A Rule-based DSS for the Qualitative Prediction of the Evolution of e-Sales

Many parameters have a significant influence on c-commerce evolution. This complicates the assessment of the requirements for and the consequences of e-sales adoption. In order to support the decisions of companies thinking about a possible e-sales channels introduction a Decision Support System (DSS) is proposed. The relevant e-commerce success factors, which constitute the DSS input, have been identified and their influence described relying upon a literature review. The DSS output aims at describing typical e-commerce evolution patterns taking into account the speed of adoption and the steady state potential diffusion (saturation level). These variables point out the considerable discrepancies between the e-commerce evolution charactering different industrial sectors. The DSS, which is based on a system of rules, allows to qualitatively predict the expected e-sales evolution for companies introducing a specific e-sales channels strategy in a given environment and to explain it in terms of different e-commerce success factor configurations.

[1]  Bart J. Bronnenberg,et al.  Exploring the implications of the internet for consumer marketing , 1997 .

[2]  Ting-Peng Liang,et al.  An empirical study on consumer acceptance of products in electronic markets: a transaction cost model , 1998, Decis. Support Syst..

[3]  D. V. Poel,et al.  Consumer Acceptance of the Internet as a Channel of Distribution , 1999 .

[4]  Sui Meng Poon,et al.  Factors influencing the types of products and services purchased over the Internet , 2000, Internet Res..

[5]  Oded Lowengart,et al.  Differential Effects of Product Category on Shoppers' Selection of Web-based Stores: A Probabilistic Modeling Approach , 2001, J. Electron. Commer. Res..

[6]  Susan M. Mudambi.  Branding Importance in Business-to-Business Markets: Three Buyer Clusters , 2002 .

[7]  Ada Scupola,et al.  Adoption issues of business-to-business Internet commerce in European SMEs , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[8]  Varun Grover,et al.  Information technology innovations: general diffusion patterns and its relationships to innovation characteristics , 2002, IEEE Trans. Engineering Management.

[9]  Michael J. Shaw,et al.  Chapter A Study on the Value of B 2 B E-Commerce : The Case of Web-based Procurement , 2002 .

[10]  Leo R. Vijayasarathy,et al.  Product characteristics and Internet shopping intentions , 2002, Internet Res..

[11]  Eric K. Clemons,et al.  Online distribution: a taxonomy of channel structures, determinants of outcome, and determinants of strategy , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[12]  Walter F. Stenning,et al.  AN EMPIRICAL STUDY , 2003 .

[13]  Nena Lim,et al.  Consumers' perceived risk: sources versus consequences , 2003, Electron. Commer. Res. Appl..

[14]  Ulf Johansson,et al.  A conceptualisation of the opportunities and barriers to e‐business development in the smaller firm , 2003 .

[15]  Aron M. Levin,et al.  Product Category Dependent Consumer Preferences for Online and Offline Shopping Features and Their Influence on Multi-Channel Retail Alliances , 2003, J. Electron. Commer. Res..

[16]  Xiao Liu,et al.  An empirical study of product differences in consumers' E-commerce adoption behavior , 2003, Electron. Commer. Res. Appl..

[17]  Kevin Grant,et al.  An Investigation into Issues Influencing the Use of the Internet and Electronic Commerce among Small-Medium Sized Enterprises , 2003, J. Electron. Commer. Res..

[18]  N. Cheikhrouhou,et al.  Supporting electronic sales channels deployment through the analysis of customer preferences , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[19]  Jau-Yang Liu,et al.  Adopting e-commerce Web site operation modes: The current state of large US firms , 2004, Inf. Manag. Comput. Secur..

[20]  Russell Abratt,et al.  Brand equity in the business-to-business market , 2004 .

[21]  Heeseok Lee,et al.  Effects of Web retail service quality and product categories on consumer behavior: a research model and empirical exploration , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[22]  K. Celuch,et al.  A classification of business-to-business buying decisions: risk importance and probability as a framework for e-business benefits , 2004 .

[23]  Lina Zhou,et al.  DISCOVERING RULES FOR PREDICTING CUSTOMERS ’ ATTITUDE TOWARD INTERNET RETAILERS , 2005 .

[24]  C. Laymon.  A. study , 2018, Predication and Ontology.