MMRCDISCUSSION PAPER SERIES
No. 457

The Multidimensionality of Design Newness:
An Empirical Survey of Product Appearance and Preference
Atsushi Akiike
Graduate School of Economics, the University of Tokyo
Sotaro Katsumata
Faculty of Economics, Nagasaki University
May 2014

東京大学ものづくり経営研究センター
Manufacturing Management Research Center (MMRC)
Discussion papers are in draft form distributed for purposes of comment and discussion. Contact the author for
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http://merc.e.u-tokyo.ac.jp/mmrc/dp/index.html

The Multidimensionality of Design Newness: An Empirical
Survey of Product Appearance and Preference
Atsushi Akiike1
Graduate School of Economics, the University of Tokyo2
Research Fellow (DC2) of the Japan Society for the Promotion of Science

Sotaro Katsumata
Faculty of Economics, Nagasaki University3

Abstract:
In this study, we examined the relationship between perceptual images and consumer
preferences in product appearance. Specifically, we focused on the image of “design
newness” and its multidimensionality. Previous studies have proposed that there ispositive relationship between design newness and product performance measures suchas sales. In other words, consumers prefer products that they perceive as “new.”
However, there are some attributes or constructs of newness, for example, “novelty,”“originality,” “uniqueness,” and “atypical.” Previous studies rarely examine thedifferences in these newness attributes. Therefore, in this study, we compared thesenewness constructs and examined their relationships to performance measures. We
surveyed consumers using a questionnaire in which hypothetical product pictures were
offered as visual stimuli to represent each of the four newness attributes listed above, as
well as product preferences. From the data analysis, we found that there were
substantial differences among these four attributes. Since the correlations among these
attributes were not high, we could not adopt them as measurements of a single“newness” construct. We observed the multidimensionality in the “design newness”
image. Moreover, from the statistical analysis used to explain product preference using
product images, we determined that each newness attribute had a different effect. For
example, “novelty” positively affects preference, while the “uniqueness” has a negative
effect. Our findings implied that we should recognize the multidimensionality of “design
newness” images. Although previous studies supported the relationship between
newness and performance, we determined that not all of the newness images positively
affected consumers’ evaluations. Companies should carefully design the product to be“novel” rather than “unique.”Key words: innovation, design innovation, design newness, differentiation strategy

1Both authors contributed equally to the work and are listed alphabetically.
Doctoral Student and Research Fellow (DC2) of the Japan Society for the Promotion of Science.
3
Associate Professor.
21

1. Introduction
Product design has a substantial impact on consumer choice and company
performance. In the recent years, many studies have focused on this issue and presented
various findings (e.g., Gemser and Leenders, 2001; Hertenstein, Platt, and Veryzer,
2005; Verganti, 2009; Utterback et al. 2006). In particular, many studies suggested that
product design affects consumers’ perception (Bloch, 1995; Veryser, 1999). Talke(2009) proposed elements of “design newness” from previous studies. According to thestudy, there are four elements of newness including originality, novelty, uniqueness, andatypically. In addition, design newness positively affects product sales. Subsequentstudies supported the relationship (Radford and Bloch, 2011; Truong et al., 2013).
However, according to the results of this study, the design newness constructconsists of more than two sub-constructs. Furthermore, from the results of the statisticalanalysis, some elements have a negative impact on product preference. “Novelty”
encouraged consumer preference, while the impact of “originality,” “uniqueness,” and“atypically” did not have a positive effect. “Uniqueness” especially had a negative
impact on preference. We also found that the “novelty” was related to technical images
and positively evaluated by many consumers. On the other hand, “unique” products
were not always preferred by consumers. We noted that companies and researchers
should consider the differences between each “newness” image attribute.
In this paper, we will first review the studies examining the relationshipbetween product appearance and consumer evaluation. Additionally, we will present
previous studies related to the elements of the design newness and the impact on
outcomes. Based on this discussion, we will introduce the hypotheses for this study,
followed by the survey research designs and empirical results. Finally, we will discuss
the results attained and propose future topics.

2. Previous Studies on Design Newness
Many existing studies focused on the effect of the product appearance on the
consumer evaluations. These studies pointed to the relationship between the productappearance and consumption (e.g., Folkes and Matta, 2004) and brand images (e.g.,
Orth and Malkewitz, 2008; van Rompay and Pruyn, 2011). In addition, productappearance positively affects consumers’ brand choice (e.g., Yamamoto and Lambart,1994).
Based on these findings, recent studies have focused on “design newness” as afactor affecting consumer evaluations. Talke et al. (2009) proposed a construct of“design newness” from an empirical survey on the German vehicle market. Talke et al.
(2009) also presented the attributes (or sub-constructs) of the “design newness”
constructs from previous studies: “originality,” “novelty,” “uniqueness,” and “atypically.”
The construct “originality” was proposed by Runco and Charles (1993) as itcorresponded to the inverse construct of “appropriateness.” This study discussed how“originality” affects the creativity. The “novelty” construct is proposed by Hekkert,
Snelders, and van Wieringen (2003) with its corresponding inverse construct “typicality.”
2The “uniqueness” construct is discussed by Bloch (1995) as a construct of the source ofdifferentiation. Loken and Ward (1990) examined the nature of “typically,” and foundthat correlated with family resemblance and attribute structure.
As Talke et al. (2009) supported the hypothesis that design newness positively
affects sales, other related findings were reported in consequent studies. These results
implied that the design newness has a positive impact on consumer behaviors andcompany performance (Radford and Bloch, 2011; Truong et al., 2013). Based on thesestudies, we expected that the “new” design would produce positive outcomes forcompanies. However, some studies doubt this seemingly general conclusion. For
example, “typical” products are sometimes preferred by consumers (Veryzer andHutchinson, 1998; Ward and Loken, 1988; Landwehr, Wentzel, and Herrmann 2013).
Consumers may evaluate a product’s “new” design, while preferring a product that issimilar to existing, ordinary products.
Moreover, consumers gather many impressions of a product’s appearance aside
from its newness, including perceiving a product as “high tech” and “user friendly”(Hoegg and Alba, 2003; Creusen, and Schoormans, 2005; Muggie and Schoormans,
2011, 2012). These other images also affect consumer preferences. Therefore, we haveto examine the interrelationships between design newness and other perceptual images.
Table 1 lists the product images focused on in this study and related studies.
To summarize, in this study we propose the following two hypotheses. In thenext section, we will explain the research design and show the empirical results.
H1: The “design newness” construct consists of four attributes: “novelty,”“originality,” “uniqueness,” and “atypical.”H2: Design newness positively affects consumer preferences.

Table 1. Product Images

DesignNewness

Items

Related Studies

Novel

Hekkert, Snelders, and van Wieringen (2003), Heng andCheng (2012), Talke et al. (2009)

Original

Runco and Chearles (1993), Talke et al (2009)

UniqueBloch (1995), Heng and Cheng (2012)4, Talke, et al. (2009)

Ward and Loken (1988), Loken and Ward (1990), Talke etal. (2009)
Creusen and Schoormans (2005), Muggie and Schoormans
High Technology(2011)
Other Design Fits the image of the Landwehr et al. (2013), Ward and Loken (1988), Loken
Imagesproduct
and Ward (1990)
Creusen and Schoormans (2005), Muggie and Schoormans
User friendly(2011)
Atypical4

Adopt the item “uniqueness” as a measurement item of the “novel.”3

3. Research Design
3.1. Objective Products
The purpose of this study is to examine the impact of the perceptual design
image on preference. To uncover this relationship, we conducted our research bysurveying consumers through a questionnaire. In this section, we will present the survey
design and items.
We chose the smartphone as our objective product for two reasons. The firstreason is the high adoption rate in the market. According to the Ministry of Internal
Affairs and Communications (2013), the adoption rate of the mobile terminal (including
ordinary mobile phones and smartphones) is close to 100% in Japan. While the overall
adoption rate of the smartphone is roughly 60%, recent survey reports indicate thatnearly 90% of younger people have smartphones (Hakuhodo Inc. 2014). Therefore, we
are able to obtain enough samples from the survey research. The second reason isrelated to the product lifecycle. As the adoption rate increases, smartphone technologies
are rapidly maturing. Therefore, in the recent market, consumers rarely evaluate thefunctional aspects of the alternatives. Thus, they tend to choose smartphones based on
appearance. As noted by Eisenman (2013) and Walsh (1996), we expect that theimportance of the product design will increase as the product matures. In fact, according
to the Impress R & D Internet media sogo kenkyujo (2013), when a consumer chooses a
new mobile phone, 44.3% of them consider the design appearance. This is the second
highest of 34 criteria, with battery efficiency (52.6%) being first. Smartphones arefeasible objects for observing preference based on appearance. In the questionnaire, we
prepared four types of hypothetical smartphones as visual stimulus to elicit preferences
and images.
3.2. Visual Stimulus
Many previous studies used a pictorial example as visual stimulus of the productto gather preferences or other perceptual images (Orth and Malkewitz, 2008; Randwehr etal. 2013). Based on these studies, we asked respondents to share their impressions on the
products presented as visual stimuli. However, attaining measurements through visual
stimuli had its shortcomings. Our purpose was to extract general tendencies in therelationship between preferences and perceptual images. Evaluations and perceptions
based on appearances often varied among consumers. Some consumers evaluated a
product design as “smart,” while other consumers did not always evaluate it as such,even if they viewed the same product at the same time.
To address this issue, we prepared four different hypothetical product picturesand solicited the perceptual images. The prepared visual stimuli are shown in Figure 1. In
the analysis, we aggregate these data and discuss general results. In the following
section, we will explain the data management in detail. Visual stimulus (A) is theclamshell-type smartphone that resembles the most frequently found feature phone
design in Japan. Stimuli (B) and (D) resemble recognized smartphones such as Experia
and iPhone. Stimulus (C) is the slider-type smartphone that is the least common designin this grouping.
In the questionnaire, we asked respondents to rate their preference using a
7-point scale for all four visual stimuli examples. In addition, we added the following
4

statement that all of the examples from (A) to (D) are smartphones and have the samefeatures including screen size, weight, size of battery, camera resolution, and operating
system. This was to declare that these examples were the same except in appearance.

Figure 1. Smartphone Visual Stimulus

3.3. Perceptual Images
As previously mentioned, we solicited perceptual images for each visual
stimulus (hypothetical pictures). We listed seven perceptual images in Table 1. In thisstudy, all items are measured using a binomial scale, the example fits the image or not.
For one visual stimulus, we measured whether respondents felt that the appearance ofthe product fit these images. Note that the term “atypical” is replaced with “typical” inthe questionnaire to be easy-to-answer.

4. Results
4.1. Data Collection
The survey was conducted on April 28, 2014, with students from the Business
Administration class at Nagasaki University. There were 342 samples, excluding any
inappropriate or missing samples. There were 214 males (62.6%) and 128 females(37.4%). The average age of the students was 19.9. In addition, 340 of 342 respondents(99.4%) owned at least one mobile phone. There were 14 respondents who had morethan two mobile phones.
Table 2 shows the results of perceptual images. The numbers indicated the rateof respondents who replied that the stimulus fit the images. For example, 8.8% of the
respondents felt visual stimulus (A) was “novel.” On the other hand, stimulus (A)received the highest rating for “typical,” followed by visual stimuli (B) and (D).
5

Stimulus (C) rated extremely low. Stimulus (A) was similar to the most recognizedfeature phone in Japan. Since the clamshell-type is the dominant design for featurephones, many consumers evaluated stimuli (A) as “typical.” However, theclamshell-type is not prominent among smartphones. Therefore, consumers did notevaluate (A) as “novel,” and “fits the image of smartphone.” We found that visual
stimulus (C) was the “newest” design among all. However, in other perceptual images,
visual stimuli (B) and (D) were ranked the highest and second highest.

Table 2. Summary Statistics of Differences
(A)
(B)
(C)
(D)
Novel
0.363
0.088
0.371
0.371
Original
0.193
0.181
0.143
0.556
Unique
0.284
0.120
0.105
0.567
Typical
0.430
0.330
0.064
0.503
High Technology
0.351
0.459
0.070
0.544
Fit the image of "Smartphone"
0.050
0.635
0.026
0.702
User friendly
0.228
0.076
0.506
0.506
Note: Numbers in bold font indicate the highest, italic font indicates the lowest among fourtypes.

4.2. Dimension of the Design Newness
Before the statistical analysis, we examined the dimensions of the design
newness construct. At first, we calculated Cronbach’s alpha coefficients from four
design newness items. The coefficients of (A), (B), (C), and (D) are 0.63, 0.26, 0.53,
and 0.35, respectively. Note that the “typical” item is reversed. All of the coefficientsare below the necessary level. We could not obtain one “newness” construct, whichmeans that the items are heterogeneous. Therefore, we cannot support H1, whichassumes the one-dimensionality of the design newness construct.
In this study, we defined the aggregated dataset to analyze the generalcharacteristics. Let X be a matrix of the perceptual images of visual stimulus (A). The
size of the matrix is 342 times 7 (number of respondents times dimensions of images).
The aggregated dataset X is obtained from X
X ,X ,X ,X
where the prime
denotes transpose. Since matrix X is obtained by the alignment of four matrices, thesize of X is 1368 times 7. The aggregated matrix contains the general information setof the perceptions, which is not influenced by certain visual stimulus. Hereafter, we will
analyze the dataset. As in the above, we calculated the Cronbach’s alpha coefficientfrom the aggregated matrix. The coefficient is 0.55. This implies that there is substantial
multi-dimensionality in the newness items. Therefore, from the aggregated matrix, we
also cannot support H1.
Table 3 shows the correlation matrix of the perceptual images obtained fromthe aggregated matrix. Note that “typical” is not reversed. We found that the correlations
among newness items are not high. It is appropriate to incorporate these items as
different explanatory variables to the regression model for preference. In the next
section, we will assess the model that assumes multi-dimensionality of the design
6newness items.

Table 3. Correlation Matrix
Novel

Fit the image
HighofOriginal Unique TypicalTechnology"Smartphone"

1.00
0.29*
0.15*
-0.11*
0.35*

1.00
0.52*
1.00
-0.20* -0.15*
0.11*
0.01

1.00
0.04

1.00

0.23*

-0.12* -0.19*

0.17*

0.40*

1.00

0.13* -0.10* -0.17*Note) *: significant at 5% level

0.22*

0.28*

0.44*

Novel
Original
UniqueTypicalHigh Technology
Fits the image of"Smartphone"
User friendly

User
friendly

1.00

From Table 2 and Table 3, we can observe some interesting results. As
mentioned in section 4.2, the visual stimulus (A) is the most “typical” example.
However, (A) is not perceived as “fits the image of the smartphone.” While stimulus (A)is the most famous design of the feature phone, it is rarely found among smartphones.
When a product category market reaches maturity as a result of technicaladvances, sometimes radical innovation will alter the existing product category and themarket competition becomes unclear again (Abnernathy, Clark and Knatrow, 1983;
Abernathy and Clark, 1985). With this change, design images are often renovated. For
example, there are substantial differences between the feature phone and the smartphone,
as examined in this study, or the CRT and LC television. In these transitional markets, it
is possible that the appearance design perceptions are affected by both old and newmarket images. In fact, we can observe the combined result in Table 3. The “novel” and“fits the image of smartphone” attributes show a positive correlation, and the “typical”
and “fits the image” attributes also show a positive correlation. However, “novel” and“typical” are not positively correlated. We have to consider both new and old productcategories when a market is in a state of de-maturity.
There are interesting relationships between “user friendly” and other images.
Although “typical” is the closest image, “novel” also shows a positive correlation.
Companies can design “user friendly” products that combine “typical” and “novel”
images.
4.3. Empirical Results of the Preference Models
In this section, we assess two models to examine the relationship between
preference and perceptual images. The objective variable is the aggregated preference,
which is defined as follows: y
y ,y ,y ,ywhere y denotes a 342
dimensional preference vector of visual stimuli (A). Model 1 estimates the aggregatedpreference based only on the design newness items. Meanwhile, Model 2 incorporates
all of the measured perceptual images. In model 2, other images such as high technology,
7

fitness, and user friendly are added as control variables.
Table 4 shows the summary results from the estimation. At first, from the
Adjusted R-squared, we find that the fitness of model 2, which incorporates otherimages, is better than model 1. This implies that model 2 is a more appropriate model toexplain the preference. If consumers evaluate a smartphone as “high tech,” “fits theimage of smartphone,” or “user friendly” from the visual appearance, the preference ofthe smartphone will increase. Among the newness items, we also found that some are
significant in terms of having a negative effect. Novelty has a positive effect on bothmodels. Therefore, the “novel” appearance products are more preferred. However,“uniqueness” negatively affects the preference. Similarly, “originality” is significantly
negative in model 1. Typicality is rather positive in model 1, while the typicality does
not show a significant affect in model 2.
As a result, we cannot entirely support H2, which assumes the positive
relationship between design newness and preference. However, some attributes like“novel” positively affect the preference. Furthermore, this result reinforces themultidimensionality of the design newness construct. Therefore, we do not support H1
and partially support H2 based on empirical analysis.
Table 4. Summary of Empirical Analysis
Estimate(Intercept)
Novel
Original
UniqueTypical

3.908
1.103
-0.310-0.7740.309

Model 1S.E.
t value

Pr(>|t|)

0.079
0.113
0.137
0.131
0.108

***
***
*
***
**

49.61
9.74
-2.27
-5.90
2.87

High Technology
Fits the image ofSmartphone
User friendly
R2
Adj. R2
Sigma
F
N

0.101
0.099
1.83
38.49
1368

Estimate
3.234
0.384
-0.162-0.402-0.104Model 2S.E.
t value

Pr(>|t|)

0.077
0.107
0.121
0.116
0.097

42.16 ***
3.59 **-1.35-3.45 ***
-1.08

0.414 0.105

3.96 **

1.305 0.110

11.86 ***

0.845 0.105

8.01 ***

0.312
0.310
1.61
88.09
1368

Note) *, **, ***: significant at 5%, 1%, and 0.1% level respectively

5. Discussions and Conclusions
As the results show, we found contradictory relationships between design
newness and preferences. “Novel” has positive impact, while “unique” has a negative
impact. Previous studies assumed that novelty and uniqueness are similar and bothconstructs are elements of a single common construct in “design newness.” However,
our result shows that there are substantial differences among design newness
sub-constructs.
8

In particular, “uniqueness” does not have positive impact on consumer choice.
There are two possibilities as to why the empirical results show negative relationships.
The first reason is related to our prepared visual stimuli. Heng and Cheng (2012)
observed that consumers do not prefer products that are too unique. It is possible thatthe product pictures proposed in this study are too unique and perceived as unattractive.
However, since we aggregated product image datasets in our analysis, the effect of thespecific visual stimuli is reduced to some extent. The second reason is related to thepreference variance. It is possible that although the unique products are heavily
preferred by some consumers, many ordinary consumers avoid unique products; thus,
we cannot observe a positive relationship from a statistical analysis. As Rogers (2003)
classified consumers, we can presume that there are more than two classes ofconsumers: ones that prefer unique products and those who do not. It will be interesting
to examine these consumer segments and behavioral/psychological characteristics. This
reason is also related to the dominant design determination (Abernathy and Utterback,1978; Abenrathy, 1978). In general, the appearances of innovative products that create
new markets or product categories are unique. However, as Rogers’ (2003) diffusion
model defined, these unique products are not accepted by the majority and only
purchased by innovators. It is difficult for companies to decide when a unique productshould be released into the market.
In addition, there are relatively high correlations between “novel,” “fits theimage of smartphone,” and “high tech.” This implies that consumers interpret andevaluate the concept of “novel” if the appearance of the product looks relatively newcompared with its competitors, but not too eccentric. As Muggie and Schoormans(2011) observed, the technical elements and the design newness have a positive
relationship; our study also supports this relationship. Based on this discussion, it ispossible that design newness has more than two sub-constructs. The first construct“novelty” is related to the technical images and is positively perceived by consumers.
On the other hand, the second construct “uniqueness” is related to radical differentiationand does not always affect preference in positive way. Unfortunately, we cannot
determine the exact sub-construct structure from our analysis. Our future objective is tofurther explore the details of design newness sub-construct characteristics. However, ourstudy’s findings indicate that there are more than two different dimensions within the“design newness,” which was not considered by previous studies.
There are some other questions to be answered. For future studies, we will needto expand the product categories and industries to ensure the robustness of the results.
By examining these other cases, we will able to reveal detailed or new constructproperties. In addition, based on these findings, we will need to examine the structure of
the “design newness” construct in detail, define the construct of “design newness,” anddevelop measurement scales. The re-examination of the “newness” definition and scale
development will become a useful research tool for future studies. Although, this study
has some limitations, our paper substantially contributes to findings related to themultidimensionality of “design newness.”

Acknowledgements
This work was supported by JSPS KAKENHI Grant Number 13J10145,
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25780267. The authors would like to thank Junjiro Shintaku (the University of Tokyo) for
his advice.

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