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Mass Customization: On-line Consumer Involvement in Product Design

Narges Kamali and Suzanne Loker
Cornell University, US

Abstract

Using a channel theory framework, this experimental study investigated the on-line involvement of consumers in product design, a mass customization approach. Three treatments varying the level of participants' design involvement in the design of a T-shirt were developed to simulate a Web-based retail environment. Results established an overall interest in design involvement, some support for higher levels of design involvement in shopping for apparel, and higher satisfaction with a Web site's navigation and usability as involvement increased. The study demonstrated that the Internet provides an acceptable interactive platform and distribution channel for consumer design involvement and should be considered by e-tailers. Additional research directions are recommended.

Introduction

Internet retail sales has been lauded as a new distribution channel for retailers to capture consumer sales through additional retail Web sites, new Internet consumers, and increased purchases by current Internet consumers. Interactive and e-commerce sales grew 12.1% in 2001 to $31.4 billion and are expected to reach $81.1 billion in 2006 (Anonymous, 2002). Apparel and accessories accounted for $1.3 billion of these sales, the third highest consumer product category sold via the Internet behind computer hardware ($2.3 billlion) and office supplies ($1.5 billion) (Greenspan, 2002).

The interactive nature of the Internet is a key attraction to building a relationship with customers (Hoffman, Novak & Chatterjee, 1995). On-line retailers incorporate features to take advantage of the Internet's two-way communication affordances such as customer service, e-mail inquiries to sales representatives, discussion forums for customers, and voice and video applications. Electronic shopping has many of the same characteristics as traditional storefront shopping and catalog shopping, such as organization of the product by department and browsing or "window shopping" possibilities (Spiller & Lohse, 1998).

Mass customization, the involvement of the customer in the design, production, or delivery process before the actual sales transactions, using technology to limit the cost, is another strategy with which businesses are experimenting. Using advanced information and production technologies, mass customization requires the customer and business to develop the product or service together so as to provide customers with exactly the product they want at the time they want it (Davis, 1987; Pine, 1993). On-line applications offer customers products, including clothing, that are made with their inputs, and provide businesses with a competitive edge (Anonymous, 2000). Evaluation of consumer interest in and satisfaction with the mass customization process and its resulting product will determine the success or failure of the business venture.

Research investigating on-line purchasing, the communication as well as the consumer behavior variables, is needed as the Internet medium advances. The purpose of the research reported here was to evaluate the level of consumer interest in and satisfaction with on-line consumer involvement in the process of designing a clothing product.

Channel Theory

Channel theory explains the use of communication, transaction, and distribution channels by consumers and businesses or between two businesses (Peterson, Balasubramanizan, & Bronnenberg,1997). Communication channels facilitate information flow, transaction channels facilitate agreement for exchange, and distribution channels facilitate the actual exchange of goods or services. Kotler (1997) applied channel theory to describe the nine functions of marketing activities (information, promotion, negotiation, ordering, financing, risk-taking, physical possession, payment, and transfer of ownership) conducted through these channels.

Li, Kuo, and Russell (1999) applied channel theory to develop and test a conceptual model for consumer on-line buying behavior. The study assumed that a consumer chose a channel high in communication, distribution, and accessibility attributes. Consumers who made on-line purchases considered themselves knowledgeable about the Internet's communication ability, understanding how to access the Internet readily and to purchase products using this transaction channel. The study's findings indicated that education, convenience, experiential orientation, channel knowledge, perceived distribution utility, and perceived accessibility were predictors of the on-line buying status (i.e. frequent, occasional, or non on-line buyer) of Internet users. Also, consumers who made frequent on-line purchases were more interested in the convenience abilities of the Internet than those who made occasional on-line purchases. The frequent on-line consumer has a lower experiential orientation (the ability to touch, see, or feel the product on-line) than the occasional on-line consumer, not needing to engage these senses before purchase. Li et al.'s conceptual model explaining on-line consumer behavior provided the framework to study consumer involvement in product design on the Internet.

Mass Customization

Davis (1987) and Pine (1993) conceptualized mass customization as a business strategy that involved customers in the development process of a product or service in order to address individual needs. The goal was to provide business customers and consumers with a differentiated, low-cost product with input from the customer and efficient production and delivery using information and manufacturing technology. Duray (1997; Duray, Ward, Milligan & Berry, 2000) empirically tested the concept of mass customization using three levels of validations: case studies of 15 companies using mass customization, plant visits and interviews in the furniture industry, and a survey of 639 companies in the furniture, fabricated metal products, machinery, electric and electronic equipment, transportation equipment, and instruments industries. She used point of customer involvement and modularity in four production phases—design fabrication, assembly and use—to classify the firms into four categories: fabricators, involvers, modularizers and assemblers. Results confirmed that point of customer involvement and modularity differentiated the firms on process choice, planning technique, technology use, and business performance variables. Furthermore, Duray (2000) argued that mass customization at the product design stage could integrate the marketing, manufacturing, and engineering functional areas.

We applied Duray's typology to clothing production using the customer involvement dimension and extended it from pattern, design, and assembly customization through production planning, delivery, and post-purchase customization (see Figure 1). These six categories of customer involvement in mass customization can be applied separately or in combination to achieve a competitive advantage for a clothing business. For example, creating patterns based on unique measurements of an individual using computer-assisted design involves the consumer or business customer in size or style decisions before the actual production of a garment. Forecasting involves business customers in the process of predicting the future needs of individual consumers or groups of customers.



Figure 1. Apparel Mass Customization Model.

Involvement in design addresses individual needs, often based on fashion, individual preferences, or business niche when the product is clothing. Component choice is one approach to mass customization at the design stage that is based on the concept of modularity (Duray, 1997; Pine, 1993) and is of particular interest to businesses. Firms could define a number of options for consumers or business customers to choose from that greatly increased the possible combinations and perception of unique design. For example, offering ten colors, two silhouettes, and two style features such as pockets to the customer to mix and match extended the possible options to 40 (i.e. 10 x 2 x 2= 40).

There is limited empirical research on mass customization and its acceptance by consumers due to the emerging nature of the strategy. Kotha (1995) demonstrated the advantages of matching mass customization and mass production strategies to maximize the competitiveness of a manufacturing business using a case study of a bike firm. Anderson, Brannon, Ulrich, and Marshall (1997) used focus groups to identify four contexts in which consumers were interested in participating in the design of clothing—copying clothing currently owned or "clothes clones," totally custom, co-designing with a trained person, and selecting from a set of options or component choice. Fiore, Lee, Kunz, and Campbell (2001) found high interest in mass customization after describing body scanning and the co-design process to subjects. Their results associated the preferred level of stimulation with the types of products, services, and experiences desired from mass customization of apparel. They also found that consumers preferred to participate in mass customization of products (i.e., jeans, swim suits), product features (i.e., fit and size) to a greater degree and color and garment details to a lesser degree (Lee, Kunz, Fiore & Campbell, 2001). Huffman and Kahn (1998) evaluated consumer ability and interest in selecting among extensive product choices. They concluded that consumers were more satisfied with selecting attributes within a choice set than having extensive or few choices. These results suggested a strategic potential for mass customization, but did not analyze the level of design involvement in which consumers were willing to participate.

Our own search for Web sites offering mass customized items uncovered many sites offering consumer involvement in design for a variety of products, including clothing, and a variety of levels of customization. Computers (dell.com and gateway.com), cars (landrover.com), diamond rings (adiamondisforever.com), greeting cards (hallmark.com), cosmetics (exface.com), and rugs (smithandnoble.com) are some examples. Clothing sites offering mass customization varied from selecting pants length and fit (llbean.com) and color and style (Nike.com) to selecting components such as fabric and style features (IC3D.com). There were several T-shirt sites that offered a small choice of T-shirt styles to which custom text and images selected from those provided or uploaded from consumer's files could be added (e.g., t-shirts.com and 99dogs.com).

Internet Shopping

Neilson (1999) identified characteristics that were important to the design and usability of a Web site and determined whether a purchase would be made, including product description, price comparisons, detailed information about the vendor, convenience, and ease of use. Neilson concluded that convenience and ease of use were the main reasons people bought on the Internet and secondary reasons were the large selection of products and pricing. Shoppers only bought 5% of the time they visited a site, and the other 95% of visits were for product research, comparison shopping, and other "non-buying tasks". Respondents wanted detailed information about the product itself when buying a product on the Web, and also looked for price comparison information and detailed information about the vendor. Li et al. (1999) found that consumers shopped on-line for reasons other than obtaining products, such as role-playing, diversion from the routine of daily life, self-satisfaction, learning about new trends, physical activity, sensory stimulation, social experiences outside the home, communication with others having a similar interest, peer group attraction, status and authority, and the pleasure of bargaining.

Clothing products are one category of goods that have been identified as appropriate for Internet sales (Silverman, 1999). Apparel e-commerce sales reached $878 million in 1999 (Ross, 2001). Lee and Johnson (1999) found that clothing consumers who purchased on the Internet were more likely to perceive shopping on the Internet as having advantages, such as bargains and a greater number of clothing choices. They also perceived retailers on the Internet as having better customer service compared to stores. They were more likely to make on-line purchases with a credit card if the retailer was perceived to be reliable, based on its brand name or an association with a well-known store. Moss and Brannon (1999) evaluated the preferences of college students for three existing Web sites selling clothing. The subjects evaluated the site with fashion-forward clothing highest, the one with hand painted T-shirts next, and the one with mass customized options on less sophisticated, more casual clothing as third. They concluded that Web-site and product design were as important to the consumer as the interactivity provided by design involvement.

Recently, new opportunities for customer involvement on apparel Web sites have expanded from catalog-like offerings to interactive offerings (Yamada, 2001). Several sites (e.g., IC3D.com and t-shirt.com) offer customers design involvement through selection of design features, colors, or fabrics. Lands' End addressed the customers' reluctance to purchase clothing without trying it on by using on-line virtual models. The 3-D model can be adapted to resemble a customer's body shape and then dressed with clothing of interest to that customer. 3-D visualizations that more closely resemble customers by incorporating their photos, as well as size prediction and the ability to try on one size smaller and one size larger, will soon be available for on-line clothing shoppers (Abend, 2001).

Early research on Internet shopping (Spiller & Lohse, 1998) classified Internet retail stores into five categories: superstore, promotional store, plain sales catalog, one-page catalog, and product listing. The extraordinary evolution experienced by business-to-consumer (B2C) Internet sites in the past several years called for a revised classification scheme that included additional consumer services, such as design involvement, personal shoppers, and information of interest to the target customer.

Internet shopping for clothing was definitely increasing and the medium provided the interactivity necessary to involve consumers in the design process. But we wondered whether consumers even wanted to be involved. To find out, we used an experimental design with three customization treatment levels to evaluate interest in component choice mass customization. Component choice was defined as the process that allowed customers to participate in the design process by choosing an individualized combination of product style, fabric, color, and size from a finite set of options (Anderson et. al., 1997).

Hypotheses

We identified three hypotheses for testing:

H1. Intent to purchase will be greater for participants in the limited customization treatment than in the control group (current retail option) and will be greater in advanced customization than in either control or limited customization treatments.

H2. Satisfaction with the customization process will be greater for participants in the limited customization treatment than in the control group (current retail option) and will be greater in advanced customization than in either control or limited customization treatments.

H3. Satisfaction with the Web site interface will be greater for participants in the limited customization treatment than in the control group and will be greater in the advanced customization treatment than in either the control group or limited customization treatments.

Methods

Sample Selection

The sample was drawn from female university students, 18-25 years in age, who had bought a clothing item on the Internet in the past two years. It was necessary to focus on either women or men due to the gendered nature of clothing, and women were chosen as they were the primary household shoppers in America (Verdisco, 1999). Gay's (personal communication, June 2001) research indicated that college women's greatest on-line distraction was shopping, supporting the choice of college-age women as participants in this research.

A random sample of 600 female Cornell students was drawn by the university registrar. The number was based on earlier experience with e-mail survey response rates and level of Internet shopping for clothing. These potential participants were e-mailed twice within a one-week interval. Unfortunately, only ten women volunteered and completed the experiment from this e-mail request. Additional participants were solicited from classes and campus organizations. Seventy-two participants completed the experiment, 24 in each of three treatment groups.

Treatments

Participants were randomly assigned to three experimental treatment groups in order of arrival at the experimental location. They viewed the mock Web site on computers with broadband connections. Three levels of interactive design involvement were implemented on a mock Web site, T-shirt.com, developed specifically for this research. Treatment one, the control, offered five ready-to-wear garments that had three pre-designed variables: style, color, and graphic images. The treatment simulated a customer's involvement at traditional retail stores or Web sites. Treatment two, limited customization, simulated custom T-shirt shops where customers had a limited choice of components. Participants were able to mix and match from components of style (2), color (5), and graphic images (5) making a total of 50 possible combinations (i.e., 2 X 5 X 5). Treatment group three, advanced customization, offered the greatest amount of design involvement. Participants chose from five neckline options, five sleeve options, 20 color options, three bodice lengths, five graphic images, and five placements/sizes or from 37,500 possible combinations (i.e. 5 x 5 x 20 x 3 x 5 x 5 = 37,500).

Participants in the control group selected one T-shirt from five potential flat drawings illustrated on the Web site, while participants in the limited and advanced customization treatment groups were able to combine a variety of design components and view them using the interactivity of the Web site. A drawing was modified as the subject chose design features by clicking on various components. The process was similar to several commercial Web sites (e.g., IC3D.com). Screen captures of the three treatments appear below in Figure 2 (a)(b)(c).



Figure 2(a). Control Condition.



Figure 2(b). Limited Customization Treatment.



Figure 2 (c). Advanced Customization Treatment.



Measures

The dependent variables were selected to test the differences between treatment groups based on Li et al.'s conceptual model of on-line behavior: 1) intent to purchase, 2) satisfaction with co-design customization process and outcome, 3) and satisfaction with the Web site interface. Intent to purchase was operationalized as the dollar amount subjects were willing to spend on the T-shirt product and their expressed likelihood of purchasing the product. Nine-point Likert items and scales were used to measure satisfaction with involvement in the design process, including anticipation, customization process, and future involvement compared with traditional retail shopping. Fiore et al.'s (2001) definition of co-design was adapted for this study to describe component choice, a specific case of co-design, and presented to the subjects, "Co-design is the process that a customer follows to choose an individualized combination of product style, fabric, color, and size from a finite group of options." Fiore et al.'s (2001) survey questions were adapted to the Internet context and additional co-design customization satisfaction questions were developed to measure this variable. Satisfaction with the appeal, navigation, and usability of the Web site were measured using three, nine-point Likert scales adapted from Jones, Rieger, Treadwell, and Gay (2000).

Demographic and technology use questions were posed to confirm that the random assignment of participants in treatment groups resulted in no significant differences among participants on the variables identified by Li et al. (1999) to explain on-line buying behavior, including education, channel knowledge, accessibility, and experiential orientation. Education was controlled in the selection process. Income was measured using annual amount spent on clothing as a proxy, given that the subjects were all students. Five-point categorical scales measured knowledge of the Internet based on amount of computer and Internet use. The frequency of shopping for apparel on the Internet, in traditional retail stores, and from mail order catalogs was measured on four-point categorical scales.

A pilot test was conducted with students who had completed courses related to clothing design and had a high level of computer competence. These students did not participate in the ultimate research experiment. Revisions to the Web site and the written instrument were made based on suggestions from pilot subjects.

Analyses used ANOVA, Kruskal Wallis, Chi-square, and cross tab statistical tests to test for differences between the means of the three treatment groups depending upon the categorical or interval level of responses. Post hoc Tukey tests were used to determine which treatment groups were significantly different.

Results

Differences among Treatment Groups

There were no significant differences between the three treatment groups in the annual dollar amount spent on apparel, in shopping habits, or in level of computer use, except for the level of Internet use for communication with others. The clothing expenditure means ranged from $996-1059 with a total sample mean of $1027 (F = .03, p < .05). While there were no significant differences between treatment groups on their frequency of clothing purchases on the Internet ( Χ2 = 2.56, p < .05), in a traditional retail store ( Χ2 = .87, p < .05), or from a mail order catalog ( Χ2 = 4.81, p < .05), the levels of use differed slightly across the three venues. Retail store shopping was most frequent, ranging from 1.83 to 2.00 (1=a few times a year, 2=every month, 3= every week, 4=other) across the groups for a total sample average of 1.94, closest to the "every month" response. Mail order catalog shopping frequencies were slightly lower, ranging from 1.54 to 2.33 for a 1.82 total sample average, while Internet shopping was least frequent at 1.71 with a range of 1.50-1.96.

Overall computer use for the sample was 4.43 (1=never, 2=occasionally, 3=weekly, 4=daily, 5=hourly) and e-mail use was similar at 4.42. Internet use was slightly less at 4.22. These results established that the overall level of computer use in the sample was very high. The total sample used the Internet the most for information seeking (3.65) and communication with others (3.50), indicating a less than daily but more than weekly use. All other Internet uses were only occasional at 2.29 for shopping and file sharing, 2.15 for downloading software, 2.04 for travel, and 2.00 for business. There were no significant differences between groups except for the frequency of communicating with others using the Internet. The control group was significantly more likely to communicate with others on the Internet than the advanced customization group.

Intent to Purchase

There were no significant differences between groups on the four questions about the intent to purchase the T-shirt produced during the experiment (see Table 1). However, it was interesting that there was a very high overall willingness to purchase the T-shirt product from the Web site at 88%. The advanced customization group had the highest interest in purchasing the T-shirt at 91% while 83% of the limited customization group and 88% of the control group expressed an interest in purchase.

 

Control

Limited

Advance

Total

Significance

 

M

M

M

M

M

Willing to Purchase

88% (Yes)

83% (Yes)

81% (Yes)

88% (Yes)

χ2 = .72

Willing to Pay

$16.60

$15.74

$16.89

$16.43

F = .32

Price Range

3.36

3.17

3.25

3.26

χ2 = .33

Pay More for

7.08

6.75

6.58

6.81

F = .71

Note. Price range: 1 = $5-10, 2 = $11-15, 3 = $16-20, 4 = $21-25, 5 = $26-30, 6 = $41-45, 7 = $46-50, 8 = $51-above.
Pay more for co-design measured on a 9-point scale: 1 = disagree strongly and 9 = agree strongly.
*p< .05

Table 1. Questions Measuring Intent to Purchase.

The average price participants were willing to pay ranged from $15.54 for the limited customization treatment group to $16.89 for the advanced customization treatment group, with an overall mean of $16.34. This was corroborated by asking the participants to respond in price ranges, resulting in a total sample average response of 3.26, nearest the $16-20 category. When asked about willingness to pay more for customized T-shirts, participants responded affirmatively. Responses varied from 6.58 for the advanced customization group to 7.08 for the control group for an overall 6.81 response on a 9-point Likert scale where 9 was strongly agree, 5 neutral, and 1 strongly disagree. Although these results were not in the predicted direction of differences among groups, the differences were not significant.

Satisfaction with Mass Customization

Satisfaction with the customization process was measured with eleven items (see Table 2), seven that were adapted from Fiore et al.'s (2001) survey used with participants who had not actually participated in a mass customization process. In that study, the process of co-design, or component choice, was described and participants were asked about their interest in spending time and money on the process. We called this the anticipation scale. Four additional items were developed to measure satisfaction with the mass customization process after actually experiencing involvement in design during the experimental procedures. Care was taken to word the questions to be compatible with all three treatments, such as "chosen T-shirt" rather than "designed T-shirt." Two of these items focused on the satisfaction with the process and the resulting product. The other two asked participants to compare their likely future involvement in designing a T-shirt and selecting a T-shirt that was already designed on a Web-site. All responses were on a nine-point scale, where 1 = strongly disagree and 9 = strongly agree. The scales were tested for reliability using the Cronbach's alpha and all were above the accepted .70 level.

Anticipation Scale

1. I would be willing to pay more than usual for a co-designed clothing product.
2. It is important that the clothing industry have a co-designed process for customized clothing products.
3. I would be likely to use co-design in clothing products using a computer and the Internet.
4. I view a co-design process with clothing on the Internet as an exciting experience.
5. I would be very interested in using co-design to select from groups of clothing design options on the Internet.
6. I would be very interested in using co-design to create my own unique clothing design on the Internet.
7. I would be willing to spend more time, compared to the time spend purchasing stock clothing, to create a co-designed clothing product on the Internet.
Reliability coefficient = .86

Satisfaction with Customization Scale

8. I enjoyed the process used on this Web site.
9. I am satisfied with the T-shirt product that I chose on the Web site.
Reliability coefficient = .70

Future Involvement with Customization Scale

10. I would rather choose my own components of a T-shirt on a Web site than to buy one that has been pre-chosen for me.
11. I would rather shop on a co-design T-shirt Web site than shop on a traditional T-shirt Web site or T-shirt store.
Reliability coefficient = .81
Table 2. Satisfaction with Customization Scales.

Results are presented in Table 3. No significant differences were found between the three treatment groups on the anticipation and future involvement with customization scales. A significant difference was found between groups for the satisfaction with customization scale (F = 5.84, p < .05). A post-hoc Tukey test found the significant differences between the control group and the other two treatment groups, limited and advanced customization. There was no significant difference between the limited and advanced customization treatment groups.

Scale

Control

Limited

Advanced

Total

F

 

M

M

M

M

 

Anticipation

7.23

7.24

7.49

7.32

.46

Satisfaction

6.60a

7.19b

7.67b

7.16

5.84*

Future Involvement

7.15

7.15

7.13

7.13

0.00

Note. Judgments were made on a 9-point scale: 1 = disagree strongly and 9 = agree strongly. Means in the same row that do not share superscripts differ at p < .05 in the Tukey honestly significant difference comparison.
*p <= .05

Table 3. Satisfaction with Mass Customization.

These results partially supported hypothesis two that satisfaction increased with the customization process and resulting product as customization was introduced. The overall scores were high on all scales, averaging 7.13-7.32 on nine-point Likert scales with 5 being neutral, indicating a general interest in design involvement. In addition, the results were in the expected direction in the anticipation and satisfaction with customization scales—the advanced customization treatment group was most satisfied and the limited treatment group was more satisfied than the control group.

Participants were also asked to select the categories that best represented how much time they were willing to spend on design involvement and how long they were willing to wait for a mass customized garment. There were no significant differences between the treatment groups on either response ( Χ2 = 1.75, p < .095 and Χ2 = .70, p < .05, respectively). The sample mean was 2.85 (2=up to 15 minutes, 3=up to 30 minutes) on the amount of time willing to spend on design involvement and 4.88 (4=up to 1 week, 5=up to 2 weeks) on the amount of time willing to wait for a customized garment.

Finally, high interest in design involvement and the mass customization process was expressed specific to eight clothing product features-neckline, shirt length, sleeve length, printed image, color, fiber content, overall style, and fit. Responses were on a nine-point Likert scale with 9 as very interested and 5 as neutral. The overall means for the features ranged from 7.51-8.29 with group mean responses ranging from 7.08-8.42. There were no significant differences between treatment groups. The highest overall interest in mass customization applications were expressed for fit (M=8.29), color (M=8.21), and style features (M=8.07). The least interest was in fiber content (M=7.42).

Satisfaction with Web Site Interface

The final hypothesis, that satisfaction with the appeal, navigation, and usability of the Web site increased as design involvement increased, was tested using three scales adapted from Jones et al. (2000) that measured satisfaction with the appeal, navigation, and usability of the Web site (see Table 4). Cronbach's alpha coefficients established reliability at .88, .91, and .94, respectively.

All three treatment groups indicated a high level of satisfaction with the Web site interface, with total mean scores of 7.42 on appeal, 7.57 on navigation, and 7.31 on usability, on scales where five was neutral and nine was very satisfied. The mean responses were in the predicted direction for all three scales, with the advanced customization treatment group responding most positively, the limited customization treatment group second, and the control group least positively.

Scale

Control

Limited

Advanced

Total

F

 

M

M

M

M

 

Appeal

7.25

7.40

7.61

7.42

.43

Navigation

6.85a

7.79b

8.08b

7.57

6.75*

Usability

6.36a

7.58b

7.99b

7.31

8.85*

Note. Judgments were made on a 9-point scale: 1 = disagree strongly and 9 = agree strongly. Means in the same row that do not share superscripts differ at p < .05 in the Tukey honestly significant difference comparison.
*p <= .05

Table 4. Satisfaction with the Web Site Interface.

No significant differences were found between the treatment groups on the appeal scale. As the Web site treatments were designed to maximize similarity with only one page unique to each treatment, no difference was expected. There were significant differences found between the treatment groups on the scales measuring the satisfaction with navigation and usability of the Web site interface. Post hoc Tukey tests indicated that in both scales, the significant differences were between the control group and the limited and advanced customization treatment groups. There were no significant differences between the limited and advanced customization groups. The results partially supported hypothesis three in that as participant involvement through customization opportunities was offered, the satisfaction with the Web site's navigation and usability increased.

Internet Shopping Preferences

Participants were asked why they liked to purchase clothing on the Internet. Eighty-six percent of participants said that they used the Internet to purchase clothing because it was convenient, including specific remarks such as "shipped to your door." Over half of the sample thought Internet shopping was easier than shopping at a mall or by mail order. Approximately 35% of the sample noted variety of selection and the ability to get what they were looking for as reasons to shop for clothing on the Internet. Experiences exclusive to the Internet, such as fun, relaxation, and special sales and promotions specific to the Web site, motivated 24% of the participants to purchase clothing on the Internet. Specific types of clothing that participants preferred to co-design and then purchase on the Internet included bottom and tops (both 50%), design features such as style, color, and fit, (35%), and shoes (22%).

Discussion

The results of this research suggest high consumer satisfaction with design involvement in a Web-based mass customized process. Participants not only indicated satisfaction with the process and Web site interface, but also expressed very high levels of intent to purchase the products they designed. The experimental design of this study compared the responses of those who had experienced thousands of choices of design features with those who had experienced some design choice and those who had only experienced the traditional retail choices of completed garments. The fact that all three treatment groups expressed positive interest in the process, the Web site interface, and the purchase of the resulting garment, even when not actually experiencing it, indicates potential for greater consumer involvement in the design of clothing and other consumer products on the Internet. Although not significant, the lower response levels of the advanced customization group than the control and limited customization groups on willingness to pay more for and interest in future involvement in mass customization need to be further investigated. Participants' interest and experience in design are two variables that could explain this directional result.

The product type may make a difference in the acceptance and willingness to pay more for the mass customization of a consumer product. Subjects were willing to purchase the customized T-shirt but only willing to pay an average of $16-20. It may be that consumers have a set limit on the amount they will pay for particular clothing items, especially product types such as T-shirts that they possess in multiple numbers. However, design involvement afforded by mass customization may add symbolic meaning to the product for consumers, while the manufacture and delivery of the product may cost more. Therefore, it is important to evaluate which product types are likely to be worth more to the consumer if customized. T-shirts, for example, appear to have a value limit at about $16-20 for college students. Higher priced products and products with more practical or symbolic value, such as clothing for work and wedding dresses, may offer the highest competitive advantage for involving the consumer in the design of the product.

Increasing the number of design feature options for a mass customized product may not make a difference in the satisfaction with the mass customization process. The non-significant differences between the limited and advanced customization treatment groups in satisfaction with the process and the Web site in this study supports the concept of threshold introduced by Huffman and Kahn (1999). Participants seemed as satisfied with 50 design feature choices as with 37,500. Future research should address the concept and level of threshold in the involvement of consumers in on-line design of clothing and other consumer products.

The risk of not seeing, feeling, and trying on clothing before purchase may be the greatest challenge for Internet clothing sales, and is an issue that must be addressed in both mass customized or traditional retail assortments. Perhaps product type makes a difference. For example, the research reported here used the T-shirt product, for which consumers were willing to pay $16-20 and which provided a lower risk than tailored suits at hundreds of dollars or wedding dresses with life-long symbolic meaning. Perhaps fit makes a difference. Snugly fitted garments may be less likely to be purchased without trying them on. Or, perhaps fabric feel makes a significant difference in whether a consumer will purchase a garment or not. If a garment were touched and appreciated for its smooth, silky texture, its purchase might be more likely than when viewed on-line without direct seeing or touching it. Accommodations for lack of touch, sight, and trying-on should be investigated. Virtual 3-D try-on technology might reduce the risk of ill-fitting or inappropriately styled clothing for one's body type, by providing the consumer with a view of the garment on his/her body. Prototype fabrics might be provided by mail. Retail outlets may provide actual fabric and garment viewing in combination with interactive Internet offerings such as the opportunity for consumers to design a unique garment by choosing among design features. Research on these enabling technologies and their consumer acceptance is needed.

The significant increase of satisfaction with the navigability and usability of the Web site interface as design involvement was introduced leads to the conclusion that Web site design is very important in promoting the on-line involvement of consumers in clothing design. Li et al. (1999) pointed out the importance of channel knowledge including computer and Internet literacy. Controlling for the level of channel knowledge and use, our results found that the increased interactivity provided by design involvement in on-line purchasing motivated consumers to purchase. Further research is needed which focuses on the relationship between level of interactivity and satisfaction with navigation and usability of the Web site interface for the purchase of clothing and other consumer products .

Participants in this study indicated that Internet shopping for clothing was easy, convenient, and likely to satisfy their requests with no stock-outs. These results support the potential of a competitive niche for selling mass customized clothing over the Internet. Involving consumers in the design of clothing or other consumer products through component choice provides them with the added benefits of individualization that may be worth a price premium. The manufacturer offers more selection through component choices while reaping added profit paid by consumers for ease, convenience, perfect match to request, and uniqueness based on design involvement. This mass customization process has great potential for the apparel and consumer products industries to increase both communication and transactions with consumers. It could complement mass production activities of manufacturing firms as suggested by Kotha (1995) or stand alone for firms exploiting a customization niche.

In summary, mass customization strategies that promote design involvement for the consumer are an attractive approach for Web-based businesses. This study demonstrated that consumers' satisfaction increased as interactivity based on design involvement was offered. Involving consumers in product design using Internet technology affords potential competitive advantage for businesses and should be explored and exploited.

Acknowledgments

This research was supported in part by a Cornell University College of Human Ecology grant and the Cornell University Agricultural Experiment Station federal formula funds, Project No. NYC-329404 received from Cooperative State Research, Education, and Extension Service, U.S. Department of Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.

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About the Authors

Narges Kamali completed her master's degree in the Department of Textiles and Apparel at Cornell University in August 2001. She is a product development trainee at the Federated Department Stores in New York City.

Suzanne Loker is the J. Thomas Clark Professor of Entrepreneurship and Personal Enterprise in the Department of Textiles and Apparel at Cornell University. She specializes in research on the apparel industry, including the management of information and production technology, mass customization, and team based approaches. In addition, she is developing a Web-based electronic textbook, Designers as Entrepreneurs, which will be used for an undergraduate course and in outreach education to the apparel industry. Her research has been published in a number of journals and as chapters in books. She received her Ph.D. degree from Kansas State University and has also served as a faculty member at the University of Idaho, University of Vermont, Kansas State University, Washington State University and Queens College of CUNY.
Address: Department of Textiles and Apparel (TXA), 326 Martha Van Rensselaer Hall (MVR), Cornell University, Ithaca, NY 14853-4401. Phone 607-255-6204 FAX: 607-255-1093.

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