ISSN: 0971-1023 | NMIMS Management Review Volume XXIX April-May 2016 Abstract Should a design manager invest more in improving aesthetics (hedonic benefit) or function (utilitarian benefit)? The answer depends upon the relative consumer preference for hedonic attributes over utilitarian attributes, or vice versa. Therefore, it is important for designers to understand how customers choose between competing products with different levels of hedonic and utilitarian benefits. Choosing a product from a choice set requires customers to make tradeoffs between design attributes such as aesthetics and functionality that make up the product alternatives. This article introduces an experimental design methodology to estimate tradeoff exchange rate between any two product attributes. The proposed method uses a discrete choice experiment, combined with point allocation across the alternatives in the choice set, to measure the preferences of the respondents. This approach combined with Fieller's theorem allows us to obtain information on the respondent's most preferred product alternative as well as information on his or her relative attribute preferences. Keywords: Conjoint analysis; Fieller's theorem; Hedonic attributes; Utilitarian attributes; Pareto optimal choice sets. Aesthetics versus Function: Assessing Relative Customer Preference Ravindra Chitturi Pallavi Chitturi Aesthetics versus Function: Assessing Relative Customer Preference 11
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Aesthetics versus Function: Assessing Relative Customer ... · is between aesthetics and functionality—aesthetics for pleasure and functionality to serve a useful purpose. For example,
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Aesthetics versus Function: Assessing Relative Customer PreferenceAesthetics versus Function: Assessing Relative Customer Preference
14 15
Changes
cities of India, and therefore street
Contents
mall farmers. Majority of the
farmers (82%) borrow less than
Rs 5 lakhs, and 18% borrow
between Rs 5 – 10 lakhs on a
per annum basis. Most farmers
(65.79%) ar
** p < .01 + Reliability coefficie
** p < .01 + Reliability coefficie
References
Table 23: The Results of Mann-Whitney U Test for DOWJONES Index Daily ReturnsDr. Rosy Kalra
Mr. Piyuesh Pandey
STUDY
The product category of cell phones was selected for
this research. Cell phones were chosen because they
are purchased directly by college students, are widely
used, and are highly familiar. Also, they are of such
value that their purchase will require more
deliberation than the purchase of a relatively
inexpensive consumable. Moreover, cell phones can
be clearly defined which allows respondents to make
comparisons among competing profiles. The cell
phone stimuli (photos) were pre-tested for their visual
appeal.
Data Collection. Each task consisted of a Pareto
optimal choice set composed of three (or four)
alternatives which included the no-choice option. Each
alternative was described in terms of two groups of
attributes: aesthetics and functionality. The cell phone
task needed subjects to imagine that their cell phone
broke down and they want to purchase a new one. Cell
phones were described in terms of functional
attributes (network coverage, battery capacity, and
sound clarity) and aesthetic attributes (oyster flip
phone, phone color, program ring tune). For the choice
set composed of four alternatives {-11, 00, 1-1, no
choice}, henceforth called choice set 1, subjects were
instructed to distribute 100 points across the
alternatives so as to reflect their preferences. For the
choice set composed of three alternatives {01, 10, no
choice}, henceforth called choice set 2, subjects were
instructed to distribute 75 points across the
alternatives so as to reflect their preferences.
Photographs were included to portray the hedonic
attributes of the product.
The cover page of the survey stated that the researcher
was interested in understanding how consumers make
purchase decisions. It emphasized that there were no
right or wrong answers in the survey. Page two of the
survey introduced the cell phone purchase task. Page
three contained the cell phone task in a matrix format
with the alternatives (cell phones) representing
columns in the matrix and the attributes (aesthetics
and functionality) representing rows. A no-choice
option was also included as one alternative (see Figure
1). Page four of the survey asked respondents to
indicate the level of importance they would give to cell
phone attributes. Page four also included questions on
the respondent's demographic characteristics such as
age and gender. Data collected was captured on a
spreadsheet.
*To validate the style and attractiveness (hedonic) manipulation, a separate group of twenty subjects was asked to rate eleven cell phone photographs in terms of their attractiveness on a ten-point scale. Three cell phones with average ratings of 3.7, 5.7, and 8 were chosen to represent the low, medium and high levels of the hedonic dimension of a cell phone.
FIGURE 1EXAMPLE OF CELL PHONE TASK
Cell phone A Cell phone B Cell phone C No Cell Phone
Functionality
Network Coverage: 98%
Battery Capacity : 3 days
Sound Clarity : very high
Style & Attractiveness* Oyster flip phone : No Change phone colors: No Program ring tune : No
Functionality
Network Coverage: 95%
Battery Capacity : 2 days
Sound Clarity : high
Style & Attractiveness* Oyster flip phone : No Change phone colors: No Program ring tune : Yes
Functionality
Network Coverage: 92%
Battery Capacity : 1 day
Sound Clarity :medium
Style & Attractiveness* Oyster flip phone : Yes Change phone colors: Yes Program ring tune : Yes
Aesthetics versus Function: Assessing Relative Customer PreferenceAesthetics versus Function: Assessing Relative Customer Preference
16 17
Changes
cities of India, and therefore street
Contents
mall farmers. Majority of the
farmers (82%) borrow less than
Rs 5 lakhs, and 18% borrow
between Rs 5 – 10 lakhs on a
per annum basis. Most farmers
(65.79%) ar
** p < .01 + Reliability coefficie
** p < .01 + Reliability coefficie
References
Table 23: The Results of Mann-Whitney U Test for DOWJONES Index Daily ReturnsDr. Rosy Kalra
Mr. Piyuesh Pandey
STUDY
The product category of cell phones was selected for
this research. Cell phones were chosen because they
are purchased directly by college students, are widely
used, and are highly familiar. Also, they are of such
value that their purchase will require more
deliberation than the purchase of a relatively
inexpensive consumable. Moreover, cell phones can
be clearly defined which allows respondents to make
comparisons among competing profiles. The cell
phone stimuli (photos) were pre-tested for their visual
appeal.
Data Collection. Each task consisted of a Pareto
optimal choice set composed of three (or four)
alternatives which included the no-choice option. Each
alternative was described in terms of two groups of
attributes: aesthetics and functionality. The cell phone
task needed subjects to imagine that their cell phone
broke down and they want to purchase a new one. Cell
phones were described in terms of functional
attributes (network coverage, battery capacity, and
sound clarity) and aesthetic attributes (oyster flip
phone, phone color, program ring tune). For the choice
set composed of four alternatives {-11, 00, 1-1, no
choice}, henceforth called choice set 1, subjects were
instructed to distribute 100 points across the
alternatives so as to reflect their preferences. For the
choice set composed of three alternatives {01, 10, no
choice}, henceforth called choice set 2, subjects were
instructed to distribute 75 points across the
alternatives so as to reflect their preferences.
Photographs were included to portray the hedonic
attributes of the product.
The cover page of the survey stated that the researcher
was interested in understanding how consumers make
purchase decisions. It emphasized that there were no
right or wrong answers in the survey. Page two of the
survey introduced the cell phone purchase task. Page
three contained the cell phone task in a matrix format
with the alternatives (cell phones) representing
columns in the matrix and the attributes (aesthetics
and functionality) representing rows. A no-choice
option was also included as one alternative (see Figure
1). Page four of the survey asked respondents to
indicate the level of importance they would give to cell
phone attributes. Page four also included questions on
the respondent's demographic characteristics such as
age and gender. Data collected was captured on a
spreadsheet.
*To validate the style and attractiveness (hedonic) manipulation, a separate group of twenty subjects was asked to rate eleven cell phone photographs in terms of their attractiveness on a ten-point scale. Three cell phones with average ratings of 3.7, 5.7, and 8 were chosen to represent the low, medium and high levels of the hedonic dimension of a cell phone.
FIGURE 1EXAMPLE OF CELL PHONE TASK
Cell phone A Cell phone B Cell phone C No Cell Phone
Functionality
Network Coverage: 98%
Battery Capacity : 3 days
Sound Clarity : very high
Style & Attractiveness* Oyster flip phone : No Change phone colors: No Program ring tune : No
Functionality
Network Coverage: 95%
Battery Capacity : 2 days
Sound Clarity : high
Style & Attractiveness* Oyster flip phone : No Change phone colors: No Program ring tune : Yes
Functionality
Network Coverage: 92%
Battery Capacity : 1 day
Sound Clarity :medium
Style & Attractiveness* Oyster flip phone : Yes Change phone colors: Yes Program ring tune : Yes
Aesthetics versus Function: Assessing Relative Customer PreferenceAesthetics versus Function: Assessing Relative Customer Preference
18 19
Changes
cities of India, and therefore street
Contents
mall farmers. Majority of the
farmers (82%) borrow less than
Rs 5 lakhs, and 18% borrow
between Rs 5 – 10 lakhs on a
per annum basis. Most farmers
(65.79%) ar
** p < .01 + Reliability coefficie
** p < .01 + Reliability coefficie
References
Table 23: The Results of Mann-Whitney U Test for DOWJONES Index Daily ReturnsDr. Rosy Kalra
Mr. Piyuesh Pandey
example, we test the proposed methodology to
estimate relative customer preference between
aesthetics and functionality for the category of cell
phones. The same methodology can be replicated for
any other two attributes of a product in any product
category.
How does the knowledge of relative customer
preference between any two attributes under
consideration by design engineers and managers
benefit the product development team? A clear and
precise understanding of relative customer preference
between determinant attributes is critical to
optimizing product design leading to a greater
• Adamowicz, W. P., Boxall, M., and Williams, M. (1998). Stated Preference Approaches for Measuring Passive
Use Values: Choice Experiments versus Contingent Valuation, American Journal of Agricultural Economics, 80,
64-75.
• Batra, R., and Ahtola, O. T. (1990). Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes,
Marketing Letters, 2:2, 159-170.
• Batsell, R. R., and Louviere, J. L. (1991). Experimental Choice Analysis, Marketing Letters, 2, 199-214.
• Chitturi, R. (2015a). Design for Affect: A Core Competency for the 21st Century, GfK-Marketing Intelligence
Review, Fall 2015.
• Chitturi, R. (2015b), “Good Aesthetics is Great Business: Do We Know Why?” in The Psychology of Design:
Creating Consumer Appeal, Rajeev Batra, Colleen M. Seifert, and Diann E. Brei, eds. Routledge: Taylor &
Francis Group, Pages 252-262, (September 2015). Based on the invited papers presented at the Psychology of
Design conference at the University of Michigan at Ann Arbor.
• Chitturi, R. (2009). Emotions by design: A consumer perspective. International Journal of Design, 3(2), 7-17.
• Chitturi, R., Raghunathan, R., and Mahajan, V. (2007). Form Versus Function: How the Intensities of Specific
Emotions Evoked in Functional Versus Hedonic Tradeoffs Mediate Product Preferences, Journal of Marketing
Research, 44, 702 - 714.
• Dhar, R. (1997). Consumer Preference for a No-Choice Option, Journal of Consumer Research, 24, 215-231.
• Dhar, R., and Wertenbroch, K. (2000). Consumer Choice Between Hedonic and Utilitarian Goods, Journal of
Marketing Research, 37 (1), 60 – 71.
probability of new product success. Knowledge of a
tradeoff exchange rate allows designers and marketers
to calibrate the relative change in customer preference
obtained by enhancing the level of a product attribute
involved in the tradeoff vis-à-vis the other attribute.
This relative difference in customer preference when
combined with the cost of enhancing these attributes
gives design and marketing managers a basis for
pricing the product. Collectively, the information
allows designers to make optimal attribute selection
decisions, and allows marketing managers to more
accurately manage return on investment (ROI)
associated with a product development project.
References
• Finney, D. J. (1971). Probit Analysis, Cambridge: Cambridge University Press.
• Ghose, S. and Lowengart, O. (2012). Consumer Choice and Preference for Brand Categories, Journal of
Marketing Analytics Vol. 1, 1, 3–17.
• Haider, W., and Ewing, G.O. (1990). A Model of Tourist Choices of Hypothetical Caribbean Destination, Leisure
Sciences, 12, 33-47.
• Johnson, R. M., and Olberts, K. A. (1991). Using Conjoint Analysis in Pricing Studies: Is One Price Variable
Enough? American Marketing Association Advanced Research Techniques Forum Conference Proceedings,
164-173.
• Kamakura, W. A., and Srivastava, R. K. (1984). Predicting Choice Shares Under Conditions of Brand
Interdependence, Journal of Marketing Research, 21, 420-434.
• Koelemeijer, K., and Oppewal, H. (1999). Assessing the Effect of Assortment and Ambience: a Choice
Experimental Approach, Journal of Retailing, 75(3), 319-345.
• Louviere, J. L., and Woodworth, G. (1983). Design and Analysis of Simulated Consumer Choice of Allocation
Experiments: A Method Based on Aggregate Data, Journal of Marketing Research, 20, 350-67.
• Luce, M. F., Bettman, J. R., and Payne, J. W. (2001). Emotional Decisions, in Monographs of the Journal of
Consumer Research, 1, ed. D. R. John, University of Chicago Press, Chicago, IL.
• Luce, R., and Tukey, J. W. (1964). Simultaneous Conjoint Measurement: A New Type of Fundamental
Measurement, Journal of Mathematical Psychology, 1, 1-27.
• Okada, E. M. (2005). Justification Effects on Consumer Choice of Hedonic and Utilitarian Goods, Journal of
Marketing Research, 42 (1), 43 – 53.
• Olson, J. and Jacoby, J. (1973). Cue utilization in the quality perception process. In: M. Venkatesan (ed.) rdProceedings 3 Annual Conference. Chicago, IL: Association of Consumer Research, pp. 167–179.
• Propper, C. (1995). The Disutility of Time Spent on the United Kingdom's National Health Service Waiting Lists,
Journal of Human Resources, 30, 677-700.
• Raghavarao, D., and Wiley, J. B., (1998). Estimating Main Effects with Pareto Optimal Subsets, Australian
Journal of Statistics, 40(4), 425-432.
n• Raghavarao, D., and Zhang, D. (2002). 2 Behavioral Experiments Using Pareto Optimal Choice Sets, Statistica
Sinica, 12, 1085-1092.
• Richardson, P.S., Dick, A.S. and Jain, A.K. (1994). Extrinsic and intrinsic cue effects on perceptions of store
brand quality. Journal of Marketing 48(4): 29–36.
• Wiley, J. B. (1978). Selecting Pareto optimal subsets from multi-attribute alternatives, in Advances in
Consumer Research, V, ed. K. Hunt, Chicago, IL pp. 171-174.
Aesthetics versus Function: Assessing Relative Customer PreferenceAesthetics versus Function: Assessing Relative Customer Preference
20 21
Changes
cities of India, and therefore street
Contents
mall farmers. Majority of the
farmers (82%) borrow less than
Rs 5 lakhs, and 18% borrow
between Rs 5 – 10 lakhs on a
per annum basis. Most farmers
(65.79%) ar
** p < .01 + Reliability coefficie
** p < .01 + Reliability coefficie
References
Table 23: The Results of Mann-Whitney U Test for DOWJONES Index Daily ReturnsDr. Rosy Kalra
Mr. Piyuesh Pandey
example, we test the proposed methodology to
estimate relative customer preference between
aesthetics and functionality for the category of cell
phones. The same methodology can be replicated for
any other two attributes of a product in any product
category.
How does the knowledge of relative customer
preference between any two attributes under
consideration by design engineers and managers
benefit the product development team? A clear and
precise understanding of relative customer preference
between determinant attributes is critical to
optimizing product design leading to a greater
• Adamowicz, W. P., Boxall, M., and Williams, M. (1998). Stated Preference Approaches for Measuring Passive
Use Values: Choice Experiments versus Contingent Valuation, American Journal of Agricultural Economics, 80,
64-75.
• Batra, R., and Ahtola, O. T. (1990). Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes,
Marketing Letters, 2:2, 159-170.
• Batsell, R. R., and Louviere, J. L. (1991). Experimental Choice Analysis, Marketing Letters, 2, 199-214.
• Chitturi, R. (2015a). Design for Affect: A Core Competency for the 21st Century, GfK-Marketing Intelligence
Review, Fall 2015.
• Chitturi, R. (2015b), “Good Aesthetics is Great Business: Do We Know Why?” in The Psychology of Design:
Creating Consumer Appeal, Rajeev Batra, Colleen M. Seifert, and Diann E. Brei, eds. Routledge: Taylor &
Francis Group, Pages 252-262, (September 2015). Based on the invited papers presented at the Psychology of
Design conference at the University of Michigan at Ann Arbor.
• Chitturi, R. (2009). Emotions by design: A consumer perspective. International Journal of Design, 3(2), 7-17.
• Chitturi, R., Raghunathan, R., and Mahajan, V. (2007). Form Versus Function: How the Intensities of Specific
Emotions Evoked in Functional Versus Hedonic Tradeoffs Mediate Product Preferences, Journal of Marketing
Research, 44, 702 - 714.
• Dhar, R. (1997). Consumer Preference for a No-Choice Option, Journal of Consumer Research, 24, 215-231.
• Dhar, R., and Wertenbroch, K. (2000). Consumer Choice Between Hedonic and Utilitarian Goods, Journal of
Marketing Research, 37 (1), 60 – 71.
probability of new product success. Knowledge of a
tradeoff exchange rate allows designers and marketers
to calibrate the relative change in customer preference
obtained by enhancing the level of a product attribute
involved in the tradeoff vis-à-vis the other attribute.
This relative difference in customer preference when
combined with the cost of enhancing these attributes
gives design and marketing managers a basis for
pricing the product. Collectively, the information
allows designers to make optimal attribute selection
decisions, and allows marketing managers to more
accurately manage return on investment (ROI)
associated with a product development project.
References
• Finney, D. J. (1971). Probit Analysis, Cambridge: Cambridge University Press.
• Ghose, S. and Lowengart, O. (2012). Consumer Choice and Preference for Brand Categories, Journal of
Marketing Analytics Vol. 1, 1, 3–17.
• Haider, W., and Ewing, G.O. (1990). A Model of Tourist Choices of Hypothetical Caribbean Destination, Leisure
Sciences, 12, 33-47.
• Johnson, R. M., and Olberts, K. A. (1991). Using Conjoint Analysis in Pricing Studies: Is One Price Variable
Enough? American Marketing Association Advanced Research Techniques Forum Conference Proceedings,
164-173.
• Kamakura, W. A., and Srivastava, R. K. (1984). Predicting Choice Shares Under Conditions of Brand
Interdependence, Journal of Marketing Research, 21, 420-434.
• Koelemeijer, K., and Oppewal, H. (1999). Assessing the Effect of Assortment and Ambience: a Choice
Experimental Approach, Journal of Retailing, 75(3), 319-345.
• Louviere, J. L., and Woodworth, G. (1983). Design and Analysis of Simulated Consumer Choice of Allocation
Experiments: A Method Based on Aggregate Data, Journal of Marketing Research, 20, 350-67.
• Luce, M. F., Bettman, J. R., and Payne, J. W. (2001). Emotional Decisions, in Monographs of the Journal of
Consumer Research, 1, ed. D. R. John, University of Chicago Press, Chicago, IL.
• Luce, R., and Tukey, J. W. (1964). Simultaneous Conjoint Measurement: A New Type of Fundamental
Measurement, Journal of Mathematical Psychology, 1, 1-27.
• Okada, E. M. (2005). Justification Effects on Consumer Choice of Hedonic and Utilitarian Goods, Journal of
Marketing Research, 42 (1), 43 – 53.
• Olson, J. and Jacoby, J. (1973). Cue utilization in the quality perception process. In: M. Venkatesan (ed.) rdProceedings 3 Annual Conference. Chicago, IL: Association of Consumer Research, pp. 167–179.
• Propper, C. (1995). The Disutility of Time Spent on the United Kingdom's National Health Service Waiting Lists,
Journal of Human Resources, 30, 677-700.
• Raghavarao, D., and Wiley, J. B., (1998). Estimating Main Effects with Pareto Optimal Subsets, Australian
Journal of Statistics, 40(4), 425-432.
n• Raghavarao, D., and Zhang, D. (2002). 2 Behavioral Experiments Using Pareto Optimal Choice Sets, Statistica
Sinica, 12, 1085-1092.
• Richardson, P.S., Dick, A.S. and Jain, A.K. (1994). Extrinsic and intrinsic cue effects on perceptions of store
brand quality. Journal of Marketing 48(4): 29–36.
• Wiley, J. B. (1978). Selecting Pareto optimal subsets from multi-attribute alternatives, in Advances in
Consumer Research, V, ed. K. Hunt, Chicago, IL pp. 171-174.