Object Salience in the Division of Labor: Experimental Evidence

Marlo Raveendran (), Phanish Puranam () and Massimo Warglien ()
Additional contact information
Marlo Raveendran: University of California, Riverside, Riverside, California 92521
Phanish Puranam: INSEAD, Singapore 138676
Massimo Warglien: Department of Management, Ca’Foscari University of Venice, 30121 Venice, Italy

Management Science, 2016, vol. 62, issue 7, 2110-2128

Abstract: When we engage in the process of division of labor, there are typically multiple alternatives, but insufficient knowledge to choose among them. Under such conditions, we propose that not all alternatives are equally likely to be pursued. In particular, when we engage in the process of division of labor for novel and nonrepetitive production, we argue that we display a tendency to perceive and select object-based task partitions over activity-based partitions. We experimentally investigate how the salience of objects over activities manifests itself in individuals and groups engaged in division of labor for the assembly of strongly or weakly decomposable products. We draw implications for organization design as well as the impact of technological change on organizations. This paper was accepted by Jesper Sørensen, organizations .

Keywords: organization design; division of labor; decomposability; experiments (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2015.2216 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:62:y:2016:i:7:p:2110-2128

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().