Development and determinants of end-user intention: usage of expert systems
| Autor: | Ronald D. Freeze, Khaled A. Alshare, Mohammad Kamel Alomari, Peggy L. Lane |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Knowledge management
End-user General Computer Science Computer science media_common.quotation_subject 02 engineering and technology Unified theory of acceptance and use of technology computer.software_genre Structural equation modeling LISREL 020204 information systems 0502 economics and business 0202 electrical engineering electronic engineering information engineering Quality (business) Relevance (information retrieval) Behavioral intention media_common Expectancy theory End user business.industry 05 social sciences Expert systems use Expert system 050211 marketing UTAUT business computer Information Systems |
| Zdroj: | Journal of Systems and Information Technology. 21:166-185 |
| ISSN: | 1328-7265 |
| DOI: | 10.1108/jsit-08-2018-0108 |
| Popis: | Purpose Expert systems (ES) design emulates expertise with the intention that the ES be used by non-experts. This study aims to predict end-user intention and use of ES by proposing a research model that extends the basic components of the unified theory of acceptance and use of technology (UTAUT) by including additional relevant factors to ES, including the expert domain, perceived relevance, reliability, quality of ES and management support, which directly and indirectly influence the end-user intention to use an expert system. Design/methodology/approach A structural equation model (SEM), using LISREL, was used to test the measurement and structural models using a sample of 205 end-users of expert systems in the USA. These users of expert systems come from a variety of domains. The factors include both internal and external factors for the individual level of analysis design of this study. Findings The results showed behavioral intention had the strongest effect on usage, followed by perceived relevance. With respect to the factors that impact intention, perceived relevance had the strongest total effect, followed by attitude. For attitude, effort expectancy had the strongest total effect, followed by management support and perceived relevance. Research limitations/implications The results of this study should assist decision-makers in planning training and communications about the use of expert systems so that the expert systems will be used as intended. Originality/value The originality of this work resides in the addition of external factors to the UTAUT model that helps provide advice to practitioners in the support needed to insure expert system implementation success. |
| Databáze: | OpenAIRE |
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