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Open Access

Open Journal of Information Systems (OJIS)
Volume 2, Issue 2, 2015
www.ronpub.com/journals/ojis
ISSN 2198-9281

Concept Design for Creating
Essential Hypothesis, Rules, and Goals:
Toward a Data Marketplace
Jun Nakamura A, Masahiko Teramoto BA

Kanazawa Institute of Technology, 1-3-4 Atago, Minato-ku, Tokyo 105-0002 Japan,
jyulis@neptune.kanazawa-it.ac.jp
B
ex-Volvo Group Trucks Technology, masa.teramoto@gmail.com

ABSTRACTThe abductive reasoning model has been discussed in the context of business strategy. However, this modelseems unrealistic for applications in the real business world considering the unpredictable, competitive businessenvironment. This study improves the model by formulating an experimental case study through a web-based
workplace for generating product ideas. We discuss the possible embodiment of product ideas as the basis forconfiguring features through the use of dynamic quality function deployment. The entire concept design process
is proposed as a blueprint for building a data marketplace.

TYPE OF PAPER AND KEYWORDS
Short communication: abduction, generating ideas, data marketplace, quality function deployment

1 INTRODUCTION
Differentiation is a fundamental business strategy toaddress competitive factors [11, 4]. On examining the
successful business strategies followed by companies
such as Disney 1 , Starbucks 2 , and Google 3 , eachoffering has its own context and unique stories throughwhich essential hypothesis and rules are created. This
results in goals distinct from those of their competitors
despite uncertain external information and data when
designing business strategy. Similar features emerge

when participants in a data marketplace take actions togenerate new ideas. To address this concern, this study
attempts to answer the following two researchquestions:
1. How will the new idea (as a goal) along with the
hypothesis and rules be formulated simultaneouslywith limited information or data?
2. How will the outcome of any new ideas generated
be embodied in a tangible form as a concept
design?
To answer the former question, we introduce anempirical environment called Kotofactory, which isable to record action logs in sufficient detail tofacilitate the examination of the process of idea
generation. The empirical environment as a tool for1

http://www.disney.com/
http://www.starbucks.com/
3
http://www.google.com/
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J. Nakamura, M. Teramoto: Concept Design for Creating Essential Hypothesis, Rules, and Goals: Toward a Data Marketplace

supporting creative activities [2] elucidates the firstquestion to some extent. Although the data volume islimited, we shall focus our knowledge and databases tocomplement the data to polish participants’ intuition.
The challenge here is how to reconstruct the data
already known as well as how to utilize this knowledge
to achieve the goal as new ideas in order to design adata marketplace. In this study, the data marketplace isdefined as an environment that helps both data
providers and data requesters retrieve the data’s value
by means of combination, reconstruction, or
reinterpretation of the data through the exchange ofshared data profiles as well as to ensure that thepossible concept design is as practical as possible.
To answer the second question, we introduce
dynamic quality function deployment (DQFD), which
is a framework to find optimized numeral and timeseries variables of market requirement, quality
characteristic, and design specification that shall becommonly related with each other to design a tangible
form. These three factors are sometimes conflicted, e.g.,longer distance as a market requirement of electricvehicles, 500 km per one battery charge as a quality
characteristic, but the battery shall be bigger andheavier and only few people can ride the electricvehicles as a design specification. Therefore, we arerequested to consider and identify the distance and sizeof battery the market can get using DQFD. We refer to
DQFD as a framework Kotoframe in this study. It is
used in the manufacturing industry for concept design,
and we assume that Kotoframe would support theprocess of embodying high-level ideas into tangible
forms, thus answering the second research question.
The rest part of this paper is organized as follows.
The following section introduces the related research.
Section 3 explains the experimental method to
implement Kotofactory. In Section 4, we provide
details of the results. Section 5 discusses a case study
on how the hypotheses, rules, and goals generated by
Kotofactory would be formalized. Finally, we discuss
the consecutive “Koto” series as an input for designingan effective data marketplace.

datasets to draw conclusions, make predictions, andformulate hypotheses and rules. An inference consistsof deduction, induction, and abduction. Abduction
develops hypotheses and rules in the context of
discovery, whereas induction is a manipulation for
empirically testing hypotheses and rules, derived from
abduction, in the context of justification [10]. In otherwords, hypotheses and rules derived from abduction
are different from those observed directly, andinduction involves inducing the existence ofphenomena similar to those observed.
Therefore, abductive reasoning contributes to thediscovery of hypotheses and rules, which could beconsidered as a method of generating new ideas. In theprocess of abductive reasoning, the first step is tosearch for possible hypotheses, and the second step is
to identify the best hypothesis from the pool ofpossibilities by activating one’s inference inconsideration of overall integrity. Peirce points out thatthe best hypothesis in the second step should be
evaluated on the basis of plausibility, verifiability,
simplicity, and economy [10].
Abductive reasoning has been modeled forformulating a business strategy, as shown in Figure 1,
depicting three business cases, Starbucks, IKEA 4, andCoca-Cola 5 [6]. The model in Figure 1 shows that in
the event that an observed fact F is given, ideas
generated as option A passed on to B, which is inferredin the context of rule 1. In contrast, option X implies B′
instead, which was initially an unexpected idea but was
discovered by abductive reasoning in the hypotheticalcontext based on rule 2, enabling the achievement ofgoal C.
However, business strategy and/or generating ideas
in the real world might become more complex
depending on the competitive environment and otherexternal factors like a technological revolution, whichbrings rapidly changing services associated with agrowing rate of internet access around the world. Thus,
the issue faced when generating new ideas is that thegoal itself could be uncertain or ill-defined and even

2 RELATED RESEARCH
We introduce previous research relative to abductive
reasoning, Kotofactory, and Kotoframe in this section.
The former one includes related studies by otherauthors, and latter ones are the previous research of theauthors.

Figure 1: Abductive reasoning model ofprevious research

2.1 Abductive Reasoning
4 http://www.ikea.com/

Abductive reasoning is utilized in the context of
discovery, thus using existing knowledge and given

5 http://www.coca-cola.com/global/glp.html

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Open Journal of Information Systems (OJIS), Volume 2, Issue 2, 2015

diffused with various types of noise, which makes it
difficult to understand what is intrinsic to the company
in terms of its attractiveness in the market. The data
marketplace would create a similar situation whereparticipants need to create hypotheses, rules, and goals
as well, which needs to be rather innovative.

2.3 Kotoframe
QFD, developed by Akao and others [3], simplifies
concept design by deploying identified functions interms of product traits such as durability,
maintainability, and/or adaptability to the market. QFD
supports efforts to determine design specifications
along with offering a mechanism to ensure conformity
with market requirements. DQFD [12, 13], known here
as Kotoframe, has been developed as a tool for concept
design to support players (or designers) to solve
simultaneously rising problems where the solution to
one will impact the solution to the other. Examples
include market requirements and design restrictions.
This method facilitates decision making in productplanning. For example, the market for electric vehicles
expresses two different needs, namely, longer range(distance) and lower costs at the same time. However,meeting these two market requirements creates conflictbecause a battery system should be heavier to satisfy
the former requirement but lighter to satisfy the latter
requirement. Thus, Kotoframe facilitates the process offiguring out how to reconcile such conflicts to arrive atsolutions.
Once the solution is resolved by means of trial anderror, it shall be examined and refined in light ofmarket requirements. This in turn generates ideas to be
improved and/or elaborated upon further, by utilizing
Kotofactory again. Hence, the proposed model could
be operated in several rounds and in a type of double
helical model [9]. The Kotoframe’s final target is toembody Kotofactory’s output into tangible forms as
concept designs in a product planning process. The
relationship between Kotofactory and Kotoframe isdiscussed in Section 5.

2.2 Kotofactory
Kotofactory is a web-based game that serves as anempirical environment, which has been developed tostimulate players’ abductive reasoning and generate
new product ideas [5]. It is called a “game” expecting
psychologically lower players’ level of entry to web
based experiment in comparison with something calleda test or experiment because those would lead players
to expect that their natural cognitive process will be
judged. A game has two elements:
 Enjoying while playing it online, free fromlimitations of space and time, and Ill-defined goals, as in SimCity 6 , which is notabout winning or losing.
Kotofactory is an empirical environment for testing
cognitive and hypothetical reasoning simply byshowing two different products from different
industries along with their components consisting ofseveral pieces. This method was chosen because in realbusiness fields, employees developing new productideas tend to concentrate on their own products fromtheir own industry, avoiding ideas from different
products in different industries.
For example, experts on automotive engine sensor
product development would ignore the needs of sensors
in the entertainment industry such as in products
similar to Nintendo’s Wii 7 . Therefore, Kotofactory
introduces the most simplified method to incorporate
two different products in an empirical trial. These ideas
can be considered at an initial stage for concept designin product planning in order to overcome stereotypicalthinking and thus improve a firm’s chance ofdifferentiating itself from others.
By introducing Kotofactory in the concept of
abductive reasoning, the path from being given
unstructured data to forming the goal can be visualized
and reproduced using the game’s play log with
sufficient details to enable analysis on how hypotheses
and rules as well as the goal are composed. The methodof using Kotofactory is detailed in Section 3.

6
7

3 METHOD
In this section we introduce empirical environment anddata on the experiment.

3.1 Empirical Environment
A web-based process called Kotofactory has beendeveloped to study how framed components are
generated by combining different products, which isconsidered useful when designing a data marketplace [5]. As shown in Figure 2, on the left side of eachcomponent for a given product, we can see threeelements, which are way to use, function, andmaterial/infrastructure given in different colors. A
different product is shown in the same manner on theright side. Accordingly, product A’s function could beapplied to product B as its way to use, whereas product

http://www.simcity.com/
http://www.nintendo.com/wiiu

18

J. Nakamura, M. Teramoto: Concept Design for Creating Essential Hypothesis, Rules, and Goals: Toward a Data Marketplace

combinations of pieces. Moreover, unobserved dataand information as well as tacit rules are indispensable
to the enrichment of inferred ideas. This entails
abductive reasoning by combining given pieces in
addition to one’s imagination and knowledge. It mightoccur in the context of actions in data marketplace as
well. This hypothesis was evaluated by analyzing theplaying logs of the developed web-based tool.

3.2 Experiments
Twenty undergraduate students from the University ofTokyo participated in the experiments; each student
was given two out of the four proposed existingproducts, with their component pieces displayed on thescreen. Students were assigned to groups of two or
three member, and they finished the game within 30
min. The following two products were given at the startof the game. Words in angle brackets were written on
the cards that are pieces of components given on thescreen.

Figure 2: Screen Image to start playing the game
B’s material could be applied to product A’s function,
depending on the case.
Administrators have the ability to edit bilateral
product names, component names, number ofcomponents (maximal 5 elements per product: threecomponents are shown in Figure 2), component colors(to be selected from 10 given colors), and component
pieces (maximal 6 pieces). Players can engage inbuilding combinations and relating cards to a cluster bydragging components from both sides via lines andarrows in the workplace at the center of the screen.
In this way, framed thinking is assumed to generate
new product offerings comprising restructured
components utilizing relevant and selected pieces. A
previous study has demonstrated that such a framedesign workplace facilitates new product ideas byrestructuring pieces of components from different
products [5]. Notes can be made at any time during thegame in order to record created ideas or scenarios. The
space to take notes can be found at the bottom ofscreen, as shown in Figure 3.Kotofactory has been
upgraded so that all log data can be saved
automatically to enable reproduction of all processes
that the players performed during the experiment.
Two additional screens were prepared. One screen,
as depicted in Figure 4, asks players about who thecustomers/markets are, what product ideas are
generated, and why the product is competitive. These
questions are displayed immediately after finishing thegame. Another game feature is shown in Figure 5. At
the end of the game, players are requested to respond toquestions about levels of satisfaction regarding the
product idea generated by the players themselves, andto point out cards that were missing from the screen but
necessary to complete the product idea. This
information is crucial to answer this paper’s firstresearch question.
We hypothesize that generating new product ideas(different from other ideas) are inferred by theunexpected combination resulting from presenting
different products’ components with different

A vending machine consisting of (1) ways to use<pieces: time is money, check new drinks, sell drinks,
exchange money, security, stock for disaster>, (2)function <pieces: maintain humidity, maintain control,
calculation, refrigeration, lighting, air-conditioning,
advertisement,
card
recognition>,and(3)material/infrastructure <pieces: safety, monitoring
network, water bottles, light, electricity, logistics>
A cram school consisting of (1) ways to use <pieces:
improve results, learning, career counseling, maintainmotivation, extend community, practice exams>, (2)function <pieces: correction, stimulate competition,
prepare questions, re-produce text in video, evaluation,knowledgeofeducation>,and(3)material/infrastructure <pieces: text book, lounge,
instructor, DVD, dormitory, inbox questions>.
Given that the above products are not relevant tothe same industry, we cannot expect architecturalcompatibility with the new unexpected product ideas.

4 RESULTS
Throughout the experiments, all players initially
utilized induction rather than deduction to address how
to combine component pieces. Deduction here signifies
a top-down approach entailing intentional efforts toselect relevant cards by proposing hypotheses and rules
at the beginning. Induction signifies a bottom-up
approach using a quick-fix mentality, wherein cards aretentatively categorized without proposing hypotheses
and rules, but by exploring the meaning of batches ofcards as well as of those interrelationships. We foundthat using this type of bottom-up (all players) approach
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Open Journal of Information Systems (OJIS), Volume 2, Issue 2, 2015

Figure 3: Example of a result generated
by the experiment
Figure 4: A screen to explain the outcome
in terms of who, what, and why

to explore possible hypotheses and rules as well as
those goals was able to reveal players’ imagined
pieces, which were not given on the screen.
Let us introduce a case study that highlights theabovementioned phenomenon, regarding the vending
machine and cram school. The players were shown thefactors considered during the experiment as depicted in
Figure 3. The information was found at the bottom of
the screen with the box title of “Story.” The note forthis case included that “players should evaluate rooms
based on their eco-life. This evaluation should be
performed on the basis of the room’s overall design
considering comfort with respect to lighting and airconditioning as well as cost of living with respect toelectricity cost. This web service will offer the best
possible room.”
Following the screen shown in Figure 3, players
were asked to describe their generated ideas in moredetails using three questions: (1) Who/what is thecustomer/market? (2) What product idea is generated?
(3) Why is the product competitive?

Figure 5: Final screen image where several
questions are posed
further improvement of the application. In addition,
players were asked to name the cards that they thought
were critical for generating ideas and therefore should
be added as depicted in Figure 5. In this case, theseincluded “any element in relation to ecolonomics indaily life, e.g., gas and water.”

The respondents’ feedback is as follows as shownin Figure 4.
Who is requesting the product, and who evaluates theroom?: Anyone in the general public is a potential
requester. Experts on “ecolonomic” design and thegeneral public will evaluate the proposed room.

5 NEW MODEL
In this section based on the result from the above work,
we develop a new abductive reasoning model toexpress the outcome of Kotofactory by formalization.
Then, we discuss about how Kotofactory would relatewith Kotoframe. Finally, we explore clue to a data
market place showing entire concept design processwith both Kotofactory and Kotoframe.

What product idea is generated?: One generated idea si
to provide a web-service to compete ranking by scoring
individual rooms in terms of ecolonomics.
Why is the product competitive?: Following recent
ecolonomic trends, many people could be attracted to
web services, resulting in sponsorship from various
companies. Competition with score ranking and prize
promotion may provide motivation to attract morecustomers.

5.1 Abductive Reasoning ModelFirst, the players’ proposals will be analyzed. This
experiment used a web service to create a competitive
ranking of individual rooms in terms of ecolonomics.

At the end of the game, players gave constructive
feedback about the web-based workplace to enable
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J. Nakamura, M. Teramoto: Concept Design for Creating Essential Hypothesis, Rules, and Goals: Toward a Data Marketplace

This web service represents a higher-order concept ofsoft products consisting of the following contextual
clauses where Pn is defined as an appropriate context
that is a constituent part of the entire story:
 Context P1 : Lighting and air-conditioning makes aroom comfortable, and room design creates a goodenvironment.
 Context P2 : By uploading photos of proposed rooms
on the web, the general public can browse throughthem, leading to expectation of revenue fromadvertising.
 Context P3 : Objective evaluations can be made byexperts and the general public, providing
satisfactory confirmation of the results.
 Context P4 : ID cards enable us to collect data onelectricity and appropriate data for a fair evaluation.

Figure 6: Revised abductive reasoning modelWithin the above four contextual clauses, theconcept of a web service emerges from the mixed
given facts, mixed additional data, mixed hypotheses,
and mixed rules. We believe that this complex structure
represents not only a possible situation for providing adata marketplace but also the situation when we
consider a company’s marketing strategy. In this regard,an abductive reasoning model can be developed fromthe one mentioned in Section 2, i.e., in the event that an
observed fact F is given, plausible hypothesis is
generated where A8 causes B9 (B should be as long asA) in the context of the possible rule.
We will incorporate this hypothetical reasoning into
the entire context by trial and error as appropriately as
possible and subsequently determine the most plausible
consolidation of a set of given facts, additional data,tacit memory and knowledge, hypotheses, rules andgoals, thereby resulting in a change in consolidated
hypothetical reasoning. Additional data here include(1) complementary data and information that is not
given initially but is obtained by searching internal andexternal sources, and (2) tacit memory and knowledge
that one develops during the task. By considering themeaning of these consolidations, product ideas are
finally constructed.
The above process can be generalized andformalized, provided that given facts F are observed,
and hypothesis h is generated to explain theobservation such as { P1 , P2 , ･ ･ ･ , Pn }, where therelevant rule r is known. Let us call this initial process
Rule 1, as shown Figure 1. Note that the new fact will

be added on the fact F initially given, depending on
how one will search additional information fromexternal sources such as web or books. Thus, the scopeof F will become larger than the initial F, which isexpressed with a symbol of F’. A proposed idea (ahigher-order hypothesis) would be inferred involving a
set of Pi including respective hypothesis h, a set of ruler(Σr), and a set of given fact F and additional
facts(ΣF′) , where ΣF′ U {Σr, Σh} are satisfied by theincorporated rule R, as shown in Figure 6. The circle’s
size serves to illustrate the scope of context, i.e, A
passed on to B according to Rule 1 and the given fact F,
while X passed on to B’ according to Rule R and theextended fact F’. Here, X contains combined different
contexts.
In other words, once a fact F is given, an idea X,
which incorporates sub-ideas where a set of A is passed
on to a set of B in the context of respective rule r,
implying B’, can be developed by employing aknowledge data management system. This in turn could
be one function of the proposed data marketplace, as
well as abductive reasoning. The abduction here is aprocess of discovery to explain how X is passed on toB’, as shown in the area drawn with diagonal lines in
Figure 6. As shown in Figure 6, the dynamic process ofhuman thought in considering of overall integrity could
be broken down into several contextual clauses
introducing formal logic, which is a rather static
constraint among both rules and facts.
Moreover, the goal C could be shifted during thedesign processes depending on how additional Pi are
generated and how an entire set of ∑ Pi would beconstructed in response to player’s awareness of thegoal. Therefore, the phenomenon here is not a typicalabduction process that fixes the goal from the
beginning. Thus, in a hypothetical context, collective
rules and additional data are all incorporated on thebasis of subsets and tacit knowledge, so as to identify
the final goal C.

5.2 Formalizing the Outcome
8 A means a well-formed formula such as p(f(a)) which

Returning to the previous four contextual clauses of thecase { P1 , P2 , P3 , P4 }, it could formally be described in
the following manner:contains predicate p, function f, invariable a (  D).
9 Same formula as A, but different meaning.

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Open Journal of Information Systems (OJIS), Volume 2, Issue 2, 2015

Context P1 : Lighting and air-conditioning make a room
comfortable, and room design creates a goodenvironment.

satisfied with the rule r1 , r2 , the given Fact andhypothesis h.

Set = { r1 : Making a room comfortable→ creating a good environment,

Context P4 : ID cards enable us to collect data onelectricity along with other appropriate data for a fairevaluation.

r2 : Making a room comfortable

Set = { r1 : Appropriate data → fair evaluation,→ investing money}
Fact = {Lighting and air-conditioning→ being comfortable}
Hypothesis: Lighting and air-conditioning as roomdesign.

r2 : Appropriate data → depending on data}
F = {Using ID card → data such as electricity could becollected properly}
Hypothesis: Identifying individuals by using an ID card.

Consequently, F’ U { ri , h} creates a goodenvironment, which means that we take hypothesis hwhere “create a good environment” is satisfied with theConsequently, F’ U { r1 , r2 , h} yields a fairevaluation depending on data. This means that we takehypothesis h where “fair evaluation depending on data”

rule r1 , the given Fact, and hypothesis h.

is satisfied with the rule r1 , r2 , the given Fact andhypothesis h.

Context P2 : By uploading photos of the proposedrooms on the web, the general public can browse
through them, leading to the expectation of revenue
from advertising.

As an overall outcome, with a subset of hypotheses
and rules such as ∑ Pi , the hypothesis X states that
players evaluate rooms in terms of their eco-life, where
the rule is that the evaluation is performed on the basisof the room’s design considering 1) comfort withrespect to lighting, 2) comfort with respect air
conditioning, and 3) cost of living along withelectricity cost. The final goal C is a web service tooffer ranking by scoring individual rooms in terms of
ecolonomics. These are in conformity with the revised
abductive reasoning model in Figure 6.
The idea of a product comprises several contextual
clauses sufficiently different to infer a higher-ordercontext or service-offering scenario. Thus, the model
corresponds to the first research question on how abusiness goal would be formulated using a hypothesis
and rules.

Set = { r1 : Many people browsing through photos of the
proposed room on the web →expectation of revenue from advertising,r2 : Many people browse through photos of the
proposed room on the web →wonder about the effect of advertising due tonot knowing exactly how many people viewed it}
Fact = {Propose uploading photos of the room on the
web → many people browse through them}
Hypothesis: Propose uploading photos of the room onto
the web.
Consequently, F’ U { ri , h} expects revenue fromadvertising, which means that we take hypothesis hwhere “expectation of revenue from advertising” issatisfied with Rule r1, the given Fact and hypothesis h.

5.3 Alignment with Kotoframe

Context P3 : Objective evaluations can be made byexperts and the general public, providing satisfactory
confirmation of the results.

As explained in Section 2, Kotoframe is a framework
to support possible convergence from diversified ideas,
and could function as a second phase of a data
marketplace to address those ideas embodied withtangible forms. It reveals the conflict between market
requirements and design specifications that should be
resolved in the process of concept design, prior to
manufacture, a proof of concept.

Set = { r1 : Objective evaluation →satisfactory result,r2 : Objective evaluation →depending upon evaluators}
Fact = {Evaluation of rooms by experts and the generalpublic → objective evaluation}
Hypothesis: Rooms should be evaluated by experts andthe general public.

Kotoframe is described in the dotted-line boxes asshown in Figure 7. Kotoframe consists of three factors:
performance, quality characteristic, and design
specification. The factors in brackets, which are related
to Kotofactory, correspond to performance, market
requirement, and design specification.

Consequently, a satisfactory result for F’ U { r1 , r2 ,h} depends on evaluators. This means that hypothesis hwhere “satisfactory result depending on evaluators” is22

J. Nakamura, M. Teramoto: Concept Design for Creating Essential Hypothesis, Rules, and Goals: Toward a Data Marketplaceissue of how to design distributed computing with aservice orientation could be considered, such as options
involving a cloud application and/or virtualization for
visitors to view huge numbers of uploaded photos (asan additional infrastructure) with lower cost to enableeasier comparisons.
Figure 7 shows a conflict between parameters
within Kotoframe, which is expected to address theissue. With reference to the revised abductive
reasoning model shown in Figure 6, the shaded area isa delta to enlarge the context in consideration of F’
instead of F, which is activated by the need to address
the identified issue. Therefore, after generating ahypothesis, rules, and a goal through first step of using
the data marketplace, the outcomes shall be formed by
Kotoframe. However, Kotoframe might raise a conflictin the solution, and this means it is necessary to returnto the first step of the data marketplace so as to further
refine the previously generated ideas as would happen
in a double helical model [9].
Therefore, Kotoframe supports efforts to findconflicts, knowledge of which is rather valuable input
to consider when undertaking to implement the ideas
derived from phase 1 of the data marketplace. This
responds to the second research question: how thegenerated ideas will be embodied into tangible forms as
concept design.

Figure 7: Kotoframe drawn with dotted lines
Performance is a parameter that customers expect
as given before deciding to invest in/procure theproduct. The quality characteristic is a condition of therelevant market related to usage and the circumstancecondition, such as maneuvering, time to use,
temperature to be maintained, customer emotions, etc.
The design specification is about the product’s systemarchitecture, which consists of dimension, weight,
energy to drive, temperature, CPU, etc.
In the aforementioned case, the generated ideas
should be considered in light of the three specified
factors of Kotoframe, i.e., market requirement, quality
characteristic, and design specification. An example ofsuch ideas is given as follows, where the categories inbrackets are the frame of Kotofactory, and theperspectives with italics are the frame of Kotoframe: How the eco-life is evaluated by experts andgeneral public to score ranking is the performance
perspective (way to use);

5.4 As a Clue to a Data Marketplace
In this section, we discuss how to relate Kotofactoryand Kotoframe to each other and how these two tools
should be considered in relation to the data marketplace.
This study proposes that the data marketplace could
consist of two phases: (1) generating new ideas fromgiven data as explained with Kotofactory and (2)forming those ideas into embodied concept designs asmentioned in relation to Kotoframe.
The two phases are needed because players in thetwo phases are different in terms of knowledge andexpertise, or even roles and responsibilities, inparticular, in a company’s in-house concept design
process. For example, the company has different
departments, such as product planning, productdevelopment, manufacturing, sales and marketing,where different experts are indispensable.
In this regard, Kotofactory is so straightforward toforce participants to create their own hypothesis, rules,
and goals by combining pieces of different products on
the workspace, which includes nothing at the
beginning. The effect of framed components has
already been discussed in [5]. However, Kotofactory isable to record all actions, reproduce the user’s process,and change the data/components/ products shown on
the screen for various types of case studies. These How individual rooms could be viewed on a webbased environment considering the number of
uploaded photos of the proposed room is the quality
characteristic perspective (function); How the web-based architecture should be designed
in terms of operational databases, storage, interface,
and feeds from/to internal applications, are issues
covered under the design specification perspective(infrastructure).
From the case study introduced in the previous
section, a conflict could possibly arise between a userfriendly interface and a possibly huge number of
uploaded photos. The former element is a quality
characteristic in terms of viewing the individual rooms
on the web, whereas the latter one is a design
specification on how many photos of the rooms to be
uploaded so that experts and the general public canview sufficiently and efficiently to evaluate rooms in ashort period of time.
Once the possibility of such a conflict is identified,
it can be solved with possible new technology ormechanism (such as an additional function for
efficiently searching graphic data). Alternatively, the23Open Journal of Information Systems (OJIS), Volume 2, Issue 2, 2015

functions enable researchers to simulate how
hypotheses, rules, and goals are generated, resulting inan ability to analyze possible abductive reasoning.
When considering a data marketplace, it isimportant to remember that Kotofactory is not designed
for the data marketplace itself but as an empiricalenvironment to analyze participants’ cognitive
processes. The data written on the card is not expected
to be used in the data marketplace but rather
empirically by a high-level agent for a stated limited
purpose. Thus, the issue of whether the card data
should be open/revealed or close/sanitized in terms ofthe data marketplace is beyond the scope of this study,
despite the fact that this question is very important toconsider when designing the data marketplace.
When it comes to Kotoframe, the following threefactors should be discussed:from the noun and verb, as shown in Figure 8 [8].
The measurable adjective is, for example,“weighted” something, which can be identified interms of a specific unit of measurement such as
tons, kg, or meters, while the unmeasurable
adjective is an element such as a “premium” design.
For this unmeasurable element, the designer shallprepare some drawings to be evaluated by therequester or potential customer as a form ofsensitivity analysis.
 The design specification needs feedback or input
from persons having sufficient relevant productexpertise to abstract a mechanism to get products towork.
Thus, the two steps are proposed on the basis of thesame platform for consistency. We propose that thedata marketplace contains two phases as shown in
Figure 9, to form an overall concept design process,where both Kotofactory and Kotoframe are interlinked.
The concept design idea shall be returned to
Kotofactory for further refinement from Kotoframe toresolve conflict and/or to reflect the voice of customers.

 The market requirement is clarified in line with who
the customers are, how the product can be used,
what and what product performance the customers
expect.
 The quality characteristic is identified bygrammatically breaking down the requirement into
an adjective, adverb, noun, and verb. For example,
the phrase “correct time to show it accurately” canbe split to adjective (correct) and noun (time),
which together mean correct time, while the adverb(accurately) and verb (show) together mean
accurately show. The quality here can be identified
from adjective and adverb, which might be alsotranslated into a measurable or un-measurable
indicator, while the function here can be identified

Figure 8: Relation between quality and function
with regard to QFD [8]Figure 9: Entire concept design process with Kotofactory and Kotoframe in relation to data marketplace24

J. Nakamura, M. Teramoto: Concept Design for Creating Essential Hypothesis, Rules, and Goals: Toward a Data Marketplace

Innovations in the past have shown that competitive
strategies derived from analyses of objective data are
not always successful [7]. Therefore, abductive
reasoning with plausible logic is expected in the use
not only with objective data (or facts) but also withlatent data, enriched to improve the probability ofinnovative success. The knowledge used to innovate
emerges from indirectly stimulating a person’s mind to
identify compatibility and other relationships between
sets of data, as detailed in previous sections and in
Figure 6.
We believe abductive inferences are stimulated by
the limited given data set and components even as if
the constraint stimulates creative ideas [1] with support
from the data marketplace, which has two phases as
depicted in Figure 9. We also believe an alignment of
Kotofactory and Kotoframe can be considered as an
input to the proposed design data marketplace, where
players interact with the unstructured data provided togenerate new ideas. These ideas are also expected to befurther developed as hard or soft products throughKotoframe.

K. Hori, “Creativity Support, Theory andapplication of AI Approach toward Creativity”(in Japanese), Chiyoda-ku, Tokyo, Ohmusha,
2007.

[3]

S. Mizuno and Y. Akao, “Quality Function
Deployment” (in Japanese), Minato-ku, Tokyo,
Union of Japanese Scientists and Engineers, 1978.

[4] Y. Moon, “Different: Escaping the Competitive”,
Herd. New York, NY, Crown Business, 2011.
[5] J. Nakamura, “Frame as a clue to intention of
data: toward new product ideas with framedcomponents”, International Conference on Data
Mining Workshops, pp. 720-725, Dallas, TX,
December 7, 2013.
[6] J. Nakamura, H. Kido, and Y. Ohsawa, “Dynamic
Capability to Formulate Business Strategy in
Abduction”, Annual Conference in 2014 forInternational Academy of Strategic Management,
pp. 69-72, Bunkyo-ku, Tokyo, September 13,
2014.
[7] I. Nonaka and A. Katsumi, “Rules of Behavior for
Innovation” (in Japanese), Chiyoda-ku, Tokyo,
Nihonkeizai Shinbunsha, 2009.

6 CONCLUSIONSThis paper introduces abductive reasoning model that
produces hypothesis, rules, and goals, by referring to
the case study utilizing an empirical environment
called Kotofactory. As a blueprint for building a data
marketplace, we have discussed about the entire
concept design process associating Kotofactory with
Kotoframe, i.e., from idea generation to addressing
those ideas embodied with tangible forms through theuse of dynamic quality function deployment.
Kotofactory and Kotoframe enable simple
framework formation, which supports reconfigurationof ideas for use in a design data marketplace. Morecollective evaluation of case studies as well as
designing the actual data marketplace, however, will be
the subjects of future work.

[8] T. Ohfuji, “QFD: A method to enable securing
quality from product planning phase” (inJapanese), Minato-ku, Tokyo, Japanese Society
for Quality Control, 2010.
[9] Y. Ohsawa and Y. Nara, “Decision Process
Modeling across Internet and Real World byDouble Helical Model of Chance Discovery”,
New Generation Computing, vol. 21, pp. 109-121,
2003.
[10] C. S. Peirce, “Philosophical Writings of Peirce”,
New York, NY. Dover Publications, 1955.
[11] M. E. Porter, “Competitive Strategy”, New York,NY, Free Press, 1980.
[12] M. Teramoto and J. Nakamura, “e-Trucks realize
four Zeroes expectations–The challenge byMarket of Data”, International Conference on
Data Mining Workshops, pp. 726–732, Dallas,
TX, December 7, 2013.

ACKNOWLEDGEMENTS
This study was supported by Japan Science andTechnology Agency (JST), and Core Research for
Evolutionary Science and Technology (CREST).

[13] M. Teramoto and J. Nakamura, “Adaptability of
Dynamic QFD to concept design of electrictruck”, International Conference on Quality’14Tokyo, Shinjuku-ku, Tokyo, October21, 2014.

REFERENCES
[1]

[2]

R. A. Finke, T. B. Ward, and S. M. Smith,“Creative Cognition, Theory, Research andApplication”, Cambridge, MA, The MIT Press,
1992.

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Open Journal of Information Systems (OJIS), Volume 2, Issue 2, 2015

AUTHOR BIOGRAPHIES
Dr. Jun Nakamura is a visiting
professor at Research Center forSocial and Industrial Management
Systems, Kanazawa Institute ofTechnology. The areas of his
research are innovation management
and cognitive science.

Masahiko
Teramoto
isaConsultant of Electric Vehicle
Technology. He worked in Nissan
Motor Col., Ltd. as a senior
engineer, and in Volvo Group
Trucks Technology as a seniorchief engineer. He is the specialistof Electric Vehicle Development.

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