USER DESIGN OF CUSTOMIZED PRODUCTS

User design offers tantalizing potential benefits to manufacturers and consumers, including a closer match of products to user preferences, which should result in a higher willingness to pay for goods and services. There are two fundamental approaches that can be taken to user design: parameterbased systems and needs-based systems. With parameter-based systems, users directly specify the values of design parameters of the product. With needs-based systems, users specify the relative importance of their needs, and an optimization algorithm recommends the combination of design parameters that is likely to maximize user utility. Through an experiment in the domain of consumer laptop computers, we show that for parameter-based systems, outcomes, including measures for comfort and fit, increase in the expertise of the user. We also show that for novices, the needs-based interface results in better outcomes than the parameter-based interface.

[1]  M. Sujan,et al.  Consumer Knowledge: Effects on Evaluation Strategies Mediating Consumer Judgments , 1985 .

[2]  S. Hunt,et al.  The Commitment-Trust Theory of Relationship Marketing , 1994 .

[3]  Arvind Rangaswamy,et al.  Choice in Computer-Mediated Environments , 1997 .

[4]  Lawrence Olson,et al.  Specification and Estimation of a Simultaneous-Equation Model with Limited Dependent Variables , 1978 .

[5]  Anol Bhattacherjee,et al.  Individual Trust in Online Firms: Scale Development and Initial Test , 2002, J. Manag. Inf. Syst..

[6]  Stacy Wood,et al.  Prior Knowledge and Complacency in New Product Learning , 2002 .

[7]  Mark W. Watson.  Introduction to econometrics. , 1968 .

[8]  Joel Huber,et al.  The Effectiveness of Alternative Preference Elicitation Procedures in Predicting Choice , 1993 .

[9]  David W. Beach,et al.  Integrated Product Design for Marketability and Manufacturing , 1997 .

[10]  J. Wind,et al.  Customerization: The next revolution in mass customization , 2001 .

[11]  Christoph H. Loch,et al.  Collaborative Prototyping and the Pricing of Custom - Designed Products , 2004, Manag. Sci..

[12]  Langche Zeng,et al.  Analyzing Censored and Sample-Selected Data with Tobit and Heckit Models , 1999, Political Analysis.

[13]  Gurpreet Dhillon,et al.  TORKZADEH AND DHILLON Measuring Factors that Influence the Success of Internet , 2015 .

[14]  Joseph P. Cannon,et al.  An Examination of the Nature of Trust in Buyer–Seller Relationships: , 1997 .

[15]  Karl T. Ulrich,et al.  Product Design and Development , 1995 .

[16]  H. Theil.  Introduction to econometrics , 1978 .

[17]  Karl T. Ulrich,et al.  Holistic Customer Requirements and the Design-Select Decision , 1999 .

[18]  John G. Lynch,et al.  Wine Online: Search Costs Affect Competition on Price, Quality, and Distribution , 2000 .

[19]  Christian Terwiesch,et al.  Measuring the Frictional Costs of Online Transactions: The Case of a Name-Your-Own-Price Channel , 2003, Manag. Sci..

[20]  Karl T. Ulrich,et al.  Augmenting the house of quality with engineering models , 2018 .

[21]  Chan Su Park,et al.  Surprising Robustness of the Self-Explicated Approach to Customer Preference Structure Measurement , 1997 .

[22]  R. Ferber.  Handbook of Marketing Research , 1974 .

[23]  A. Rangaswamy,et al.  Consumer Choice Behavior in Online and Traditional Supermarkets: The Effects of Brand Name, Price, , 2000 .

[24]  David Yermack,et al.  Do Corporations Award CEO Stock Options Effectively , 1994 .

[25]  Merrie Brucks,et al.  Search Monitor: An Approach for Computer-Controlled Experiments Involving Consumer Information Search , 1988 .

[26]  Eric J. Johnson,et al.  When Web Pages Influence Choice: Effects of Visual Primes on Experts and Novices , 2002 .

[27]  A. Rangaswamy,et al.  Customer satisfaction and loyalty in online and offline environments , 2003 .

[28]  J. Hauser,et al.  The virtual customer , 2002 .

[29]  J. G. Cragg.  Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods , 1971 .

[30]  R. Lal,et al.  When and How is the Internet Likely to Decrease Price Competition , 1999 .

[31]  John O. Summers,et al.  Reliability and Validity of Conjoint Analysis and Self-Explicated Weights: A Comparison , 1984 .

[32]  Karl T. Ulrich,et al.  A Designer’s Spreadsheet , 1997 .

[33]  E. Dahan,et al.  The predictive power of internet-based product concept testing using visual depiction and animation , 2000 .

[34]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

[35]  J. Bettman,et al.  Effects of Framing on Evaluation of Comparable and Noncomparable Alternatives by Expert and Novice Consumers , 1987 .

[36]  William L. Moore,et al.  A Comparison of Conjoint Methods When There Are Many Attributes , 1999 .

[37]  Christopher P. Puto,et al.  Adding Asymmetrically Dominated Alternatives: Violations of Regularity & the Similarity Hypothesis. , 1981 .