定性比较分析
计算机科学
领域(数学)
因果模型
集合(抽象数据类型)
管理科学
业务
过程管理
定性研究
知识管理
独创性
社会学
经济
医学
病理
机器学习
程序设计语言
纯数学
社会科学
数学
作者
Anna Salonen,Marcus Zimmer,Joona Keränen
标识
DOI:10.1108/ijopm-08-2020-0537
摘要
Purpose The purpose of this study is to explain how the application of fuzzy-set qualitative comparative analysis (fsQCA) and experiments can advance theory development in the field of servitization by generating better causal explanations. Design/methodology/approach FsQCA and experiments are established research methods that are suited for developing causal explanations but are rarely utilized by servitization scholars. To support their application, we explain how fsQCA and experiments represent distinct ways of developing causal explanations, provide guidelines for their practical application and highlight potential application areas for a future research agenda in the servitization domain. Findings FsQCA enables specification of cause–effects relationships that result in equifinal paths to an intended outcome. Experiments have the highest explanatory power and enable the drawing of direct causal conclusions through reliance on an interventionist logic. Together, these methods provide complementary ways of developing and testing theory when the research objective is to understand the causal pathways that lead to observed outcomes. Practical implications Applications of fsQCA help to explain to managers why there are numerous causal routes to attaining an intended outcome from servitization. Experiments support managerial decision-making by providing definitive “yes/no” answers to key managerial questions that address clearly specified cause–effect relationships. Originality/value The main contribution of this study is to help advance theory development in servitization by encouraging greater methodological plurality in a field that relies primarily on the qualitative case study methodology.
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