Information Quality Training Requirements Analysis Guideline Demonstrated in a Healthcare Context

In recent years, organizations have realized the critical importance of Information Quality Management (IQM) as a means to achieve long-term business success. Yet, many organizations find it difficult to implement an effective Information Quality Training Plan (IQTP). One reason for this is that they fail to identify the prime predictors of Information Quality (IQ) and therefore, the resources and likewise the commitment to develop an effective, systematically tailored approach to IQTPs is lost. To systematically address this problem is the primary driver behind this thesis. To achieve this objective, a practical conceptual model, which allows the measurement of the anticipated predictors’ impact on IQ, and a Design Science (DS) problem-based solution; an Information Quality Training Requirements Analysis Guideline (IQTRAG) is proposed. This model was validated by means of a controlled laboratory experiment, a Web based survey and interviews and discussions with practitioners and experimental participants. Data was gathered through triangulation, evaluated through a combination of ttests and correlation and regression analysis. The findings show that relationships exist between IQ and 1) trainee characteristics, 2) training design, 3) work environment, 4) trainee’ perception of IQ importance, and 5) trainee work and documentation experience. The results from the analysis supported the hypotheses and the need for an Information Quality Training Requirements Analysis Guideline to aid the development of Information Quality Training Plans. Based on this analysis it can be recommended that organizations use the Information Quality Training Requirements Analysis Guideline before implementing Information Quality Training Plans.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Dublin City University

Year dc:date.issued

2011

Author dc:creator

• Levis, Mary

• Knowledge management
• Management

Language dc:language

en