Ernst Cassirer and the Symbolic Foundation of Institutions

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Abstract

In this Counterpoint, we introduce a conceptualization of the symbol that constructively contrasts the ideas presented by Phillips and Moser. We do not see the need to mobilize ideas and vocabularies from evolutionary biology, as they do, but instead propose to return to cultural approaches to the symbol that resonate more deeply and profoundly within our discipline. Specifically, we revisit the work of German philosopher Ernst Cassirer on the symbolic foundation of culture and society. To fully harness the potential of such a renewed approach in organization research, we encourage a conversation with foundational and more recent work in institutional organization theory. The aims of our article are to (a) offer an alternative understanding of the symbol; and (b) elaborate how such understanding can reinvigorate organizational and institutional analysis.

Organizations as Algorithms: A New Metaphor for Advancing Management Theory

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Abstract

According to the ‘Point’ essay, management research's reliance on corporate data threatens to replace objective theory with profit-biased ‘corporate empiricism’, undermining the scientific and ethical integrity of the field. In this ‘Counterpoint’ essay, we offer a more expansive understanding of big data and algorithmic processing and, by extension, see promising applications to management theory. Specifically, we propose a novel management metaphor: organizations as algorithms. This metaphor offers three insights for developing innovative, relevant, and grounded organization theory. First, agency is distributed in assemblages rather than being solely attributed to individuals, algorithms, or data. Second, machine-readability serves as the immutable and mobile base for organizing and decision-making. Third, prompting and programming transform the role of professional expertise and organizational relationships with technologies. Contrary to the ‘Point’ essay, we see no theoretical ‘end’ in sight; the organization as algorithm metaphor enables scholars to build innovative theories that account for the intricacies of algorithmic decision-making.