Online Mass Customization and the Customer Experience

Association for Information Systems

AIS Electronic Library (AISeL) AMCIS 2004 Proceedings

Americas Conference on Information Systems (AMCIS)

12-31-2004

Online Mass Customization and the Customer Experience Arnold Kamis Bentley College

Marios Koufaris Baruch College, CUNY

Tziporah Stern CUNY

Follow this and additional works at: http://aisel.aisnet.org/amcis2004 Recommended Citation Kamis, Arnold; Koufaris, Marios; and Stern, Tziporah, "Online Mass Customization and the Customer Experience" (2004). AMCIS 2004 Proceedings. Paper 481. http://aisel.aisnet.org/amcis2004/481

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Kamis et al.

Online Mass Customization and the Customer Experience

Online Mass Customization and the Customer Experience Arnold Kamis Bentley College 405 Smith Center 175 Forest St Waltham, MA 02452 [email protected]

Marios Koufaris Zicklin School of Business Baruch College, CUNY 55 Lexington Ave, Box B11-220 New York, NY 10010 [email protected]

Tziporah Stern Zicklin School of Business Baruch College, CUNY 55 Lexington Ave, Box B11-220 New York, NY 10010 [email protected] ABSTRACT

Today, many companies use the Web to let customers customize their own products. Examples include Dell for computers, NikeID for shoes, and Reflect.com for cosmetics. This study will examine under what circumstances a certain type of mass customization interface is appropriate. We will conduct an experiment to study the effect of end-user factors such as computer playfulness and computer anxiety on the customer experience when using mass customization on the web. We will also study the effect of the number of product options on that experience. Keywords

Mass customization, computer playfulness, computer anxiety, web commerce, online customers. INTRODUCTION

Mass customization (MC) is the ability to manufacture individually customized products and services on a mass production scale without a significant cost increase (Davis, 1987; Pine, 1993). Through the use of flexible, and well-integrated manufacturing processes, companies can tailor their products and services to fit their customers’ individual needs. Today, companies like Dell let customers use the Web to customize their products. Other examples include NikeID (http://www.nikeid.com) where customers can customize their shoes and Reflect.com where customers may customize their own cosmetics. However, not all MC initiatives have been successful. Mycereal.com, by General Mills Inc., allowed customers to create their custom-made cereal but eventually failed due to a high price and the inability of the customers to know how taste combinations would taste. Similarly, Personalblends.com, a web site for customized coffees by Procter & Gamble Co., failed when customers did not understand the questions they were asked and were not willing to pay the higher prices for the coffee (Keenan et al., 2002). One model that may explain some of the failed MC initiatives is the Task-Technology fit model (Goodhue 1998; Dishaw and Strong 1999). It is possible that online customers are hindered by their own traits as computer users and the characteristics of the system they use. Providing the appropriate interface and information to the different types of web users could determine the success of a MC initiative. Another possible reason may be that customers are overwhelmed with the large number of possible versions of the product. Too much choice can actually lead to feelings of confusion, anxiety, and eventually regret (Iyengar and Lepper, 2000). This paper describes research in progress that examines the factors that make online MC successful. Those factors involve individual end-user characteristics of the online customer as well as the number of product choices available. We will

Proceedings of the Tenth Americas Conference on Information Systems, New York, New York, August 2004

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conduct an experiment to determine how factors such as computer playfulness, computer anxiety, and number of product choices change the preferences of customers between three types of mass customization (MC) interfaces. We expect our results to show that providing the online customer with more choices and more powerful tools to select from those choices may not always be the best strategy for an online company. THEORY AND PARTIAL LIST OF HYPOTHESES

Mass customization on the web is usually implemented using three different types of interfaces: a)

Alternative-based: The customer does not actively customize the product but instead is presented with all the available customized options visually and is asked to choose one.

b) Attribute-based: The customer is presented with all the available choices for each customizable attribute of the product. For example, a customer chooses colors of each customizable part of a shoe at NikeID. c)

Question-based: Customers ask the company to customize the product for them based on answers they give to a number of relevant questions. For example, Reflect.com customers answer questions about themselves and their customized fragrance and at the end they are presented with a choice of three customized fragrances.

The impact of online MC on the customer experience can be both cognitive, through cognitive effort reduction, and attitudinal, through a positive shopping experience. The cognitive effort required to select a customized product from a number of alternatives may be greater than when customers can customize each attribute separately. In most cases, the attributes are all presented simultaneously, as is the case with NikeID, where customers can watch the shoe change as they adjust each attribute. Such an attribute-based interface may allow customers to select from a much larger set of alternative versions of the product than if they were choosing from pre-defined alternatives. It also embraces the anchor-and-adjust heuristic that people are known to use (Jacoby, Jaccard et al. 1994; Johar, Jedidi et al. 1997; Venkatesh 2000). The result can be that customers feel that they have made a more optimal final decision and may be more satisfied with that decision (Huffman and Kahn, 1998). However, beyond the cognitive effect of such an interface, it may also have an impact on the attitudinal experience of online customers. The process of involving the customer in the final design of the product is a prototypical example of customer value co-creation that creates more satisfied and more loyal customers (Kambil and Eselius, 2000; Friesen, 2001). By allowing customers to customize their products, companies are engaging them in the creation of product value that is eventually transferred to the customers themselves. In addition, there is a definite element of play in designing one’s own product. This can lead to increased enjoyment and more perceived control by customers, both of which increase customer intention to purchase from and return to the site (Koufaris et al., 2001-2002; Koufaris, 2002). In addition, the willingness of a company to customize its products can increase an on-line customer’s trust in that company (Koufaris and Hampton-Sosa, 2003). However, since using a MC interface requires that the customer interact with a web site, end-user factors are also significant. One such factor is computer playfulness, defined as the tendency of a user to interact with a computer in a spontaneous and playful fashion (Webster and Martocchio, 1992). The attribute-based MC interface involves an element of play by allowing the customer to experiment with the product design. Therefore, we expect that individuals with high computer playfulness will have a more positive experience using the attribute-based interface while those with low playfulness will have a more positive experience using the alternative-based interfaces. H1a: Users with high computer playfulness will show higher (satisfaction with the decision, shopping enjoyment, perceived control) when using the attribute-based MC interface than when using the alternative-based or the question-based one. H1b: Users with low computer playfulness will show higher (satisfaction with the decision, shopping enjoyment, perceived control) when using the alternative-based MC interface than when using the attribute- based or the question-based one. Computer anxiety refers to the tendency of certain users to feel anxious, apprehensive and even fearful when they interact with computer systems (Igbaria and Chakrabarti, 1990). Since the MC interface can only be experienced through extensive interaction with the web site, we expect that customers with high computer anxiety will prefer the alternative-based MC interface which is simpler to use. Keeping everything else constant, online customers with low computer anxiety should prefer the attribute-based MC interface for its flexibility and efficiency. H2a: Users with high computer anxiety will show higher (satisfaction with the decision, shopping enjoyment, perceived control) when using the alternative-based MC interface than when using the attribute-based or the question-based one.


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H2b: Users with low computer anxiety will show higher (satisfaction with the decision, shopping enjoyment, perceived control) when using the attribute-based MC interface than when using the alternative-based or the question-based one. Our study will also test the effect of personal innovativeness in IT (Agarwal and Prasad, 1998), product involvement (McQuarrie and Munson, 1992), and web skills (Koufaris, 2002). We believe that the effect of using one of the three MC interfaces on the customer experience will change as the number of product choices increases. A common assumption with long-time empirical support is that more choices are better than fewer ones (Langer and Rodin, 1976; Deci and Ryan, 1985; Taylor, 1989). Recent empirical research, however, has shown that, in fact, it is sometimes preferable to present customers with fewer choices and that there is such a thing as too much variety. In an experimental study, when presented with six choices, customers were more likely to purchase the product and were happier with their choice than when they were presented with twenty-four or thirty choices (Iyengar and Lepper, 2000). A possible explanation is that the relationship between the decision-making, purchasing, and consumption experiences with a variety of choices follows an inverted U-shape (Desmeules, 2002). Initially, as variety increases, the consumer has a more positive experience fueled by the increase in choices. However, at a certain level of variety, the consumer begins to reach a plateau where an additional choice makes little or no difference to his or her experience. As variety increases, the experience of the consumer starts to decline. One possible explanation for those results is that too many choices bring on confusion and frustration. Another reason may have to do with the customer’s sense of regret. As more choice become available, customers are more likely to use heuristics or rules of thumb to make their decisions (Desmeules, 2002). They move from an optimizing strategy to a satisficing one (Simon, 1957). As a result, customers may increasingly fear that they will regret their choice later on. Sometimes, in order to avoid such regret they will not purchase the product at all. In the context of MC, one option for customers is to allow the company to customize the product for them. This reduces the cognitive effort required in making a decision and may also shift the responsibility or subsequent blame to the company instead of the customer. Therefore, we expect that as the number of available options of the product increases, customers will have a more positive experience with a question-based MC interface. H3: As the number of product choices increases, all customers will show higher (satisfaction with the decision, shopping enjoyment, perceived control, concentration) when using the question-based MC interface than when using the attributebased or the alternative-based one. METHODOLOGY

In order to test our hypotheses we will conduct an experiment. We have created a web site that includes all three types of MC interfaces (alternative, attribute, and question) for two customizable products: a watch and a backpack. Furthermore, we have created three versions of each interface for each product with a different number of total product choices (8, 54, and 150). Therefore, our study has a 2x3x3 factorial design where the dimensions are product type, MC interface type, and number of product choices respectively. After they familiarize themselves with the three interfaces using a dummy product, subjects will be randomly assigned to one MC interface for one of the products and will be asked to customize that product to their liking. To give an incentive to the subjects to customize the product, they will be told they are entered in a drawing where if they are picked, they will win the customized product they selected. After they customize the first product, they will also be asked to customize the second product but this time they will be randomly assigned to a different MC interface with a different number of possible product options. Immediately after they customize each product the subjects will be asked to answer an online questionnaire that measures their satisfaction and experience. At the end of the entire process, they will also answer a short questionnaire on their individual characteristics. CONCLUSION

Analysis of the data collected through our experiment should demonstrate that despite its intuitive appeal, online mass customization may not be appropriate for all customers. One such example involves online customers with low computer playfulness who may prefer a more straightforward product choice process. Also, as the number of product choices increases, customers may find the traditional attribute-based MC interface overwhelming and may opt to let the company customize the product for them. Such results will shed light on the success factors of mass customization online and provide useful guidelines for online companies that provide such a service, such as how to understand and leverage the shifting inverted Ushaped curve, how to profile end-users to fit their needs better, and can extend and refine the Task-Technology fit model. ACKNOWLEDGEMENTS


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This project is funded by a Eugene M. Lang Research Fellowship award at Baruch College, CUNY. REFERENCES

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