ISA-INFORMS Award for
"Best Industry Studies Paper"

Background

This prize was established through an agreement about joint activities between ISA and INFORMS with the goal of developing a closer relationship that would advance the goals of both organizations. 

The goal of the ISA, as defined in its articles of incorporation, is to promote industry studies scholarship. Properly designed, the awards also can encourage a cultural shift by scholars toward explicit recognition of their engagement with industry practitioners and demonstrate the benefits of such interaction for scholarly research. It is often the case that scholars benefit from interaction with industry practitioners in ways that shape the nature of the problems they address, the methodologies employed, and/or the interpretations of their findings.  This kind of engagement is not always apparent in published journal articles. Awards for excellence in industry studies publications demonstrate the contribution of engagement with practitioners in advancing the frontiers of knowledge, and encourage more interaction at this important interface. 

Selection Criteria

Winners and Runners-up are selected based on the following criteria:

1. The paper’s level of analysis can be individual, group, organizational, industry, or cross-industry, but it
    must explore and provide insight into issues of significance at the industry level of analysis.
2. The paper must be based on field research, defined as “the systematic study of original data –
    qualitative or quantitative – gathered from real organizations.” Original data may include: interviews,
    field experiments, quantitative data collected in the field, participant observation, or contextual
    understanding of an industry gained through long involvement with the industry.
3. Even where there is evidence that the authors understand the workings of the industry in a
    grounded, contextualized way
, it is essential that this understanding is also communicated explicitly to
    the reader, i.e. it must appear in the article and not solely reside in the author’s head.
4. Research using analytical modeling or statistical analysis of secondary datasets must address
    research questions that have been motivated by in-depth, firm- or industry-specific observations made
    by the authors. Authors must demonstrate a deep understanding of their research context through rich
    phenomenological descriptions, make extensive use of empirical data, and demonstrate methodological rigor.

Previous Winners

2013

Winner: “Selection at the Gate: Difficult Cases, Spillovers, and Organizational Learning; by Mihaela Stan and Freek Vermeulen, Organization Science

Runner-up #1: “Earnings Effect of Entrepreneurial Activity: Evidence from the Semiconductor Industry by Benjamin Campbell, Management Science.  

Runner-up #2: “Organization and Bargaining: Sales Process Choice at Auto Dealerships by Victor Bennett, Management Science

Runner-up #3: “Optimal Bidding in Multi-Item Multislot Sponsored Search Auctions by Vibhanshu Abhishek and Kartik Hosanagar, Operations Research

2012

Winner: “Process Management Impact on Clinical and Experiential Quality: Managing Tensions Between Safe and Patient-Centered Healthcare” by Aravind Chandrasekaran, Claire Senot, and Kenneth Boyer, Manufacturing and Service Operations Management

Runner-up #1: “Equitable and Ef´Čücient Coordination in Traffic Flow Management,” by Cynthia Barnhart, Dimitris Bertsimas, Constantine Caramanis, and Douglas Fearing, Transportation Science.  

Runner-up #2: “What Firms Make vs. What They Know: How Firms' Production and Knowledge Boundaries Affect Competitive Advantage in the Face of Technological Change,” by Rahul Kapoor and Ron Adner, Organization Science

2011

Winner:  “Anticipatory Sorting and Gender Segregation in Temporary Employment” by Isabel Fernandez-Mateo (London Business School) and Zella King (U. of Reading), Management Science 57 (6) 989–1008.

Runner-up: “Product Customization and Customer Service Costs: An Empirical Analysis” by Anuj Kumar and Rahul Telang (both at Carnegie-Mellon University), Manufacturing and Service Operations Management 13(3) 347–360.