Knowledge bases for users' attitudes toward ERP systems

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Knowledge bases for users' attitudes toward ERP systems

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Abstract

Enterprise resource planning (ERP) systems have gained much attention from both practitioners and researchers because they are generally regarded as tools to improve business efficiency and employee productivity. Previous surveys featured ERP software markets as high potential margin and intense competition. Many factors may influence business decision on ERP systems implementation where users' attitudes regarding these factors play a crucial role to the decision. ERP software vendors must design the marketing strategies in harmony with the prospective users' requirements. This study proposes a novel approach, Bayesian networks, in developing knowledge bases of users' attitudes toward ERP systems. Using the Bayesian network, the factors and their interrelationship will be modeled compactly in graphical as well as numerical levels. This work first identifies the factors influencing business decision to implement ERP systems

Abstract
Introduction
ERP systems
Bayesian networks
1. Factors influencing ERP implementation
2. Learning the parameter from questionnaire data
3. Limitation of this study
Queries from the knowledge bases
1. Case 1
2. Case 2
Conclusion
References

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[...] The variable D provides referential information in predicting the potential users' ultimate attitudes toward ERP systems besides their business profiles. The information of D is listed as follow. Among the ones whose companies have implemented ERP systems would still implement the systems if they were the decision makers; while only are against the proposal. Oppositely, for those whose companies are not using and will not use ERP systems, approximately 50% would approve the implementation and only 10% would disapprove the implementation. [...]

[...] The joint probability distribution of the Bayesian network is expressed as i i i i = i i =11 i =13 i = Fig Procedure for building the Bayesian networks on users' attitudes toward ERP systems A19 A18 ERP disseminate in the industry Variety of demand A2 A1 User friendliness DP capability A3 Functions B7 Pressure from environments A4 Data accuracy A17 A16 Project scheduling capability B1 System Functionality B2 Information quality C B6 Organizational and managerial factors Status of ERP implementation A5 Compatibility with other systems Strategic compliance B3 Experiences of related systems A15 Users' involvement in decision Business variation A6 Effectiveness of related systems B5 A13 Intangible benefits Expected benefits B4 Costs Top management support A14 Education and training costs Tangible benefits Satisfaction at related systems A7 A8 intangible costs Acquisition costs A12 A11 A10 A9 Fig 3 The Bayesian network on users' attitudes toward ERP systems Each node in the network has three possible states and 3. [...]

[...] O'Donnell; Foreign Subsidiary Compensation Strategy and Agent Theory Perspective, Academy of Management Journal, Vol pp 678- R. Rumelt, Strategy, Structure, and Economic Performance, Harvard University Press, Cambridge, MA L. K. Lewis and D. R. Seibold; Innovation Modification during Intra-organizational Adoption, Academy of Management Review, Vol pp 322- L. Dong; Modeling Top Management Influence on ES Implementation, Business Process Management Journal, Vol pp 243- C. P. Holland and B. Light; A Critical Success Factors Model for ERP Implementation, IEEE Software, Vol pp 30- S. Wee, Juggling toward ERP Success: Keep Key Success Factors High Available at [...]

[...] This study proposes a novel approach, Bayesian networks, as the knowledge bases for users' attitudes toward ERP software. Bayesian networks have been widely utilized as the knowledge bases in medicine, engineering, business, etc, whose application in enterprise systems marketing are still sparse. This study adopts a three-stage procedure in developing the knowledge bases. In the first stage, by literature review this work identifies the factors influencing business decision to implement ERP software. In the second stage, based on these factors this work designs the questionnaire to investigate users' attitudes towards ERP systems, which are further analyzed for the foundation of the marketing knowledge bases. [...]

[...] P ( D = 1 = P ( D = 1 P = 0.5434 c P ( D = 2 = P ( D = 2 P = 0.3677 c P ( D = 3 = P ( D = 3 P = 0.0888 c Since this customer tends to demand high information quality ( B2 = 1 high expected benefits ( B5 = 1 and faces high pressure from environments ( B7 = 1 the marketing strategy should accentuate the relevant utility to reinforce the customer's motivation in adopting ERP systems Conclusion This study proposes a novel approach, Bayesian networks, in developing knowledge bases of users' attitudes toward ERP systems. [...]

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