User acceptance of complex electronic market mechanisms: Role of information feedback

Abstract This paper broadens the scope of evaluating the design of economic mechanisms that is traditionally done solely from an economic perspective. We introduce and demonstrate the application of acceptability to evaluate complex economic mechanisms. In particular, we apply our approach to the evaluation of continuous combinatorial auctions, which represent a complex, sophisticated market mechanism that has not been generally available in the online marketplace but has the potential to enhance the economic efficiency of trade for assets with interdependent values. Such auctions are being increasingly used in industry, e.g., to procure logistical services. Intuitively, acceptance and usage of a complex mechanism can be fostered by a design that provides information and tools that meet the users’ task demands. Based on prior research and an analysis of the auction tasks, we discuss practical and innovative information feedback schemes for reducing the cognitive burden of formulating bids in combinatorial auctions. Then, we use constructs from the technology acceptance model (TAM) – which have been consistently shown to be key determinants of technology acceptance in the extant literature – to compare the acceptability of the mechanism under three different information regimes. In addition, we borrow constructs from marketing theory to assess the potential growth in adoption of the mechanism. We compare user perceptions of the three alternative designs in a laboratory experiment with over 130 subjects. Our study constitutes a complementary and novel approach in evaluating the design of complex economic mechanisms. Results indicate a higher adoption and usage potential of the mechanism with advanced information feedback, supporting the potential of combinatorial auctions as a user-acceptable market mechanism with appropriate feedback.

[1]  Robert J. Kauffman,et al.  Strategic 'morphing' and the survivability of e-commerce firms , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[2]  Andrea Seaton Kelton,et al.  Internet financial reporting: The effects of information presentation format and content differences on investor decision making , 2012, Comput. Hum. Behav..

[3]  Mark M. Bykowsky,et al.  Mutually Destructive Bidding: The FCC Auction Design Problem , 2000 .

[4]  Iris Vessey,et al.  Cognitive Fit: A Theory‐Based Analysis of the Graphs Versus Tables Literature* , 1991 .

[5]  Alok Gupta,et al.  Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions , 2005, Inf. Syst. Res..

[6]  Sandy D. Jap The Impact of Online Reverse Auction Design on Buyer–Supplier Relationships , 2007 .

[7]  Bruce Cooil,et al.  A Longitudinal Examination of Net Promoter and Firm Revenue Growth , 2007 .

[8]  Shawn P. Curley,et al.  Impact of Information Feedback in Continuous Combinatorial Auctions: An Experimental Study of Economic Performance , 2013, MIS Q..

[9]  Pinar Keskinocak,et al.  Combinatorial Auctions in Procurement , 2004 .

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

[11]  Viswanath Venkatesh,et al.  Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation , 1999, MIS Q..

[12]  Paul Milgrom,et al.  Putting Auction Theory to Work , 2004 .

[13]  Elena Katok,et al.  The Effect of Timing on Jump Bidding in Ascending Auctions , 2007 .

[14]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[15]  Ronald M. Harstad,et al.  Computationally Manageable Combinational Auctions , 1998 .

[16]  Tuomas Sandholm,et al.  Approaches to winner determination in combinatorial auctions , 2000, Decis. Support Syst..

[17]  Fred D. Davis,et al.  Development and Test of a Theory of Technological Learning and Usage , 1992 .

[18]  Paul Sparrow,et al.  Strategy and cognition: understanding the role of management knowledge structures, organizational memory and information overload , 1999 .

[19]  P. Cramton,et al.  Introduction to Combinatorial Auctions , 2006 .

[20]  Ho Soo Lee,et al.  Special Issue: 2002 Franz Edelman Award for Achievement in Operations Research and the Management Sciences: Combinatorial and Quantity-Discount Procurement Auctions Benefit Mars, Incorporated and Its Suppliers , 2003, Interfaces.

[21]  Andrew B. Whinston,et al.  Decentralized Mechanism Design for Supply Chain Organizations Using an Auction Market , 2003, Inf. Syst. Res..

[22]  Paul A. Pavlou,et al.  Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior , 2006, MIS Q..

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

[24]  A. Bandura Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .

[25]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[26]  Paul Jen-Hwa Hu,et al.  Theory-Informed Design and Evaluation of an Advanced Search and Knowledge Mapping System in Nanotechnology , 2012, J. Manag. Inf. Syst..

[27]  Martin Bichler,et al.  A Computational Analysis of Linear Price Iterative Combinatorial Auction Formats , 2009, Inf. Syst. Res..

[28]  Joseph A. Swanson,et al.  Special Issue: Experimental Economics in Practice: The First Use of a Combined-Value Auction for Transportation Services , 2002, Interfaces.

[29]  Barbara H Wixom,et al.  A Theoretical Integration of User Satisfaction and Technology Acceptance , 2005, Inf. Syst. Res..

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

[31]  David Porter,et al.  Combinatorial auction design , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Wynne W. Chin The partial least squares approach for structural equation modeling. , 1998 .

[33]  E. Rogers Diffusion of Innovations , 1962 .

[34]  Arvind K. Tripathi,et al.  Understanding Willingness-to-Pay Formation of Repeat Bidders in Sequential Online Auctions , 2010, Inf. Syst. Res..

[35]  Paul J. Feltovich,et al.  An Introduction to Cambridge Handbook of Expertise and Expert Performance: Its Development, Organization, and Content , 2006 .

[36]  Charles A. Holt,et al.  An Experimental Test of Flexible Combinatorial Spectrum Auction Formats , 2010 .

[37]  Jayashankar M. Swaminathan,et al.  Models for Supply Chains in E-Business , 2003, Manag. Sci..

[38]  Andrew B. Whinston,et al.  Research Commentary: Introducing a Third Dimension in Information Systems Design - The Case for Incentive Alignment , 2001, Inf. Syst. Res..

[39]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[40]  David S. Johnson,et al.  Computers and In stractability: A Guide to the Theory of NP-Completeness. W. H Freeman, San Fran , 1979 .

[41]  F. Reichheld The one number you need to grow. , 2003, Harvard business review.

[42]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[43]  E. Bendoly Real-time feedback and booking behavior in the hospitality industry: Moderating the balance between imperfect judgment and imperfect prescription , 2013 .

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

[45]  Elena Katok,et al.  The Effect of Timing on Bid Increments in Ascending Auctions , 2005 .

[46]  Alok Gupta,et al.  Replicating Online Yankee Auctions to Analyze Auctioneers' and Bidders' Strategies , 2003, Inf. Syst. Res..

[47]  Thomas Morris,et al.  Reinventing the Supplier Negotiation Process at Motorola , 2005, Interfaces.

[48]  Fred Reichheld,et al.  The Ultimate Question: Driving Good Profits and True Growth , 2006 .

[49]  Andrea Seaton Kelton,et al.  The Effects of Information Presentation Format on Judgment and Decision Making: A Review of the Information Systems Research , 2010, J. Inf. Syst..

[50]  Viswanath Venkatesh,et al.  Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..

[51]  C. Carter,et al.  Electronic reverse auction configuration and its impact on buyer price and supplier perceptions of opportunism: A laboratory experiment , 2007 .

[52]  Shawn P. Curley,et al.  Design and Effects of Information Feedback in Continuous Combinatorial Auctions , 2007, ICIS.

[53]  Martin Pesendorfer,et al.  Auctioning bus routes: the London experience , 2006 .

[54]  Lawrence A. Gordon,et al.  Information overload: A temporal approach☆ , 1990 .

[55]  Viswanath Venkatesh,et al.  Leveraging Digital Technologies: How Information Quality Leads to Localized Capabilities and Customer Service Performance , 2013, MIS Q..

[56]  Shirley Gregor,et al.  The Anatomy of a Design Theory , 2007, J. Assoc. Inf. Syst..

[57]  Terence R. Mitchell,et al.  A cost-benefit mechanism for selecting problem-solving strategies: Some extensions and empirical tests , 1982 .

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

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

[60]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[61]  Ephraim R. McLean,et al.  Information Systems Success: The Quest for the Dependent Variable , 1992, Inf. Syst. Res..

[62]  Stephen J. Rassenti,et al.  Theory, experiment and the federal communications commission spectrum auctions , 2003 .

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

[64]  Charles A. Holt Markets, Games, & Strategic Behavior , 2007 .

[65]  Peter B. Seddon A Respecification and Extension of the DeLone and McLean Model of IS Success , 1997, Inf. Syst. Res..

[66]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

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

[68]  K. Anders Ericsson,et al.  The Cambridge Handbook of Expertise and Expert Performance: An Introduction to The Cambridge Handbook of Expertise and Expert Performance : Its Development, Organization, and Content , 2006 .

[69]  David Levine,et al.  Changing the Game in Strategic Sourcing at Procter & Gamble: Expressive Competition Enabled by Optimization , 2006, Interfaces.

[70]  J. Jacoby Perspectives on Information Overload , 1984 .

[71]  N. Malhotra Information Load and Consumer Decision Making , 1982 .

[72]  Vivek Choudhury,et al.  Issues and Opinions - IT Careers Camp: An Early Intervention Strategy to Increase IS Enrollments , 2010, Inf. Syst. Res..

[73]  Ramayya Krishnan,et al.  Effects of Information-Revelation Policies Under Market-Structure Uncertainty , 2007, Manag. Sci..

[74]  John W. Payne,et al.  Contingent decision behavior. , 1982 .

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

[76]  Dale Goodhue,et al.  Understanding user evaluations of information systems , 1995 .

[77]  Viswanath Venkatesh,et al.  Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model , 2000, Inf. Syst. Res..

[78]  F. Reichheld,et al.  The microeconomics of customer relationships , 2006 .

[79]  C. O'Reilly Individuals and Information Overload in Organizations: Is More Necessarily Better? , 1980 .

[80]  Chris Caplice,et al.  Combinatorial Auctions for Truckload Transportation , 2005 .

[81]  Dennis F. Galletta,et al.  Cognitive Fit: An Empirical Study of Information Acquisition , 1991, Inf. Syst. Res..

[82]  A. M. Harrell,et al.  THE EFFECT OF INFORMATION LOAD ON DECISION MAKERS' CUE UTILIZATION LEVELS AND DECISION QUALITY IN... , 1990 .

[83]  Fred D. Davis,et al.  A Model of the Antecedents of Perceived Ease of Use: Development and Test† , 1996 .

[84]  Anthony M. Kwasnica,et al.  A New and Improved Design for Multiobject Iterative Auctions , 2005, Manag. Sci..

[85]  Martin Bichler,et al.  Industrial Procurement Auctions , 2005 .

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

[87]  Neil A. Morgan,et al.  The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business Performance , 2006 .

[88]  R. Brent Gallupe,et al.  Information Overload: Addressing the Productivity Paradox in Face-to-Face Electronic Meetings , 1999, J. Manag. Inf. Syst..

[89]  Martin Bichler,et al.  Design science in information systems research , 2006, Wirtschaftsinf..

[90]  Terry P. Harrison,et al.  Better, Faster, Cheaper: An Experimental Analysis of a Multiattribute Reverse Auction Mechanism with Restricted Information Feedback , 2005, Manag. Sci..

[91]  Arun Rai,et al.  Assessing the Validity of IS Success Models: An Empirical Test and Theoretical Analysis , 2002, Inf. Syst. Res..