Factors Influencing the Adoption of Mass Customization: The Impact of Base Category Consumption Frequency and Need Satisfaction

Mass customization has received considerable interest among researchers. However, although many authors have analyzed this concept from different angles, the question of which factors can be used to spot customers most likely to adopt a masscustomized product has not been answered to a satisfactory extent until now. This article explicitly deals with this question by focusing on factors related to the base category, which is defined as the group of all standardized products within the same product category as the mass-customized product under investigation. Specifically, this article investigates the influence of a customer’s base category consumption frequency and need satisfaction on the decision to adopt a mass-customized product within this base category. A set of competing hypotheses regarding these influences is developed and subsequently evaluated by a combination of partial least squares and latent class analysis. This is done by using a sample of 2,114 customers surveyed regarding their adoption of an individualized printed newspaper. The results generated are threefold: First, it is shown that there is a significant direct influence of base category consumption frequency and need satisfaction on the behavioral intention to adopt. The more frequently a subject consumes products out of the base category or the more satisfied his or her needs are due to this consumption, the higher the behavioral intention to adopt a mass-customized product within this base category. Second, the article provides an indication that base category consumption frequency has a significant moderating effect when investigating the behavioral intention to adopt in the context of the theory of reasoned action and the technology acceptance model. The more frequently a subject consumes products out of the base category, the more important will be the impact of perceived ease of use mediated by perceived usefulness. Finally, this article shows that different latent classes with respect to unobserved heterogeneity regarding the latent variables base category need satisfaction or dissatisfaction have significantly different adoption behaviors. Individuals who show a high level of need dissatisfaction are less interested in the ease of use of a mass-customized product than its usefulness (i.e., increase in need satisfaction). On the other hand, subjects who have a high degree of base category need satisfaction base their adoption decision mainly on the ease of use of the masscustomized product. These results are of managerial relevance regarding the prediction of market reactions and the understanding of the strategic use of product-line extensions based on mass-customized products. This work provides an

[1]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[2]  Totz,et al.  The many faces of personalization - an integrative overview of mass customization and personalization , 2001 .

[3]  Kamel Jedidi,et al.  A Hierarchical Bayesian Methodology for Treating Heterogeneity in Structural Equation Models , 2000 .

[4]  Peter H. Bloch Involvement Beyond the Purchase Process: Conceptual Issues and Empirical Investigation , 1982 .

[5]  J. Vermunt,et al.  Latent class cluster analysis , 2002 .

[6]  F. Bookstein,et al.  Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory , 1982 .

[7]  Benedict G. C. Dellaert,et al.  Marketing Mass-Customized Products: Striking a Balance between Utility and Complexity , 2005 .

[8]  B. J. Pine,et al.  Making mass customization work , 1993 .

[9]  Rashi Glazer Marketing in an Information-Intensive Environment: Strategic Implications of Knowledge as an Asset , 1991 .

[10]  Fred D. Davis A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[11]  Andrew B. Whinston,et al.  The Complementarity Of Mass Customization And Electronic Commerce , 2000 .

[12]  G. Laurent,et al.  Measuring Consumer Involvement Profiles , 1985 .

[13]  Alladi Venkatesh,et al.  Beyond Adoption: Development and Application of a Use-Diffusion Model , 2004 .

[14]  Detlef Schoder,et al.  Mass Customization in the Newspaper Industry: Consumers' Attitudes Toward Individualized Media Innovations , 2006 .

[15]  J. Scott Armstrong,et al.  Hypotheses in Marketing Science: Literature Review and Publication Audit , 2005 .

[16]  J. Bessant,et al.  Mass customization: the key to customer value? , 2004 .

[17]  Charles D. Barrett Understanding Attitudes and Predicting Social Behavior , 1980 .

[18]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[19]  A. Bardakci,et al.  Mass‐customisation in marketing: the consumer perspective , 2003 .

[20]  N. Franke,et al.  Value Creation by Toolkits for User Innovation and Design: The Case of the Watch Market , 2004 .

[21]  Alan R. Andreasen,et al.  Consumer Responses to Dissatisfaction in Loose Monopolies , 1985 .

[22]  G. Laurent,et al.  Repeat Purchasing of New Automobiles by Older Consumers: Empirical Evidence and Interpretations , 2005 .

[23]  Steve Hoeffler,et al.  Measuring Preferences for Really New Products , 2003 .

[24]  John D. C. Little,et al.  The Marketing Information Revolution , 1994 .

[25]  Frank T. Piller,et al.  Does mass customization pay? An economic approach to evaluate customer integration , 2004 .

[26]  Mark S. Johnson,et al.  The Different Roles of Satisfaction, Trust, and Commitment in Customer Relationships , 1999 .

[27]  J. Farquhar,et al.  Consumer responses to dissatisfaction with financial service providers: An exploration of why some stay while others switch , 2004 .

[28]  Bernard C. Y. Tan,et al.  A Cross-Cultural Study on Escalation of Commitment Behavior in Software Projects , 2000, MIS Q..

[29]  Kai Riemer,et al.  The Many Faces of Personalization , 2003 .

[30]  Thomas W. Leigh,et al.  Competitive assessment in service industries , 1989 .

[31]  Philip Kotler,et al.  From mass marketing to mass customization , 1989 .

[32]  S. Kotha Mass Customization: The New Frontier in Business Competition , 1992 .

[33]  Alan G. Sawyer,et al.  The Significance of Statistical Significance Tests in Marketing Research , 1983 .

[34]  Alice M. Tybout,et al.  Schema Congruity as a Basis for Product Evaluation , 1989 .

[35]  Ellen Day,et al.  Share of Heart: What is it and How can it be Measured? , 1989 .

[36]  Eric von Hippel,et al.  The Journal of Product Innovation Management 18 (2001) 247–257 PERSPECTIVE: User toolkits for innovation , 2022 .

[37]  A. Kaplan,et al.  A Beginner's Guide to Partial Least Squares Analysis , 2004 .

[38]  Steven M. Shugan The Cost Of Thinking , 1980 .

[39]  R. Rothschild The Theory of Monopolistic Competition: E.H. Chamberlin's Influence on Industrial Organisation Theory over Sixty Years , 1987 .

[40]  Gregory S. Carpenter,et al.  Meaningful Brands from Meaningless Differentiation: The Dependence on Irrelevant Attributes , 1994 .

[41]  T. S. Robertson,et al.  A Propositional Inventory for New Diffusion Research , 1985 .

[42]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[43]  Andreas M. Kaplan,et al.  Toward a Parsimonious Definition of Traditional and Electronic Mass Customization , 2006 .

[44]  Frank Huber,et al.  Capturing Customer Heterogeneity using a Finite Mixture PLS Approach , 2002 .

[45]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

[46]  C. Hart Mass customization: conceptual underpinnings, opportunities and limits , 1995 .

[47]  B. Nooteboom,et al.  Effects of trust and governance on relational risk. , 1997 .

[48]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[49]  Detmar W. Straub,et al.  The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption , 2000, J. Assoc. Inf. Syst..

[50]  H. Wold Path Models with Latent Variables: The NIPALS Approach , 1975 .

[51]  J. Zaichkowsky Measuring the Involvement Construct , 1985 .

[52]  N. Franke Key Research Issues in User Interaction with Configuration Toolkits in a Mass Customization System , 2002 .

[53]  V. Zeithaml Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence: , 1988 .

[54]  Rosanna Garcia,et al.  A critical look at technological innovation typology and innovativeness terminology: a literature review , 2002 .

[55]  Jochen Wirtz,et al.  Congruency of Scent and Music As a Driver of In-Store Evaluations and Behavior , 2001 .

[56]  Blair H. Sheppard,et al.  The Theory of Reasoned Action: A Meta-Analysis of Past Research with Recommendations for Modifications and Future Research , 1988 .

[57]  Wynne W. Chin,et al.  A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic - Mail Emotion/Adoption Study , 2003, Inf. Syst. Res..

[58]  Jorgen P. Bansler,et al.  Corporate Intranet Implementation: Managing Emergent Technologies and Organizational Practices , 2000, J. Assoc. Inf. Syst..

[59]  J. Gentry,et al.  Characteristics of Adopters and Non-Adopters of Home Computers , 1983 .

[60]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[61]  John Hulland,et al.  Use of partial least squares (PLS) in strategic management research: a review of four recent studies , 1999 .

[62]  E. Hippel,et al.  Customers As Innovators: A New Way to Create Value , 2002 .

[63]  E. Chamberlin The Theory of Monopolistic Competition , 1933 .

[64]  W. DeSarbo,et al.  Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity , 1997 .

[65]  Elena Karahanna,et al.  Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage , 2000, MIS Q..

[66]  Claudio Roberto Boër,et al.  Editorial: Shoe design and manufacturing , 2004, Int. J. Comput. Integr. Manuf..

[67]  B. Kahn,et al.  Variety for sale: Mass customization or mass confusion? , 1998 .

[68]  Wynne W. Chin,et al.  Structural equation modeling analysis with small samples using partial least squares , 1999 .

[69]  Gilbert A. Churchill A Paradigm for Developing Better Measures of Marketing Constructs , 1979 .

[70]  Giovani J.C. da Silveira,et al.  Mass customization: Literature review and research directions , 2001 .

[71]  Bernard H. Boar,et al.  Strategic Thinking for Information Technology , 1996 .

[72]  C. Judd,et al.  Statistical difficulties of detecting interactions and moderator effects. , 1993, Psychological bulletin.

[73]  S. Ram,et al.  Consumer Resistance to Innovations: The Marketing Problem and its solutions , 1989 .

[74]  I. Simonson,et al.  The Effect of New Product Features on Brand Choice , 1996 .

[75]  Richard L. Celsi,et al.  The Role of Involvement in Attention and Comprehension Processes , 1988 .

[76]  Terry L. Childers,et al.  Understanding how Product Attributes Influence Product Categorization: Development and Validation of Fuzzy Set-Based Measures of Gradedness in Product Categories , 1999 .