Effects of decision space information on MAUT-based systems that support purchase decision processes

This paper shows that decision makers often have a misconception of the decision space. The decision space is constituted by the relations among the attributes describing the alternatives available in a decision situation. The paper demonstrates that these misconceptions negatively affect the usage and perceptions of MAUT-based decision support systems. To overcome these negative effects, this paper proposes to use a visualization method based on singular value decomposition to give decision makers insights into the attribute relations. In a laboratory experiment in cooperation with Germany's largest Internet real estate website, this paper moreover evaluates the proposed solution and shows that our solution improves decision makers' usage and perceptions of MAUT-based decision support systems. We further show that information about the decision space ultimately affects variables relevant for the economic success of decision support system providers such as reuse intention and the probability to act as a promoter for the systems. Consumers often have misconceptions about the decision space.We propose that DSSs supporting consumer decisions should visualize the decision space.We suggest a visualization based on singular value decomposition.Our proposed visualization improves the satisfaction with recommendations.Our proposed visualization improves the perception of the DSS.

[1]  Viswanath Venkatesh,et al.  Expectation Confirmation in Technology Use , 2012, Inf. Syst. Res..

[2]  Beat Kleiner,et al.  Graphical Methods for Data Analysis , 1983 .

[3]  Greg M. Allenby,et al.  A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules , 2004 .

[4]  Izak Benbasat,et al.  The Nature and Consequences of Trade-Off Transparency in the Context of Recommendation Agents , 2014, MIS Q..

[5]  Rohit Verma,et al.  Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research , 2009, Eur. J. Oper. Res..

[6]  Zhengyuan Zhu,et al.  Singular Value Decomposition and Its Visualization , 2007 .

[7]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[8]  Roger D Feldman Filled Radar Charts Should not be Used to Compare Social Indicators , 2013 .

[9]  Evangelos Triantaphyllou,et al.  An examination of the effectiveness of multi-dimensional decision-making methods: A decision-making paradox , 1989, Decis. Support Syst..

[10]  Izak Benbasat,et al.  Explanations From Intelligent Systems: Theoretical Foundations and Implications for Practice , 1999, MIS Q..

[11]  Izak Benbasat,et al.  E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact , 2007, MIS Q..

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

[13]  Kalyanmoy Deb,et al.  Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead , 2008, Manag. Sci..

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

[15]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

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

[17]  Peter A. Todd,et al.  Solutions-driven marketing , 2002, CACM.

[18]  J. Muth Rational Expectations and the Theory of Price Movements , 1961 .

[19]  F. Nah,et al.  Enhancing%' or paper_id like 'brand equity through flow and telepresence: a comparison of 2D and 3D virtual worlds , 2011 .

[20]  J. Louviere,et al.  A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best-worst scaling , 2008 .

[21]  T. C. Edwin Cheng,et al.  Extending the Understanding of End User Information Systems Satisfaction Formation: An Equitable Needs Fulfillment Model Approach , 2008, MIS Q..

[22]  Michael Scholz,et al.  A configuration-based recommender system for supporting e-commerce decisions , 2017, Eur. J. Oper. Res..

[23]  Alan M. MacEachren,et al.  Supporting visual analysis of federal geospatial statistics , 2003, CACM.

[24]  Michael Scholz,et al.  Measuring consumers' willingness to pay with utility-based recommendation systems , 2015, Decis. Support Syst..

[25]  Peter S. H. Leeflang,et al.  Satisfaction as a predictor of future performance: A replication , 2013 .

[26]  Frank M. Bass,et al.  Adjusting Stated Intention Measures to Predict Trial Purchase of New Products: A Comparison of Models and Methods , 1989 .

[27]  Izak Benbasat,et al.  Trust In and Adoption of Online Recommendation Agents , 2005, J. Assoc. Inf. Syst..

[28]  B. Wernerfelt,et al.  An Evaluation Cost Model of Consideration Sets , 1990 .

[29]  Lorin M. Hitt,et al.  Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses: A Study of the Online Brokerage Industry , 2002, Inf. Syst. Res..

[30]  Ronald T. Cenfetelli,et al.  The Adoption of Online Shopping Assistants: Perceived Similarity as an Antecedent to Evaluative Beliefs , 2011, J. Assoc. Inf. Syst..

[31]  Ralph E. Steuer,et al.  Multiple Criteria Decision Making, Multiattribute Utility Theory: The Next Ten Years , 1992 .

[32]  Gavriel Salvendy,et al.  Comparison of 3D and 2D menus for cell phones , 2011, Comput. Hum. Behav..

[33]  Thomas Kramer The Effect of Measurement Task Transparency on Preference Construction and Evaluations of Personalized Recommendations , 2006 .

[34]  Michael Scholz,et al.  2D versus 3D Visualizations in Decision Support - The Impact of Decision Makers' Perceptions , 2015, ICIS.

[35]  Robert S. Wyer,et al.  Puffery in Advertisements: The Effects of Media Context, Communication Norms, and Consumer Knowledge , 2010 .

[36]  Tugrul U. Daim,et al.  Exploring Adaptivity in Service Development: The Case of Mobile Platforms , 2014 .

[37]  Gerald Häubl,et al.  Searching in Choice Mode: Consumer Decision Processes in Product Search with Recommendations , 2012 .

[38]  Na Yang,et al.  Decision support for preference elicitation in multi-attribute electronic procurement auctions through an agent-based intermediary , 2014, Decis. Support Syst..

[39]  M. F. Luce,et al.  Correlation, conflict, and choice. , 1993 .

[40]  Robert J. Kauffman,et al.  What Do You Know? Rational Expectations in Information Technology Adoption and Investment , 2003, J. Manag. Inf. Syst..

[41]  Christian Schlereth,et al.  Measurement of preferences with self-explicated approaches: A classification and merge of trade-off- and non-trade-off-based evaluation types , 2014, Eur. J. Oper. Res..

[42]  Peter Haddawy,et al.  Integrating Visualization and Multi-Attribute Utility Theory for Online Product Selection , 2007, Int. J. Inf. Technol. Decis. Mak..

[43]  Hans van der Heijden Mobile decision support for in-store purchase decisions , 2006, Decis. Support Syst..

[44]  Elmar Kiesling,et al.  A comparison of representations for discrete multi-criteria decision problems☆ , 2013, Decis. Support Syst..

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

[46]  Donald R. Jones,et al.  Using Visual Representations of Data to Enhance Sensemaking in Data Exploration Tasks , 2009, J. Assoc. Inf. Syst..

[47]  Patrick J. F. Groenen,et al.  A graphical shopping interface based on product attributes , 2007, Decis. Support Syst..

[48]  W. Härdle,et al.  Applied Multivariate Statistical Analysis , 2003 .

[49]  Gilbert A. Churchill,et al.  An Investigation into the Determinants of Customer Satisfaction , 1982 .

[50]  Shawn P. Curley,et al.  Effects of Online Recommendations on Consumers’ Willingness to Pay , 2012, Decisions@RecSys.

[51]  Shiu-li Huang,et al.  Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods , 2011, Electron. Commer. Res. Appl..

[52]  Donald R. Jones,et al.  The effects of incorporating compensatory choice strategies in Web-based consumer decision support systems , 2007, Decis. Support Syst..

[53]  Chuan-Hoo Tan,et al.  Consumer-based decision aid that explains which to buy: Decision confirmation or overconfidence bias? , 2012, Decis. Support Syst..

[54]  Raimo P. Hämäläinen,et al.  On the convergence of multiattribute weighting methods , 2001, Eur. J. Oper. Res..

[55]  Wynne W. Chin,et al.  A Fast Form Approach to Measuring Technology Acceptance and Other Constructs , 2008, MIS Q..

[56]  N. Schuwirth,et al.  Methodological aspects of multi-criteria decision analysis for policy support: A case study on pharmaceutical removal from hospital wastewater , 2012, Eur. J. Oper. Res..

[57]  I. Jolliffe Principal Component Analysis , 2002 .

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

[59]  F. John MUTH, . Rational Expectations and the Theory of Price Movements, Econometrica, , . , 1961 .

[60]  Ralph L. Keeney,et al.  Common Mistakes in Making Value Trade-Offs , 2002, Oper. Res..

[61]  R. Oliver A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .

[62]  B. Wansink,et al.  The Validity of Attribute-Importance Measurement: A Review , 2007 .

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

[64]  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..

[65]  B. Ratchford,et al.  Consumer information search revisited: Theory and empirical analysis , 1997 .