E-tailers versus retailers: which factors determine consumer preferences

Abstract The growth of Internet technology and electronic commerce has not been matched by theoretically guided social science research. Clear and well-designed consumer research is needed to describe, explain, and predict what will happen to this changing landscape. The primary purpose of this study is to investigate the structure for consumer preferences to make product purchases through three available retail formats—store, catalog, and the Internet. Conjoint analysis was used to assess the structure of the decision and the importance of the attributes in the decision-making process. The results from this study noticeably show that the structure of the consumer decision-making process was found to be primarily one of choosing the retail format (store, catalog, or Internet) and price of product (set at low, medium, or high) desired. The strength of the retail store format suggests that fears that the Internet will take over the retail arena seem, at least at this point in time, overblown and exaggerated. However, there seems to be an identifiable segment of customers that has a preference for the Internet as a retail shopping alternative.

[1]  William L. Moore,et al.  Measuring preferences with hybrid conjoint analysis: The impact of a different number of attributes in the master design , 1988 .

[2]  Shirley Taylor,et al.  Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions , 1995 .

[3]  P. Linn,et al.  Implications for Research and Practice , 2003 .

[4]  I. Ajzen,et al.  A Comparison of the Theory of Planned Behavior and the Theory of Reasoned Action , 1992 .

[5]  P. Green,et al.  Conjoint Analysis in Consumer Research: Issues and Outlook , 1978 .

[6]  Kieran Mathieson,et al.  Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior , 1991, Inf. Syst. Res..

[7]  Paul E. Green,et al.  Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice , 1990 .

[8]  Thomas Hill,et al.  Role of efficacy expectations in predicting the decision to use advanced technologies: The case of computers. , 1987 .

[9]  S. Addelman.  Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments , 1962 .

[10]  Joseph F. Hair,et al.  An Assessment of the Mall Intercept as a Data Collection Method , 1985 .

[11]  M. Aldenderfer.  Cluster Analysis , 1984 .

[12]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[13]  Richard P. Bagozzi,et al.  A comparison of leading theories for the prediction of goal‐directed behaviours , 1995 .

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

[15]  Jordan J. Louviere,et al.  Analyzing Decision Making: Metric Conjoint Analysis , 1988 .

[16]  S. Balasubramanian.  Mail Versus Mall: a Strategic Analysis of Competition Between Direct Marketers and Conventional Retailers , 1998 .

[17]  Rose Sebastianelli,et al.  The state of online retailing , 2003, Internet Res..

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

[19]  J. Douglas Carroll,et al.  Psychometric Methods in Marketing Research: Part I, Conjoint Analysis , 1995 .

[20]  P. A. Dabholkar.  Consumer evaluations of new technology-based self-service options: An investigation of alternative models of service quality , 1996 .

[21]  Seymour Sudman,et al.  Improving the Quality of Shopping Center Sampling , 1980 .

[22]  R. B. Johnson.  Estimating an Evaluation Utilization Model Using Conjoint Measurement and Analysis , 1995 .

[23]  Edward Blair,et al.  Sampling Issues in Trade Area Maps Drawn from Shopper Surveys , 1983 .

[24]  Fred D. Davis.  User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts , 1993, Int. J. Man Mach. Stud..

[25]  Girish N. Punj,et al.  Cluster Analysis in Marketing Research: Review and Suggestions for Application , 1983 .

[26]  Rajan Nataraajan.  Prediction of choice in a technically complex, essentially intangible, highly experiential, and rapidly evolving consumer product , 1993 .

[27]  I. Ajzen.  Attitudes, Personality and Behavior , 1988 .

[28]  Pratibha A. Dabholkar.  Role of Affect and Need For Interaction in On-Site Service Encounters , 1992 .

[29]  Paul E. Green,et al.  Segmenting Markets with Conjoint Analysis , 1991 .

[30]  Fred D. Davis,et al.  A critical assessment of potential measurement biases in the technology acceptance model: three experiments , 1996, Int. J. Hum. Comput. Stud..

[31]  James T. Low,et al.  A Monté Carlo Simulation Study of Two Approaches for Aggregating Conjoint Data , 1983 .

[32]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

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

[34]  Peter A. Todd,et al.  Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication , 1992, MIS Q..

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

[36]  Michael R. Solomon,et al.  Predictability and Personalization in the Service Encounter , 1987 .

[37]  I. Ajzen.  The theory of planned behavior , 1991 .