使用大數據提升決策品質與績效,常苦於對大數據能力的認知不足以及數據分析師缺乏專業能力,導致大數據無法在給定的任務之下發揮最大效用。本研究根據任務科技適配理論,從數據分析師觀點提出不同的適配—「任務科技適配」與「人員科技適配」—解釋影響大數據之任務績效的前因。針對臺灣前一千大企業內的數據分析師進行問卷調查研究,共計收回142份有效樣本。研究結果發現研發—行銷策略制定、大數據能力和資料敘事能力之間的適配程度對大數據之任務績效有不同的影響效果,研發—行銷策略制定與數據分析能力的適配對新產品開發績效有正向顯著影響,而資料敘事能力與數據整合能力的適配對流程整合績效有正向顯著影響。本研究延伸任務科技適配理論於大數據使用情境,並根據研究結果提出學術與實務上的建議。
Organizations are increasingly implementing Big-Data Analytics (BDA) to attain superior performance. However current literature on BDA offers little insight on achieving task performance from the perspectives of a business analyst. As a result, we propose a framework that benefits business analysts by aligning task-technology fit and people-technology fit throughout their BDA usage. By using primary data from 142 business analysts among top 1,000 Taiwanese firms and utilizing partial least squares, we tested the impacts of fit on BDA-related task performances. This study highlights the significance of the task-people-technology fit, and the importance of data-driven storytelling in translating BDA capabilities into BDA-related task performance.