Data Quality Management and Responsiveness of Indigenous Oil and Gas Companies in Rivers State, Nigeria

Authors: Florence Arikekpar and Prof. A. E. Bestman

Abstract:
This study examined the relationship between data quality management and responsiveness of indigenous oil and gas companies in Rivers State, Nigeria. The dimension of data quality management used was data reliability. The study adopted the cross-sectional research survey design. Primary data was generated through structured questionnaire. The population of this study consisted of the thirty-three (33) registered and functional indigenous oil and gas companies in Rivers State, Nigeria. In this study the researcher adopted a census sampling technique to study all the thirty-three (33) registered and functional indigenous oil and gas companies in Rivers State, Nigeria because the population was small However, the study respondents were 99 in the 33 indigenous oil and gas companies in Rivers State. The reliability of the instrument was achieved by the use of the Cronbach Alpha coefficient with all the items scoring above 0.70. The hypotheses were tested using the Spearman’s Rank Order Correlation Coefficient. The tests were carried out at a 0.05 significance level. Findings revealed that there is a significant relationship between data quality management and responsiveness of indigenous oil and gas companies in Rivers State, Nigeria. The study concludes that that there is a positive significant relationship between data quality management and the responsiveness of indigenous oil and gas companies in Rivers State, Nigeria. Therefore, the study recommended that Indigenous oil and gas companies should establish robust procedures to validate and verify the reliability of data. This includes conducting thorough checks to ensure data accuracy, consistency, and integrity. Implement validation rules, cross-referencing techniques, and data reconciliation processes to identify and address any discrepancies or errors.

Keywords: Data quality management, Responsiveness, Data reliability, Adaptability, Agility, Praocativeness

Download pdf