that Mio simply needed more training.

It was not enough to carefully design a bot personality, build a stable system and manually train thelanguage understanding with a few people. The most important thing about developing naturallanguage conversational technology is training data. It proved difficult to sufficiently train Miowith a close to manual approach, as in mostly anticipating variations of user inputs and classifyingthem to corresponding intents. The training requires a lot more data and more automation in theprocess. The performance would likely have been much better if Mio was trained by conversationswith a lot more users and could improve from this data on its own.

This outcome of bad bot performance should perhaps have been expected, since we were aware ofhow machine learning is heavily dependant on sufficient volume and quality of data. We simply didnot have access to the required resources to improve Mio’s performance much more than this.

An alternative would however be to use a machine learning platform that has already been pro-foundly trained on understanding language. An example is the IBM Bluemix platform, whichcontains a collection of machine learning services under the name of Watson, seen in figure 3.5.This would however make Mio less adaptive to a specific conversation flow related to finding of-fices. It would be more difficult to prototype a streamlined experience according to the businesscontext and Mio’s personality. So this decision was a trade-off between better NLP, or betterconceptualization, where we chose the latter.

8.2.2 Inadequate simulation of the business scenario

To properly test the effect of adding a sustainable perspective in the data and design strategy of abusiness and influence people to more sustainable behavior, it would probably be most applicablein a real business scenario, and not with an imitative prototype. The theory of this data-drivensustainable design approach address such a wide area of operational and automated activity that aprototype easily comes up short in demonstrating the full effect.

It would be easier in a real business context to actually measure behavioral outcomes from designchanges, which is the whole point of a data-driven approach. Furthermore, having access to moreand better resources would make it easier to train the CUI. The CUI would then be able to performbetter, which would also make it easier to measure the influential impact of this method.

8.2. Limitations 75

8.2.3 Transparency trade-off

The data-driven theory of this research advocates how businesses could apply a sustainabilityperspective without compromising the user experience. It is unclear for each business scenario ifbeing open with the sustainability effort to users and customers is going to affect the user experiencepositively or negatively.

This level of intervention in the user decisions is something that Lilley addressed in her research,mentioned in the theoretical framework at section 3.1.2 [32]. This boils down to the question: Howmuch and when should a system persuade certain decisions rather than supplying a basis and thenrely on user judgment?

As data analysis and AI can predict cost and benefit, human judgment can capitalize on thisinformation to make better decisions. We claim that this judgmental aspect can happen bothin the creation of a product or service, and when using that product or service. Thus, utilizingcomputer prediction and human judgment in either case.

Regarding Mio, it seems as being transparent about the sustainability effort was favorable, since allusers who took notice claimed to be encouraged by this fact. Anyhow, this trade-off is somethingthat probably has to be considered for each case, measuring feedback and acting on the bestresults.

8.2.4 Survey response bias

There is of course high risk of bias in the evaluation form when asking users of how they perceivedbeing influenced, instead of measuring how the actual behavior was influenced. The survey didhowever give a hint of how to proceed the design work, if the development of the concept prototypewould continue.

Besides getting help to take sustainable decisions, the target group could gain advantages and savetime using the prototype. The entrepreneurs in the case of this experiment was however not in theneed of a new office, which also could have affected the results.

8.2.5 Platform limitations

The functionality of the Api.ai platform was very limited and in some ways still in a beta condition.Platforms of CUI development might not yet be mature enough for certain areas of application.At least not when methods of testing and training are limited to a manual approach. But thedevelopment process will perhaps be better adapted to a future that is more data-driven, ubiquitous,autonomous and semantically synchronized.

The Slack messaging platform was chosen out of relevance to the business orientation. Slack-botsare typically developed for less complex purposes like fetching requested information at commandand providing users of the same team with information in a collaboration context. Natural languageassistants are less common in this area. Maybe Mio could be better adapted to the platform withanother approach, or on the contrary, maybe Mio would fit better into another messaging platform.

8.2. Limitations 76

The idea is however that natural conversation should increase the ability to give a personalizedexperience.

Chatbots preferably use a messaging platform to provide a service, just as Mio is available throughSlack. These messaging applications all follow similar patterns of a chat UI, which usually signifiesconversation between people. The user expectation is therefore that the chat will work in thesame way. We think this entails that users are less forgiving when mistakes are perceived inthis interaction paradigm. Though, the conversational interaction also has a lower threshold oflearnability.

A conversation with free spoken language not only requires a high level of machine intelligence, itcould also make it harder for users to decide what to say. The cognitive load increases as users arefree to type or say whatever they want. It might be that giving suggestions of user input for everyinteraction is an effective way of reducing cognitive load, as many rule-based chatbots do. But itcan also make the conversation less dynamic. In either case, Slack did not support this type ofdevelopment approach as well as other platforms do, like for example Facebook messenger.

8.3. Conclusions 77

8.3 Conclusions

The evaluation showed how CUIs are in more need of training data than what was available in thedevelopment process of the prototype. This fact can be considered to strengthen the premise thatdata is crucial to a data-driven approach of designing for sustainable behavior.

Although the results showed a lacking performance of Mio, the test users were still optimistic aboutthe influential capacity of a conversational interface, as 90% believed that it would be effective inthis perspective and that the majority believed that a CUI would be more influential than a GUI,if it was well implemented. A conclusion is therefore that it remains uncertain if conversationalinterfaces can be a more contributing factor to influencing behavior, compared to other interactionparadigms. This addresses the research question and in particular the third objective of the research,to find if CUIs can be used for the purpose of sustainable development. See objectives in section1.2.

Even though the case study failed to show if CUIs can be effective in influencing decisions, we stillbelieve that we have met the first research objective of identifying a potent way to easier design forsustainability, referring to the design rationale (chapter 6). We also hope to have contributed tothe second objective of the research in encouraging business organizations to work for sustainabledevelopment, as a data-driven sustainable design approach takes a fundamental aim to be easierapplied in a business context. This by incorporating clear data-driven sustainable strategies alignedwith business ambitions.

We cannot fundamentally prove any theories of this research. Be that as it may, we do believe thatwe have argued objectively and realistically for the conviction that adopting data-driven strategiesand including a sustainable perspective into a business scenario can make it easier to work forsustainable development without having to compromise on ambitions of profit and user experience.We conclude that this approach could increase the willingness of technology companies to worktowards sustainable development as well as giving awareness in how to go about it.

How to apply a sustainable perspective in data-driven business strategies is thoroughly addressedin chapter 6. In summary, the data-driven approach is mainly about benefiting data insight inbusiness strategies. Applying a sustainability perspective to these strategies means to find and usedata that can give insight to the specific sustainability situation of user behavior. This process is theloop around the data supply chain (see figure 6.2), acting on feedback according to for example userresearch or integrated data analysis, and in each scenario, influencing sustainable behavior.

8.4 Main learnings

It is very difficult without any previous insight to find concrete ways to how companies can promotesustainable development, because it is a very ubiquitous problem. This particular case study ismerely one example, as in influencing settlement. The most important thing to realize is that thesignificance is not in how different examples address sustainability. It is rather in adopting realisticmethods that would consequently result in specific sustainable outcomes. Applying data-drivenstrategies for sustainable behavior is possibly such a method, shifting the sustainable responsibilityfrom the designers to the design strategy.

8.4. Main learnings 78

When reading previous work regarding design for sustainability, there are remarks about how thechoice of sustainable design methods have to be well informed. This points to the importance ofdata insight in sustainable design. One could argue that Shedroff’s principles as well as all previousframeworks briefly mentioned in section 3.1 (Natural Capitalism, Cradle to Cradle, Biomimicry,Life Cycle Analysis, Social Return on Investment, et.c.) are in a sense data-informed, since theyare all about measuring a sustainable impact [47]. There are many shared values between theseguidelines and a data-driven approach, and the frameworks could in fact even be a part of data-driven strategies.

The biggest difference is in the focus and level of automation. The focus differs mostly in terms of theambition, as a data-driven approach aims to improve also the results of profit and user experience,not solely the sustainable impact. This is, again, to lower the reluctance of businesses adoptingstrategies of sustainable development. There are also previous sustainable design frameworks thataims to be business-positive, like the Sustainability Helix [47]. However, we argue with respect to amore direct data-driven approach, that shifting the focus to be on benefiting the data itself is a moreapplicable method. It is also pointed out that the lack of data, like tracking and measurements, isa problem with some of the previous sustainable design frameworks [47]. So, instead of followingframeworks that are dependant on specific measurements, the data-driven approach stresses tomake the most out of the data that exists or is possible to extract, turning the focus to applicablemethods instead of static guidelines. We also know that data will continue to grow, making thedata-driven approach better and better.

A data-driven approach also makes better use of technology and existing data. It is supposed tonot rely as much on the willingness and performance of individuals as the mentioned frameworksdo. Leveraging data insight could in the end imply that sustainable frameworks like these could endup being part of a data-driven sustainable business strategy, but with a more independent strategicstructure and automated technological infrastructure.

In many industries, there is a growing market segment demanding more sustainable products andservices. For this reason, it can seem unnecessary to talk about design influence and persuasion.Here, it is more about helping users adopt sustainable behavior and decisions. This is, regardlessof terminology, what the data-driven approach is aiming to accomplish. Thus, it can be used alsofor when there already exists a clear willingness of sustainable behavior on both sides of a business,supply and demand.

It should also be mentioned that applying a sustainability perspective is probably more effectiveand substantial in some industries and business scenarios, while less effective in others. Regardless,we argue that adopting a sustainability perspective with a direct data-driven approach will still beeasier to implement and more likely able to retain or improve the business results, compared tousing a sustainable design framework like the ones referenced above.

The case study of this research expresses a rather complex context of designing for sustainablebehavior, but the message is on the contrary pretty straight-forward. We believe that applying asustainable perspective with data-driven design strategies could make a business more effective andmore sustainable at the same time, with minimized effort.

8.5. A futuristic perspective 79

8.5 A futuristic perspective

Is there really anything more to sustainable development than changing human behavior?

We live in a evermore digitized world, but people are the ones who makes the decisions. As stated, ittakes several initiatives in areas of policy, investment, innovation and development in accordance toenvironmental, economic and social perspectives to strive towards sustainability. All of these thingsmore or less depend on data-informed human decisions, whether it regards top-level governmentaldecisions, business judgment or individual human decisions.

More and more, human decisions are generally made in connection to digital interfaces. If everyinteraction with technology would be influenced by sustainable drivers, where data analysis helpspeople make the best decisions in every situation, we would assume the impact to be substantial.Referring to the logic of the illustration in the introduction of this thesis (figure 1.1), we argue thatinfluencing behavior is the most potent way to promote sustainable development through design.This concept is based on how every decision builds on other decisions, sustainable behavior affectingother sustainable behavior in a collateral manner. Basically, a complete economy that is driven bydata-informed sustainable decisions.

We could draw a parallel to Crolls vision of a feedback economy, addressed in section 3.2.3, wherehe suspects that constant, iterative feedback within the data supply chain will soon become thenorm for businesses and governments [37]. Today, and even more in the future, communicationbetween products/services and customers concerns digital interaction. Consider the possibilitythat there would be a sustainability perspective embedded in every human-computer interface ofthis context. Consumption and trade is based on supply and demand. If this fundamental part ofmarket economy would be co-driven by sustainable ambitions, it could amount to a great shift inhow we approach growth and development.

This might be a utopian concept, including high dependency of data and other risks like monopolyof data. Nonetheless, it also further symbolizes the value, potential and ubiquity of applying asustainable data-driven approach within a business.

Chapter 9

Future work

In this chapter, we propose and encourage how future work can build on the insights from theresearch.

9.1 Theory validation

As mentioned in the beginning of this paper, one objective with the thesis was to encourage busi-nesses to incorporate a sustainability perspective in their data strategy and use it in their customerdeliverance to easier meet goals of sustainable development. We believe that the data-driven sus-tainable approach discussed as a model and tried in the case study is applicable in a businessscenario. There is of course theory and there is reality. The approach is highly dependant on asupply of relevant data. Thus, the optimal next step to validate the theory would be to actuallyincorporate a sustainable perspective within a real business, adopting measures of sustainable de-velopment through data-driven strategies in design. This in order to see if the model of sustainabledesign is realistic and effective today, or if it can be in the future.

We do however suspect, without confirmation, that this data-driven sustainable approach is some-thing that some businesses or organizations already more or less exercise today. Working with datahas a well known value regardless of the purpose and many would be familiar with this fact, withoutreally putting an acclaimed theory or effort behind it. The sustainability perspective could howeverperhaps be further recognized in all areas of implementation.

9.2 Business applications

The case study of this research concerns a service which adopts a sustainability perspective thatmay seem slightly far-fetched. However, the idea is that applying a sustainability perspective, inthe sense of small impacts for every user, can create a big and collateral difference in total impactof a product or service. This is regardless of what business model that is applied.

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9.3. A global perspective 81

If the data-driven sustainable approach was to be proven realistic and useful, applying a sustainableperspective would probably be more substantial in certain industries and business scenarios. Just asthe value would be less obvious in others. Mapping out categories of where data-driven sustainabledesign can have most impact could therefore be important.

9.3 A global perspective

The thesis addresses the sustainability goals with Sweden as a starting point. It would however berelevant to cover the subject on a global scale. Less developed countries might benefit even morefrom data-driven sustainable behavior, while they might also have less resources of regional andrelevant data. This poses a challenge worth addressing.

There are 17 goals each with their own defined targets of sustainable development in UN’s decla-ration. Not only would it be relevant to further address what business applications and regionsthat would be most substantial to affect with a data-driven sustainable approach, but also whatsustainability goals that might be more applicable to promote. The data-driven approach couldgenerally help all goals by benefiting data and technology, but perhaps there are certain areas thathave large collections of related data that could be used more effortless than in others. Where andhow could this approach help in the most essential ways?

9.4 Interdisciplinary collaborations

The importance of accessibility and sharing of data between different fields, sectors and bordershas been discussed in the research. The data-driven approach is dependant on the supply of data.A challenge regarding this is how collaboration between governmental organizations and companiescould be promoted. Sharing more information and insights between all sorts of clusters should bebeneficial to sustainable development.

9.5 Conversational interfaces

Part of the thesis was to test if CUIs could be an effective way of influencing sustainable behavior.The case study was not able to sufficiently prove that it could or not, which is why this should befurther investigated with better development methods and resources.

Chapter 10

Acknowledgements

Much appreciation goes to all entrepreneurs and other representatives for participation in the pilotstudy and main evaluation. I am also grateful to all reviewers of the research paper.

Lastly, I would like to thank Daresay for the opportunity and support in doing this work.

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Appendix A

Pilot study: interviewmanuscript

A.1 Entrepreneur interviews

A.1.1 Company priorities

This interview is to get a deeper understanding of what the work can look like in a very early phaseand how it can be facilitated.

• As a startup, what was the biggest problems with the surrounding work during your start-up?(Beyond the work on your service or product)

• Was it difficult to find a suitable office space for your company?

• What did the process look like when you were searching?

• Was it troublesome to find an office?

• What are your requirements for a good working environment?

• What is important for a region to be in as a new company?

• What are your requirements for a good living environment?

• Do you think it is important to be located in or near a big city as a company? why?

A.1.2 Project explanation and value proposition

My master thesis is about designing for sustainability and I am doing a case study to see if it ispossible to influence companies of taking sustainable decisions, with help from accessible data andnew technology.

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A.2. Municipal leader interview 87

My specific case study is to see how you could influence companies to settle or move their office toplaces other than the most urban areas, to strive towards the United nations sustainable goals of abalanced distribution of economic growth and innovation.

My plan for the experiment is to build a prototype that collects relevant data about regions or evenhousing in Sweden where companies could base their operations. I is suppose to serve the companyneeds as well as a sustainability perspective.

Except for getting a better view of what thework can consist of in a early phase, the purpose wasalso to identify what data that would be relevant to use for finding suitable, non-urban areas inSweden, based on the company needs.

• As a software or design company, what administrative tasks would you appreciate a digitalservice helped you with?

• What do you think about the scenario of a digital service helping to find a suitable region oreven office space for your company?

• Any other opinions or ideas?

A.2 Municipal leader interview

An explanation of the research project beforehand. Then a discussion regarding the questionsbelow.

• What is important aspects of a city or region for a technology company to be in?

• What is the motive with regional efforts to increase innovation?

• How do you attract actors in the public sector to your region?

• What kind of support is given from the government to promote regional growth?

Appendix B

Main study: online survey

This survey evaluates the user experience from the interactions with a chatbot prototype calledMio. Mio can help companies find office space that has personalised social relevance. You as acompany owner is the target group. The evaluation is part of a master thesis to try an hypothesisof designing for sustainability by influencing behaviour.

(Parentheses are not part of the survey)

B.1 User experience – Part 1

(Overall influence)

Here are the first 10 statements of how the bot was perceived. Rate for each statement how muchyou agree or disagree. Answer as if you were in in need of a new office when interacting with theprototype and as if the suggestions you received were actually real and accurate.

(See the corresponding persuasive principles in the parenthesis for each statement)

(Ranked in the span of Strongly Disagree - Disagree - Neutral - Agree - Strongly Agree)

1. I think Mio simplifies the process for me to find a suitable office. (Ability)

2. I felt motivated to get in touch with an office provider. (Motivation)

3. I was clearly prompted to contact an office provider. (Triggers)

4. I think Mio has an attractive appearance. (Physical attractiveness)

5. I think Mio has a charming personality. (Psychology)

6. I think Mio treats me with a good and engaging attitude. (Language)

7. I found Mio cooperative and I felt the need to express gratitude. (Social dynamics)

8. I felt special or important when talking to Mio. (Social role)

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B.2. User experience – Part 2 89

9. I think Mio had an influential effect on my decision-making. (General influence)

B.2 User experience – Part 2

(Overall usability)

Here are the last 10 statements of how the bot was perceived. Rate for each statement how muchyou agree or disagree. Answer as if you were in in need of a new office when interacting with theprototype and as if the suggestions you received were actually real and accurate.

(Ranked in the span of Strongly Disagree - Disagree - Neutral - Agree - Strongly Agree)

1. I think that I would like to use this system frequently.

2. I found the system unnecessarily complex.

3. I thought the system was easy to use.

4. I think that I would need the support of a technical person to be able to use this system.

5. I found the various functions in this system were well integrated.

6. I thought there was too much inconsistency in this system.

7. I would imagine that most people would learn to use this system very quickly.

8. I found the system very cumbersome to use.

9. I felt very confident using the system.

10. I needed to learn a lot of things before I could get going with this system.

B.3 General questions

Here are some general questions of your experience. Answer as if you were in in need of a new officewhen interacting with the prototype and as if the suggestions you received were actually real andaccurate.

• Does the interface and system satisfy your need of finding an office?

• Do you think a conversational interface like this is effective in influencing human behaviour?

• Do you think a conversational interface like this is superior to a normal graphical user interfacewhen it comes to influencing human decisions?

• Did the fact that Mio prioritise sustainable options encourage or discourage you in consideringthe suggestions?

• If you would think of the same service instead with a typical website format, which would doyou prefer?

• Anything else?

Appendix C

Main study: ratings of influence

Figure C.1: Rating of statement addressing the persuasive principle of ability/simplicity.

Figure C.2: Rating of statement addressing the persuasive principle of motivation.

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Figure C.3: Rating of statement addressing the persuasive principle of triggers.

Figure C.4: Rating of statement addressing the persuasive principle of physical attractiveness.

Figure C.5: Rating of statement addressing the persuasive principle of psychology.

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Figure C.6: Rating of statement addressing the persuasive principle of language.

Figure C.7: Rating of statement addressing the persuasive principle of social dynamics.

Figure C.8: Rating of statement addressing the persuasive principle of social role.

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