促進服務設計洞見發現之系統研究

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國立政治大學資訊管理學系

碩士學位論文

指導教授:苑守慈博士

IT-facilitated Insight Discovery in Service Design

研究生

：謝沛剛

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IT-facilitated Insight Discovery in Service Design

促進服務設計洞見發現之系統研究

by Pei-Kang Hsieh

A Dissertation Submitted in Total Fulfillment of the Requirement for the Degree of

Master of Science

In

Management information Systems

Supervisor: Soe-Tysr Yuan, Professor, MIS, NCCU

NATIONAL CHENGCHI UNIVERSITY July2014

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Acknowledgement

終於到了幫論文補上致謝的這一天，這三年的碩士生涯，比別人長了一些，但實在收穫豐碩。這一路上有太多人需要感謝，首先，也是最重要的，真的要誠摯的感謝我的指導教授－苑守慈老師。從剛進研究所，對如何做研究毫無頭緒時、論文進度比同學落後時，常常萌生想要放棄的念頭，但苑老師總是很有耐心地領著我往知識的殿堂前進，不論是大到研究的方向，小到一個標點符號，老師都不厭其煩的指導著我。真的非常感謝苑老師這一路上的耐心指導及包容，若沒有老師的春風化雨，這篇論文可能難以完成。老師淵博且深入的知識、嚴謹求實的態度、追求新知的精神，讓我這段時間獲益良多，雖然在這短短時間內從老師身上學到的可能只有九牛一毛，但我會努力追隨老師的典範，成為一個真正能為社會貢獻價值的T型人才。感謝台科大的宋同正教授、政大的梁定澎教授、林宛瑩教授給了我許多寶貴的提點和許多的鼓勵，讓這篇論文能夠更加完整。同時也感謝所有在這一路上幫助過我的學長姐、同學、實驗室以及球隊的學弟妹、StorySense的夥伴、在新店的室友們、以及幫助我進行實驗的台科大設計系許多同學們，你們用行動幫助我、鼓勵我、在最艱困的時候扶持著我，沒有辦法一一寫出你們的名字，但我由衷的感謝你們每一個人。最後，感謝我的家人，這二十幾年來謝謝你們養育我、栽培我，讓我可以無後顧之憂的追求自己的理想，謝謝你們總是無怨無悔的支持著我的每一個決定。希望我能夠真正讓你們可以不再為我操心。謝沛剛民國一〇三年六月於台北

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Abstract

Service economy has been under the spotlight during past decades as well as design thinking has been widely promoted in recent years. Developing a desirable service needs

in-depth understanding to customers. However, in the past, discovering insights from customers usually depends on designers’ experiences. It’s hard to do it well for novice

designers as well as enterprises with G-D logic mindsets. Although some researches have been done on designers’ ability, little information is available on information technology

facilitating the service design process. In this research, we propose an IT artifact with the commonsense knowledge in ConceptNet to facilitate the insight discovery process. We also

propose a concept of insight depth which can be a measurement of the influential extent of insights. This research is believed to shed light on both the management and design field for

services. We expect this can help no matter who are designing services.

Keywords: Service Design, Design Thinking, Sense Making, Insight depth, Common Sense

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5.1 AN APPLICATION SCENARIO ... 47 5.2 SERVICE BLUEPRINT ... 52 CHAPTER 6. EVALUATION ... 55 6.1 PROPOSITIONS ... 55 6.2 ASSUMPTIONS ... 56 6.3 EXPERIMENT DETAIL ... 57 6.3.1 Experimental Subjects ... 57 6.3.2 Experiment Design ... 58

6.4 DISCUSSION OF FINDINGS ... 80

CHAPTER 7. CONCLUSION ... 84

7.1 CONTRIBUTIONS ... 84

7.2 MANAGERIAL IMPLICATIONS ... 86

7.3 LIMITATIONS AND FUTURE WORKS ... 87

7.4 CONCLUSION REMARKS ... 88

REFERENCES ... 89

APPENDIX A. BASIC PROFILE OF EXPERIMENTAL SUBJECTS ... 93

APPENDIX B. EXPERIMENT INSTRUCTION AND QUESTIONNAIRE ... 95

APPENDIX C. SUBJECTS’ PERCEIVED SATISFACTION SCORES ... 101

APPENDIX D. EXAMPLES OF CONCEPT MAPS BY DESIGNER SUBJECTS ... 103

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Table of Figures

FIGURE 2.1 DOUBLE DIAMOND DIAGRAM (DESIGN COUNCIL 2005) ... 7

FIGURE 2.2 RAW DATA TO INSIGHTS ... 8

FIGURE 2.3 GENERATION OF INSIGHT (KOLKO, 2010) ... 11

FIGURE 2.4 EXAMPLE OF CONCEPT MAP (BIRBILI 2006) ... 12

FIGURE 2.5 EXAMPLE OF MIND MAP ... 13

FIGURE 3.1 A SIMPLE SERVICE MACHINE IS A HYBRID OF STS AND SSME ... 24

FIGURE 3.2 THE V+ PLATFORM ... 24

FIGURE 4.1 CONCEPTUAL FRAMEWORK OF THIS RESEARCH ... 29

FIGURE 4.2 INSIGHT DEPTH MAP ... 32

FIGURE 4.3 SYSTEM ARCHITECTURE ... 35

FIGURE 4.4 THEME FINDING IN CONCEPTNET (LIU AND SINGH 2004) ... 36

FIGURE 4.5 THEME RECOMMENDATION ALGORITHM ... 37

FIGURE 4.6 USER PERSPECTIVE RECOMMENDATION ALGORITHM ... 40

FIGURE 4.7 ENVIRONMENT RECOMMENDATION ALGORITHM ... 42

FIGURE 4.8 EMBODIMENT RECOMMENDATION ALGORITHM ... 43

FIGURE 4.9 USER GOAL RECOMMENDATION ALGORITHM ... 44

FIGURE 4.10 CRAWLER MODULE ALGORITHM ... 45

FIGURE 5.1 THEME-‐FINDER OF DISCOVER+ ... 48

FIGURE 5.2 INTERACTIVE INTERFACE OF FRAME-‐FINDER OF DISCOVER+ (1) ... 48

FIGURE 5.3 INTERACTIVE INTERFACE OF FRAME-‐FINDER OF DISCOVER+ (2) ... 49

FIGURE 5.4 RELATED CONCEPTS OF FAMILY (家人) ... 50

FIGURE 5.5 RELATED CONCEPTS OF TOUR GROUP (旅行團) ... 50

FIGURE 5.6 RELATED CONCEPTS OF CHILDREN (兒童) ... 52

FIGURE 5.7 SERVICE BLUEPRINT OF DISCOVER+ (1) ... 53

FIGURE 5.8 SERVICE BLUEPRINT OF DISCOVER+ (2) ... 54

FIGURE 6.1 EXPERIMENT PROCESS FROM THE SUBJECTS’ POINT OF VIEW ... 58

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FIGURE 6.3 PERCENTAGES OF USING ASSOCIATIONS FOR DIFFERENT GROUP OF SUBJECTS

... 61

FIGURE 6.4 COMPOSITION OF EACH TYPE OF ASSOCIATIONS FOR DIFFERENT GROUP OF SUBJECTS ... 62

FIGURE 6.5 THE INSIGHT DEPTH MAP OF THE INSIGHTS IN PHASE 1 ... 63

FIGURE 6.6 THE INSIGHT DEPTH MAP OF THE INSIGHTS IN PHASE 2 ... 64

FIGURE 6.7 HYPOTHESIS OF THE TESTING ... 67

FIGURE 6.8 CRITICAL VALUE OF T DISTRIBUTION ... 69

FIGURE 6.9 THE PORTION OF SOURCE OF INSPIRATIONS (INSIGHT QUALITIES IN MACRO VIEW) (PHASE 2). ... 71

FIGURE 6.10 THE PORTION OF SOURCE OF INSPIRATIONS (INSIGHT QUALITIES IN MICRO VIEW) (PHASE 2). ... 72

FIGURE 6.11 THE PORTION OF SOURCE OF INSPIRATIONS (INSIGHT QUALITIES IN MACRO VIEW)(PHASE 1 AND 2). ... 73

FIGURE 6.12 THE PORTION OF SOURCE OF INSPIRATIONS (INSIGHT QUALITIES IN MICRO VIEW)(PHASE 1 AND 2). ... 73

FIGURE 6.13 THE PORTION OF SOURCE OF INSPIRATIONS FOR DESIGNERS AND NON-‐DESIGNERS ... 74

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Table of Tables

TABLE 2.1 CONCEPTUAL TRANSITION FROM G-‐D LOGIC TO S-‐D LOGIC (LUSCH AND VARGO

2006) ... 5

TABLE2.2 COMPARISON OF MEANINGFUL AND ROTE LEARNING (NOVAK, 1993) ... 10

TABLE 2.3 THE COMPARISON OF DIFFERENT TYPES OF MAPPING TOOLS ... 14

TABLE 2.4 APPLICATIONS OF MAPPING TOOLS ... 16

TABLE 2.5 THE EDGES IN CONCEPTNET WITH EXAMPLE SENTENCE FRAMES (SPEER AND HAVASI 2012) ... 18

FIGURE 2.6 KNOWLEDGE OF RELATED CONCEPTS IN CONCEPTNET (SPEER AND HAVASI 2012) ... 19

TABLE 4.1 TYPES OF ASSOCIATIONS ... 30

TABLE 4.2 THE DIMENSIONS OF PERCEIVED SATISFACTION OF INSIGHTS ... 34

TABLE 6.1 DESCRIPTIVE STATISTICS FOR PROPOSITION 2 ... 66

TABLE 6.2 FORMULA AND INTERPRETATION OF ONE-‐SAMPLE T TEST ... 67

TABLE 6.3 THE RESULT OF ONE-‐SAMPLE T TEST FOR PROPOSITION 2 ... 68

TABLE 6.4 CLICKS OF EACH TYPE OF THE SYSTEM RECOMMENDATIONS ... 75

TABLE 6.5 THE FACILITATING ABILITY OF DISCOVER+ ... 76

TABLE 6.6 DESCRIPTIVE STATISTICS FOR PROPOSITION 3 ... 78

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CHAPTER 1. INTRODUCTION

In this post-digital age, not only startups but also large enterprises make their greatest effort

to innovate their products and services to gain more revenues. Only the unceasing innovations can help enterprises to catch the market trends, or even more, create new trends.

Service design is as a means to service innovation and is a process aiming to create new or improve existing services to make them more usable, useful, and desirable for clients and

efficient/effective for organizations.

Discovering insights from customers and defining the design challenges are very

conclusive in service design. At the end of the insight defining stage, there will be a clear definition of the fundamental challenge or problem to be solved in the following stages

(Design Council, 2005). Hence, if the design challenge is not defined well, no matter how good the service is developed and delivered, customers won’t accept it. For enterprises, if the

value propositions of their services are not exactly what customers want, they may lose a huge amount of customers. Almost every firm knows that designing a service which

customers need is very important to survive in the intense competitive environment; however, there are actually only a few doing this well. Large enterprises have their existing business

model to gain revenue and most of them believe that they can continuously benefit from it. Hence, most of them would rather keep things as they always do than give a try to something

new and risky. In addition, large bureaucracies that make the big companies slow to make decisions are also barriers for new service development. In contrast, SMEs have a faster

decision-making time and usually are also more adventurous. They are much more willing to do service innovation but have less resources, both manpower and capital, to put in

innovations (Lindegaard, 2011). To sum up, both large enterprises and SMEs need a more efficient way to do the new service development.

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knowing the importance of service design and doing it well. First, for the aspect of

management, enterprises should shift their mindset from Good Dominant Logic (G-D logic) to Service Dominant Logic (S-D logic) and consider of the situations from all of the possible

stakeholders’ point of views. However, enterprises are usually accustomed to selling what they think customers need, instead of discovering what they need. Second, it is about the

aspect of the process of insight discovery. Service design is multi-disciplinary; therefore, service designers may come from many different fields including industrial design, business,

social science, engineering, etc. Although all of them are doing service design with S-D logic mindsets, the qualities of their discovered insights may be uneven since their diverse

expertise. How to combine their expertise to achieve synergies is also a question. The last is the aspect of tools. Although some studies have been devoted to the expertise of designers

(Cross, 2004; Paton and Dorst, 2011), little information is available on how information technology can facilitate the service design process. We argue that there should be an IT

system to facilitate the process of discovering the design challenge for every designer, no matter experienced ones or novice ones.

In view of these gaps, we specify three research questions in this paper which are listed

below:

(a) How to facilitate service designers to do insight discovery with S-D logic mindsets?

For the purpose of designing good services, having empathy is very crucial. We want to design a mechanism that can facilitate service designers to look upon the design context from

all of the stakeholders’ point of views so that they are able to discover insights for developing satisfying services.

(b) How the services designers process their cognitions to discover insights?

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way to discover insights as well as the qualities of derived insights might be different. We

want to examine the cognition factors of service designers when they are doing design synthesis and then we can design a mechanism to facilitate the process.

(c) How can we use information technology to facilitate the insight discovery process in service design?

For now, service design is mainly rely on human effort. Therefore, we want to adopt

information technologies to facilitate the process.

To summarize, in order to solve these research questions, in this research, we propose an interactive system to facilitate insight discovery in service design. The knowledge used in the

system comes from ConceptNet, a famous commonsense knowledge base developed by Liu and Singh (2004) in MIT. With our system, designers can easily find themes from the data

obtained through interviews and observations. The system also helps designers reframe the situation to find more prospective new users, new operating environments, and new

embodiments. Furthermore, we conduct a series of experiments to evaluate the system as well as respond our research questions.

The organization of this thesis proposal is as follows. Chapter 2 gives related literature reviews. Chapter 3 describes the motivating application – Prosperity Taiwan, which this

thesis is involved. Chapter 4 provides the basic concepts and the architecture of our system and an application scenario is provided in Chapter 5. In Chapter 6, we conduct a series of

experiments to evaluate the system and some findings are provided. At last, we conclude this research in Chapter 7 including contributions, implications and future works.

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CHAPTER 2. LITERATURE REVIEW

In order to figure out how to use information system to facilitate the insight discovery process,

in this chapter, we review some existing knowledge in order to properly position the research. Firstly, we review the basic concepts of Service-Dominant Logic followed by the design

process with design thinking embedded. And then we go through the insight discovery and problem defining process that traditionally require a lot of human efforts. Following, we

consider the insight discovery process as a meaningful learning according to the Constructivism point of view. Finally, we review the fundamental essence of ConceptNet

which is the main source of the knowledge using in the designed artifact.

2.1 Service-Dominant Logic Mindset

In the past, many enterprises only produce what they ‘think’ of customers’ wants, instead of to ‘discover’ what they really need. Once enterprises cannot shift their conservative mindset

of G-D logic to S-D logic, they can hardly innovate. From the S-D logic point of view, customers are operant resources for the firms. Instead of only offer products, firms should

interact with their customers frequently to co-create the value of the services they provide and also gain more customer understandings during the interactions (Lusch and Vargo, 2006).

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Table 2.1 Conceptual transition from G-D logic to S-D logic (Lusch and Vargo, 2006)

S-D logic argues that firms should offer total solutions with good experiences to their

customers (Vargo and Lusch, 2004). From the viewpoint of S-D logic, the value of service is co-created through the combined effort of service provider (including firms and their

employees), customers, stockholders, government, and other related entities. However, the value of a service is always determined only by the beneﬁciary (most of the time is the

customers). In view of this, when designing a service, designers (both designers inside and outside of the firm) should make their greatest efforts to understand the context of the

customers using service trying to maximize the value of service during the co-creation with them. The more the designers understand the customer, the bigger chance to make the service

successful. However, the knowledge and experiences emerged during the design process are hard to save or copy so that many enterprises don’t know how to do this well. As a result,

many research institutes proposed some systematic methodologies and ways to think for new service development. One of the most famous one is “Design Thinking” which was first

proposed by Peter Rowe (1987) and has been promoted by IDEO and Stanford d. school recently.

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2.2 Design Thinking & Service Design Process

The core of design thinking is to understand the process and method that designers use to do

user-centered design and learn to solve problems like them. Following the guidelines of design thinking, everyone can act like a designer to solve problems in our daily lives as well

as businesses can elevate innovation to a higher level. There are four stages of design process – discover, define, develop and deliver (4D’s). The double diamond diagram shown in Figure

2.1 is a design process model developed by Design Council (2005), which indicates the designers’ way to do divergent and convergent thinking in each stages. First, in discover

stage, designers search for initial ideas through user researches. And then they synthesize the discovered ideas to define the design challenge. Once the challenge is defined, designers start

to develop potential solutions. After doing several times of user testing and modifications, the service will be delivered to the market.

This research focuses on the stages of discover and define, the initial stages of design, the purpose is to identify the design problem, opportunity and needs to be addressed through

design and also build a rich knowledge resources with inspiration and insights (Design Council 2005). This is a divergent stage in which designers try to find all possible insights

from the stakeholders. The most important essence of user-centered design is empathy in users. Successful innovations rely on the thorough understanding of users including their

needs, preferences, dislikes and any other thoughts (Brown, 2008). The best way to find user needs is to observe or interview them. Through direct contact with the users, designers can

also gain some insights and inspiration, which are very important in the following stages of design process. After being engaged in the users’ lives through observations and interviews,

designers can create an empathy map, which contains what people say, do, think, and feel, to better understand the users (Osterwalder and Pigneur, 2010). Subsequently, laddering the data

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achieve a good design. This process is called design synthesis, which is the process of

making meaning through abductive sensemaking and reframing (Kolko, 2010). That is, to continuously understand the connections among people, places and events and then attempt

to find a new perspective of them (Klein et al., 2006).

Figure 2.1 Double diamond diagram (Design Council, 2005)

After having many insights, designers integrate and interpret them to define the design

challenge. Usually, they’ll have a brief to explain it before going to the develop stage. During the develop stage, designers diversify their thinking to come up with a lot of possible

solutions. In this stage, multi-disciplinary work is required in order to develop many forms of possible solutions. After testing and adapting, the resulting product or service will be

finalized and ready to be launched in the market during the deliver stage. This four stages of design is not sequential, instead, they are iterative and can go back to early stages if need. In

this research, we focus on the discover and define stage. We aim to using an IT-artifact to facilitate the insight discovery process.

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2.3 Human Efforts in Insight Discovery

As described above, finding insights includes a sequence of tasks including interview,

observation, finding themes from these data, framing and reframing the situation and then finally implications and insights may emerge. Besides, these tasks are not sequential, instead,

they are iterative. Designers may go back and forth to find more proper and better insights. The process is shown in Figure 2.2.

Raw data through interviews and observations

Themes

Frames

Environments User perspectives Embodiments User goals

Implications and insights

Figure 2.2 Raw data to insights

Traditionally, the insight discovery process needs lots of human efforts. In order to find themes from the qualitative data obtained through interview and observation, designers

should analyze word repetitions, scrutinize terms, and analyze the linguistic features like metaphors or transitions, which are very time-consuming (Ryan and Bernard, 2003).

Although many design methods invoke the use of sticky notes with grouping and abstracting techniques to facilitate the theme finding process, it still consumes a lot of time. Subsequently,

the framing process to define the design challenges and goals also requires a lot of time and depends on the expertise of designers (Paton and Dorst, 2011). Speaking of expertise, it is

also a decisive factor for the quality of discovered insights. Novice designers are playing the role much more like ‘technician’ in the design process, which means they are probably only

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able to solve the well-defined problem instead of discovering and defining by themselves

because of the lack of experiences (Paton and Dorst, 2011). For the sake of solving these problems, we regard the insight discovery process as a new knowledge creating process

which we will discuss in the following paragraph from the Constructivists’ view.

2.4 New Knowledge Discovering from Constructivists’ View

The process of insight discovery is a procedure of discovering knowledge. Back to 17th Century, Bacon (1952, 1620) argued that new knowledge is discovered best when we observe

the world carefully, unfettered by previous ideas or beliefs, which is one of the origin development of Constructivism. From the constructivism point of view, there is an idea of

epistemology which hold that knowledge is constructed based on previous knowledge and will be constantly evolving over time (Toulmin, 1972). That is, when facing a new and

unfamiliar thing, people tend to find some characteristics or properties of this new thing and try to link to one or more things that exist in their mind. For instance, when a boy had a milk

tea at the first time and not knowing what it is, he might try to find some property of it and link to his previous experience. For example, its brown color may make him think of black

tea whereas its taste may let him recall the experience of drinking milk. Hence, he will know that this thing is probably a kind of beverage and is made of some ingredient like black tea

and milk. This is an ordinal example of meaningful learning, which is contrasted to rote learning according to Ausubel’s (1963) assimilation theory. Rote learning, in contrast to

meaningful learning, is more arbitrary and more verbatim. When doing rote learning, people probably just memorize the material by repetitively read it instead of trying associate it to

their existing knowledge. It mostly happens in school learning, especially when people have to learn a lot of things in a very limited time period. The comparison of these two types of

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Table2.2 Comparison of Meaningful and Rote Learning (Novak, 1993)

Dimensions Meaningful Learning Rote Learning

Incorporation

with cognitive

structure

Non-arbitrary, non-verbatim,

substantive incorporation of new

knowledge into cognitive

structure.

Arbitrary, verbatim,

non-substantive incorporation of new knowledge into cognitive

structure.

Linkage

between new and existing

knowledge

Deliberate effort to link new

knowledge with higher-order, more inclusive concepts in

cognitive structure.

No effort to integrate new

knowledge with existing concepts in cognitive structure.

Learning

method

Learning related to experiences

with events or objects.

Learning not related to

experience with events or objects.

Commitment Affective commitment to relate new knowledge to prior learning.

No affective commitment to relate new knowledge to prior

learning.

Learning field

Creative production. Most school learning.

Novak (1993) suggests that creating new knowledge is a form of meaningful learning.

The process involves recognition of new regularities in events or objects, the invention of new concepts or extension of old concepts, and, in the most creative leaps, major

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the insight discovery process which involves observations and interviews in order to gain

empathy of stakeholders; the framing and reframing process to refresh the perspective to view the design situation which may lead to the discovery of potential innovative

opportunities. Correspondingly, this is the concept of what Kolko (2010) proposed the notion of design synthesis. Kolko (2010) suggests that a design insight can be seen as the

combination of problem-specific observations (I saw this) and professional experiences (I know this). We can find Kolko’s definition in Figure 2.3. In view of this, a design insight will

contain subjective judgment of the designer and also objective data from the design situation itself.

Figure 2.3 Generation of Insight (Kolko, 2010)

After gaining the understanding of how people discover new knowledge, in next paragraph

we will see some mapping tools that can facilitate human learning and knowledge discovery.

2.5 Mapping Tools Facilitating Framing and Reframing

2.5.1 Mapping tools

As we mentioned above, the insight discovery process is a form of meaningful learning in

order to create new knowledge, which requires the ability to form and process mental imagery. When doing the design synthesis, in order to discover insights, designers iteratively

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experience). In order to design satisfying services, designers should understand the problem

context broadly and interpret it with their existing knowledge or past experience so that they can frame the situation properly. Finding what cause the situation, who are the stakeholders

and what they desire are very critical. When framing the situation, designers can draft a concept map, which is a great tool for capturing explicit and tacit knowledge and facilitating

creative works and makes human learning in any context much easier (Novak, 1998).

Generally, nodes on a concept map are nouns or adjectives linked to each other by verbs.

It is essentially a representation of mental model of a concept that allows designers to see the ‘forest and trees’ (Kolko, 2010). For context finding and system modeling, concept map is

also very helpful. For example, Figure 2.4 is a concept map of trees and our living environment (Birbili, 2006). It tries to shape the system of all the concepts related to the

situation. By this map, designers can get the whole picture as well as many details. Designers can find the context and also understand how a change in one aspect of system may influence

the whole system.

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Besides concept map, there is another tool called mind map (Buzan, 1995). Mind map is a

tool that presents the context of how people think helping people to think logically in a visual way. Mind maps not only can make designers arrange their thoughts logically but also can let

their thinking spread fast. We can see an example of mind mapping in Figure 2.5.

Figure 2.5 Example of Mind Map

In the past, these two types of mapping tools are defined in a narrow sense. Even though both of them can provide a visual representation of some related concepts, there are still some

differences between them. Traditionally, concept map is used to present schemas of understanding within the human mind whereas mind map is usually used to present one’s

train of thought. Since its strict definitions, concept maps have been used in Science and

Engineering researches in the fields like Engineering, Medical, and Psychology (Axelrod,

1976). In the meantime, mind maps were widely used in Design and Business field to support creative works. However, the purpose of using mapping tools should be facilitating human to

think and make senses of data and some scholars think that they have not to have a clear definition only if they can do the facilitations well (Ahlberg & Ahoranta, 2004). Hence, a

notion of free-form concept map has been widely used for different fields especially suitable for social science research (Axelrod, 1976; Wheeldon & Faubert, 2009). The comparison of

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human-centered design by IDEO (2010), a successful service should hit the overlap of these

lenses: desirable, feasible, and viable. Hence, to design a successful service, designers must care about not only the design itself but also the social impact of the design, the business

model to profit, and so on. As a result, this notion of free-form concept maps is suitable for them to systematically frame the design situations. From now, in the thesis, we will use the

term ‘concept map’ to represent the notion of the free-form concept map.

Table 2.3 The comparison of different types of mapping tools

Traditional Concept Map (Novak, 1993) Free-form Concept map (Wheeldon & Faubert, 2009) Mind Map (Buzan, 1995) Sample thumbnail representation

Definition A top-down diagram

showing the

relationships between

concepts, including

cross connections

among concepts, and their manifestations Compared to traditional one, a free-form concept map is without a clear hierarchy, linking words, or even unidirectional arrows.

A radial diagram that

represents semantic or other connections

between portions of

learned material

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Concept maps can help us to organize data for studying, problem solving, and

decision-making. However, the results of concept mapping are very subjective. Under the same circumstance, different designers may construct totally different concept maps based on

their own experiences and point of views. Hence, with the purpose of developing a desirable service, the understanding of customers is very important when doing design synthesis. Only

when the designers are totally at the same side as customers’, the modeled system will be insightful. In this research, we want to design a system that can recommend objective,

problem-specific frame parts for designers to make their thought more empathy.

2.5.2 Framing and Reframing

Furthermore, as a tool, concept maps can help designers to find potential innovative

opportunities through framing and reframing. Frame is a product of mental knowledge and meaning structures (Reckwitz, 2002). As mentioned before, frames are very subjective.

Designers make subjective value judgment to build up their perspective of the design situation. Hence, frames are usually very person-specific based on the designer who

Main function or benefits Show systematic relationships among sub-concepts relating

to one main concept

Show systematic

relationships among sub-concepts in a

creative and more unrestricted manner.

Show sub-topics of a

domain in a creative and seamless manner

Understandab

ility by others

High Medium to High Low

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reframe the design challenge to jump out of the box and get some innovative thoughts. The

initial frame is the foundation of new frames. Reframing is to construct a new frame by changing the perspective to view things (Paton and Dorst, 2011). When the system model and

the context are very clear, designers may know how the new perspective would influence the situation. New frames to see the certain service or action in a new context will facilitate

designers to discover hidden links and chances (Kolko, 2010). Jon Kolko (2011) suggested that, in order to discover new chances, designers can view things in new environments, from

new user perspectives and as new embodiments. For example, when designing a tooth cleaning service, designers can think about toothbrush used in different environments like in

kitchen or on airplane. Also, different users have different purpose of tooth cleaning. For instance, the elderly may need to clean their dentures where as children may need more focus

on preventing tooth decay. Besides, we can also think about new embodiments of toothbrush like mouthwash and dental floss. By reframing again and again, designers may find some

crucial implications and insights.

2.5.3 IT-facilitated mapping tools

Due to the widely usage of concept maps and mind maps, there are plenty of computer

software and online applications aim to facilitate the mapping process. Here we list some of the popular ones in the table below.

Table 2.4 Applications of mapping tools Tool

Name

Platforms Support

Collaborative Inputs

Purpose and Feature Access

Buzan’s Mindmap

Windows,

Mac OS,

No For mind mapping.

Integrated with

http://www. thinkbuzan.

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Linux, iOS, Android

Microsoft Office and

iWork. com CMap Tools Cross-Platfor m (Java)

Yes For concept mapping.

Supporting collaboration through cmap server. http://ftp.ih mc.us Coggle Cross-Platfor m (Web-based)

Yes For mind mapping.

Web-based, supporting real-time collaboration. http://coggl e.it Visual Mind

Windows Yes For mind mapping.

Support collaboration through client-server. http://www. visual-mind .com XMIND Windows, Mac OS, Linux

No For concept mapping

and mind mapping. Different type of charts

supported.

http://www.

xmind.net

There are a lot of applications with different distinguishing features, for example, some are

for personal, some are for business, some allowing real-time collaboration, and some having lots of templates. However, we haven’t seen any tool that can give recommendation concepts

for inspiration when doing concept mapping or mind mapping. In this research, we aim to use IT to give inspirations to users who are doing framing with concept maps. For the purpose of

providing recommendations, we utilize the commonsense knowledge base in ConceptNet which was developed by MIT media lab (Liu and Singh, 2004; Speer and Havasi, 2012).

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2.6 ConceptNet

ConceptNet is a machine-usable commonsense resource that was structured as a network of

natural language fragments (Liu and Singh, 2004). So far, it has been improved to version 5 (Speer and Havasi, 2012). There are over 12.5 million assertions with several types of

inter-lingual relations as shown in Table 2.5.

Table 2.5 The edges in ConceptNet with example sentence frames (Speer and Havasi, 2012)

The representation of ConceptNet is a directed graph. Every assertion can be seen as nodes connected by edges. Nodes are words or phrases that represent concepts and edges are

relations of linked concepts as illustrated in Figure 2.6. For example, in the figure we can find that a ‘restaurant’ is used for ‘satisfying hunger’ while a cake, which is one kind of ‘dessert’

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Figure 2.6 Knowledge of related concepts in ConceptNet (Speer and Havasi 2012)

The sources of ConceptNet include Open Mind Common Sense project

(http://openmind.media.mit.edu/) by MIT media lab, WordNet (http://wordnet.princeton.edu/) by Princeton University, and Wikipedia (http://www.wikipedia.org). It contains over 12.5

million edges connecting 3.9 million concepts including concepts in English, Chinese, Japanese, Portuguese, French, Spanish, and etc. The structure of ConceptNet is very similar

to people’s mental imagery, so it’s able to find context, make analogy, gist topic, and do other cognitive tasks just like human. Previous applications were done in, for example, wearing

recommendation (Shen et al., 2007), emotion detection (Cambria et al., 2010), and conversational storytelling (Chi and Lieberman, 2011). These applications utilized

ConceptNet mainly on inferring contexts. The wearing recommendation system by Shen et al. (2010) used knowledge in ConceptNet to recommend that you should wear a suit rather than

a T-shirt to a wedding banquet since a suit has the factors of ‘formal’ and ‘stately’ which match the context of a banquet. Another application, the SenticNet by Cambria et al. (2010),

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semantical analysis of users’ opinion of some products or services on e-commerce sites or

their blogs and social networks. Since these opinions are very unstructured, it utilized the common-sense knowledge in ConceptNet to infer the polarity of opinions and is able to

support opinion mining from natural language text at a semantic, rather than just syntactic, level. Chi and Lieberman (2011) also utilized ConceptNet as a tool to do natural language

processing. Raconteur 2 is a system for conversational storytelling between the storyteller and the viewer. It recommends appropriate media items for storyteller to make the story more

engaging. It used commonsense knowledge in ConceptNet and a novel commonsense inference technique to identify story patterns. With the commonsense knowledge, they use a

concept vector representation that goes beyond traditional keyword matching or word co-occurrence based techniques to give more precise recommendations. Resulting from its

nature of the representation of real-world knowledge and its ability to do the cognitive tasks, in this research, we use the knowledge in ConceptNet to find themes, contexts and potentially

innovative frames of the service to be designed.

In response to the research problem in Chapter 1, we reviewed the literatures related to

our research problem in this chapter. First, we discussed the service-dominant logic mindset that is the fundamental viewpoint of how we see service design. Following, in order to find

out how designers manipulate their cognition to solve a design problem, we examine the service design process and human efforts in it. We found that insight discovery process which

is composed of iteratively framing and reframing which is basically a knowledge discovering process (or meaningful sense making process). Finally, we compared some mapping tools

facilitating the knowledge discovering process as well as ConceptNet, a knowledge base which can help logic inference and be utilized in combination with information technology in

order to facilitate the meaningful sense making process. We will then present the mechanism in Chapter 4.

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CHAPTER 3. MOTIVATION APPLICATION

This research work is part of a larger research project dubbed “Prosperity Taiwan”. The main

goal of “Prosperity Taiwan” is to create a set of IT-enabled policies and workflows that aids Taiwan’s economic transformation.

3.1 Taiwan’s Economic Strengths and Current Economic

Landscape

As one of the Four Asian Tigers, Taiwan’s economic growth from 1960s to 1990s has

been exceptional, maintaining at a growth rate excess of 7% per year. Taiwan’s economic strength stems from its manufacturing capabilities, especially in terms of information

technology related products. Bulk of Taiwan’s business comes from building and manufacturing white-label products that is later branded with American or European brands.

However, Taiwan’s greatest economic strength turns out to be its biggest liability in recent years due to changes in the global economic landscape.

The present global economic landscape presents several challenges for the Taiwanese economy: increase in cheaper manufacturing alternatives such as manufacturers from China

or Brazil. This leads to increased price competition, severely cutting profit margins for Taiwanese manufacturers. There is also an increased premium placed on brand recognition

and intellectual property; despite Taiwanese’s manufacturing prowess when it comes to building world class hardware such as iPhone, iPods and other globally recognized devices,

Taiwanese manufacturers often receive up to only 3 to 5% of the profit share of each device sold. This is because the Taiwanese manufacturers do not own the brand and or intellectual

property of the device.

In response to the above challenges, the Taiwanese government highlighted several industries

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culture/arts/creativity and high technology agricultural.

3.2 The “Prosperity” in Prosperity Taiwan

Prosperity comes in various forms. While most people associate “Prosperity” with material wealth, we are interested in creating all-round prosperity. They are namely: Material

Prosperity, Spiritual Prosperity, Physical Prosperity and Social Prosperity.

3.3 Vision of Prosperity Taiwan

The vision for Prosperity Taiwan is to create sustainability in economic development by

moving up the value chain. The five major goals are as follows:

1. Enhance university-industrial cooperation

2. Kick start Taiwan’s economic transformation by creating “industrial modules” 3. Create a model for industry transformation

4. Start small, go big

5. Internationalize based on successful industrial transformation

3.4 Culture, Arts and Creativity as an Example

For a start, Prosperity Taiwan focuses on the culture/arts/creativity industry. The research team believes that the culture/arts/creativity is Taiwan’s sweet spot due to Taiwan’s unique

heritage and mix of Chinese and Western influences. We also believe that this group of people and its related stakeholders will benefit most from our research work.

We find that the following characteristics define the culture, arts and creativity industry:

o Not limited liability or private limited companies

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o Typically lack financial support or lack knowledge on funding options

o Business owners are at best conservative regarding the growth prospects of the culture and arts

On an industry level, the market exhibits the following characteristics:

o Market lacks a system for evaluating intangible assets o Industry lacks interdisciplinary talent

o Lack of financial support or lack transparency in terms of funding options o The industry lacks economies of scale to support growth

o Multiple stakeholders with political, economical, social and environmental objectives.

To combat the above issues, we propose the use of intelligent service machines to aid

industry transformation.

3.5 Intelligent Service Machines to aid Economic Transformation

Intelligent Service Machine(s) (Tung and Yuan, 2010) refers to an intelligent design of the

service machine featuring the embodied cognition of co-production in terms of modeling and automating the cognitive process and knowledge representations as required. Conceptually, a

simple service machine is a socio-technical system (STS) that comprises of people, model,

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Figure 3.1 A simple service machine is a hybrid of STS and SSME

3.6 The V+ Platform

The V+ platform (Figure 3.2) is an intelligent service machine that comprises of 5 simple service engines aimed at solving problems of industry transformation as exemplified in the

independent movie makers and the industry as a whole.

Figure 3.2 The V+ Platform

a macro-level, we have Capital+ forming the base of the V+ platform, since every stage of a product life cycle in various industries will most likely involve some form of funding

activities. On top of Capital+, Next, we have Discover+ which helps businesses discover new insights, which can be further strengthened or extended by the design insight patterns

recommended by Design Intelligence+. Finally, Sourcing+ can readily translate design insights into market opportunities by bringing about various operant resources from possible

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stakeholders in an industry under a holistic system thinking view. Finally, Intellectual

Property (IP+) comes into play by ensuring that proper IP strategies can be formed based on the operant resources identified in Sourcing+.

On a micro-level, each engine works as such:

• Capital+ - designed to solve the problem of funding and investment on an individual level and the industry as a whole. It provides a prediction model for individuals to estimate which investor(s) has a higher possibility of funding them. In addition,

Capital+ provides general industry growth models based on the evolution of investors and companies within an industry.

• Discover+ - an IT-artifact with common sense knowledge aimed to facilitate design insight discovery process. The common sense knowledge in this engine is powered by

ConceptNet. Discover+ is targeted at assisting novice and enterprise designers with only the goods-dominant logic mindsets.

• Design Intelligence+ - is developing the framework and method to externalize, formalize and facilitate “insight combination” that is a process of discovering

connection between “insights” and “design patterns” and combining them to generate idea or inspiration for service design.

• Sourcing+ - a service machine targeted at Small and Medium Sized enterprises (SMEs) to enable them to discover niche markets as stipulated in the Long Tail

Theory when given design insights. Sourcing+ is also an IT-enabled system that exploits open new operant sources from various stakeholders to provide discovery of

market opportunities found in the Long Tail.

• Intellectual Property+- aims at fostering the use of intellectual property as a means for moving up the value chain either through forging alliances or as a litigation/patent strategy according to the identified operant resources by Sourcing+. It provides a

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framework based on value network that help companies to pursue, manage and

maneuver their IP strategy.

V+ platform is designed to act as a generic service engine that can be applied to most situations. For simplicity and demonstration sake, we explain an application scenario

of how V+ platform can be applied in the next section.

3.7 Application Scenario of V+ platform

Consider the various issues ranging from political, economical, environmental and social factors when a company or business sets up a factory in a less developed region. Very often,

the company may face protests from local residents or even local governments due to concerns over environmental pollution, negative impact on way of life of local residents or

even adverse effect on the local economy. The V+ platform works by helping various stakeholders search and suggest possible solutions to this problem. Let us assume that the

agreed solution is to create a museum to educate the general public on environmental protection.

In this scenario, we are expecting multiple stakeholders and not all funding for building the museum will come from the company. Therefore, V+ platform can help to source for

multiple investors based on the stakeholders involved. This can happen at various stages of the construction of the museum. The main engine used for sourcing investment is done via

Capital+.

Discover+ creates new insights based on the stakeholders’ objectives. These

stakeholders include the local residents, governments, potential visitors to the museum and the company involved. After gathering the stakeholders’ requirements, these requirements are

used as inputs for the Discover+ engine, where new and potential insights maybe created for framing and reframing process. Such information is useful for museum designers or related

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service designers. The output of Discover+ is then used as an input for Sourcing+. Design

Intelligence+ then helps elevate the insights to foster the degree of impacts in terms of the scalability of stakeholders involved or the service values incurred.

Sourcing+ analyzes the insights provided by Discover+ and maps out possible required market intelligence and or related trends. For example, what can one expect from a museum?

What form of services, artifacts, displays, etc. can one expect from a museum given various stakeholders? Sourcing+ helps answer these questions by integrating current trends and

expectations, such as screening documentaries or educational movies on environmental protection, interactive learning for children and so on as inputs for market sensing. The

market sensing capabilities of Sourcing+ then facilitates research between various stakeholders and generates possible answers to aforementioned questions.

Intellectual Property+ adds support to our scenario by providing IT-enabled support for understanding the overall patent value based on the operant resources identified by Sourcing+.

Take for instance, the museum might be interested in deploying an operant resource of interactive TV. The related technologies and hence patents may be related to such as those

used in Microsoft’s Kinect. Such knowledge will help potential implementation avoid litigation and hence pave the overall patent/litigation strategy.

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CHAPTER 4. INSIGHT DISCOVERY FACILITATION

MECHANISM

This chapter illustrates the mechanism we propose to facilitate the insight discovery process.

Follow the IS research framework (Hevner et al., 2004), our goal is to build up an artifact with applicable theories from the knowledge base, which can fulfill the business need of a

faster and much easier way to discover more satisfying insights when doing service design. Also, the whole mechanism and findings can be added to the knowledge base for further use.

To achieve this, first, we construct a conceptual model to gain an understanding of how service designers discover insights and how to use an artifact to facilitate the process. We

also propose the concept of insight depth used to measure the influencing extent of insights. Second, we will propose the system architecture of the designed artifact which is aimed to

facilitate the insight discovery process. There are several modules in the system. We will describe them and give examples to demonstrate how they work to justify the feasibility.

4.1 Conceptual Framework

Figure 4.1 shows the underlying concepts and their relationships that unfold our

mechanism. The framework contains the insight discovery process from the cognitive science point of view and the attempted facilitation by the designed artifact. The concepts and their

relationships are depicted below. We will detail the framework from left to right including concepts and their relationships.

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Figure 4.1 Conceptual Framework of this research

4.1.1 Ability to Form and Process Mental Knowledge

As we discussed before, when building up a concept map to framing the design situations, designers are actually manipulating their mental imagery trying to make sense of the data. It’s

a new knowledge creating process and we will examine this process from two perspectives. One is the ability to make association; another is the synthesis process, namely, combine

what people see with what they already know.

Arrows (1): The nodes on the mind map can be seen as frame parts that construct the whole

frame whereas the links between nodes are the relations of the connected frame parts. In order to examine the ability to process mental imagery, we categorize these relations in five

types of association based on the law of association (Dawson and Medler, 2010), which are context association, analogy association, contiguity association, contrast association and

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Table 4.1 Types of associations

Type of association Definition Examples

Context association The relations between two

concepts with a causal or sequential relation.

Hot à Sweat

Wake up à Brush Teeth

Analogy association The relations between two

concepts with some shared meaning.

Life à Drama

Final Exam à War

Contiguity association The relations between a

series of concepts in contact or in proximity.

Transportation à

Bike, Bus, Train, Ship, Plane

Contrast association The relations between two

concepts with inversed

properties.

Happy à Sad Hot à Cold

Similarity association The relations between two concepts that share lots of

similar properties.

Bowl à Cup Hotel à B&B

Arrow (2) and (3): When framing and reframing the situations, designers iteratively

combine what they observe with what they already know trying to make sense of the situation

as well as gain some implications. The ability to do observation and interview is very important. The more the details are found, the more completed the system can be modeled.

Another crucial ability is the ability to recall and associate to past experience. Every found phenomenon needs to be interpreted by the designers to give meaning of it. Hence, to

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to the novices, experts in a field of study not only have more concepts integrated in their

cognitive frameworks but also possess broader extent of propositional linkages between subordinate and superordinate concepts (Chi, Feltovich, & Glaser, 1981; Novak, 1988). In

service design field, the ability to integrate the newly found concept with the existing knowledge will influence the quality of derived insights.

4.1.2 Insight Quality

After design synthesis, the derived insights will have different qualities. Here we estimate the insight quality from both macro and micro view. From the macro view, we examine the

influencing extents of the insights. On the other hand, we consider the perceived satisfaction of the designers to examine the insight quality from a micro view.

From the macro view: Insight Depth

The purpose of discovering insights is to develop new service concepts satisfying customers’

latent needs, drawing upon the capacities and influences of service providers. Here we adapt the 3-D model for value proposition by Kwan and Yuan (2010) for the measurement of

insight depth. The model has three dimensions including stakeholders, value (strategy) directions, and customer empowerment stages. Each dimension has several nominal values,

and a point or an area in the space is a potential value proposition. As mentioned before, our main ideas embedding S-D logic that emphasizes the value co-creation between service

provider and the customer. For the purpose of co-creating with customers to maximize the value, service providers tend to let customers take control of variables that are conventionally

pre-determinant by themselves. In other words, making the customers highly empowered in order to increase involvement and responsiveness. As a result, we only focus on designing the

services with high customer empowerment so that the dimension of customer empowerment stage is not taken into consideration in this research. Furthermore, we order the nominal

促進服務設計洞見發現之系統研究 - 政大學術集成

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國立政治大學資訊管理學系

碩士學位論文

指導教授:苑守慈博士

促進服務設計洞見發現之系統研究

IT-facilitated Insight Discovery in Service Design

研究生

：謝沛剛

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IT-facilitated Insight Discovery in Service Design

促進服務設計洞見發現之系統研究

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Acknowledgement

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Abstract

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