Semantic Algorithms Can Detect How Media Language Shapes Survey Responses in Organizational Behaviour

Abstract

Research on sense-making in organisations and on linguistic relativity suggests that speakers of the same language may use this language in different ways to construct social realities at work. We apply a semantic theory of survey response (STSR) to explore such differences in quantitative survey research. Using text analysis algorithms, we have studied how language from three media domains–the business press, PR Newswire and general newspapers–has differential explanatory value for analysing survey responses in leadership research. We projected well-known surveys measuring leadership, motivation and outcomes into large text samples from these three media domains significantly different impacts on survey responses. Business press language was best in explaining leadership-related items, PR language best at explaining organizational results and “ordinary” newspaper language seemed to explain the relationship among motivation items. These findings shed light on how different public arenas construct organizational realities in different ways, and how these differences have consequences on methodology in research on leadership.

Keywords: Leadership, Organizational Behavior, Latent Semantic Analysis, Semantic Theory of Survey Response, Media Language