Problem resolution with business analytics: a task-technology fit perspective
本研究基于任务-技术匹配理论，通过12场半结构化访谈和模式匹配分析，探讨商业分析如何解决商业问题，发现迭代应用能提升问题理解与解决方案，并指出过匹配与欠匹配均可接受。
Purpose The study explores the extent to which business analytics can address business problems using the task-technology fit theory. Design/methodology/approach The qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes from the empirical data. Findings The study establishes the relationships between various forms of fit, arguing that the iterative application of business analytics improves problem understanding and solutions, and contends that both under-fit and over-fit can be acceptable due to the increasing costs of achieving ideal fit and potential unaffected outcomes, respectively. The study demonstrates that managers should appreciate that there may be a distinction between those who create business analytics solutions and those who apply business analytics solutions to solve problems. Originality/value Extant studies on business analytics have not focused on how the match between business analytics and tasks affects the level to which problems can be addressed that determines business value. This study enriches the literature on business analytics by linking business analytics and business value through problem resolution demonstrated by task-technology fit. To the authors’ knowledge, this study might be the first to apply pattern matching to study the fit between technology and tasks.