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Ashley Kristin Palmarozzo

Ashley Kristin Palmarozzo

Doctoral Student

Doctoral Student

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Ashley Palmarozzo is a graduating doctoral candidate in the Technology and Operations Management (TOM) unit at Harvard Business School. Her research uses econometric methods to understand how non-financial audit program design features influence the effectiveness of those programs. She has presented her research at several academic conferences (e.g., Academy of Management, INFORMS, and POMS) and to industry executives. In addition to this line of research, she is broadly interested in the implications of corporate social responsibility and climate risk on firm strategy and business outcomes. 

Prior to joining TOM, she worked for over three years as a senior research analyst in the energy practice area at The Brattle Group, an economic consulting firm, where she managed a long-term capacity planning optimization model used to forcast electricity prices under different energy policy scenarios. Ashley graduated magna cum laude from Villanova University in 2013 with a B.S. in Mathematics and Economics.
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Ashley Kristin Palmarozzo
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Research Summary Teaching
Overview
Downstream businesses that utilize global suppliers frequently use auditing programs to monitor their suppliers’ working conditions and are often deployed to address reputational concerns associated with procuring from unregulated suppliers. Despite their widespread use, it is unclear how audit program design decisions influence the effectiveness of those programs. My research explores features of audit programs that may affect program effectiveness.

First, I document the audit quality effects of four different auditor sourcing strategies. Using proprietary data from a global apparel brand, I find that insourced auditors yield higher quality audits, by recording more audit violations, than those done by outsourced auditors, and that third-party audit quality increases with the use of concurrent sourcing or rotational sourcing.

I also explore how differences in audit format can influence audit quality in the context of management system standard audits. This research finds that audits are of lower quality (resulting in fewer violations) when conducted remotely than when conducted in-person, possibly because remote auditors face greater difficulty obtaining information to document violations. It also finds that the remote audit quality decrement increases with multi-auditor teams and decreases when teams have more prior in-person site visits.

Finally, I identify two aspects of the buyer-supplier’s economic relationship that may influence the resolution of violations in supplier working conditions. I finds that the odds of violation resolution increase when a greater portion of supplier output goes to the buyer that is auditing them, and when there are more alternative suppliers for the buyer to choose from.
Keywords: Audit Quality; Remote Work; Outsourcing; Econometric Analysis; Switching Costs; Service Operations; Performance Effectiveness; Retail Industry; Service Industry
Ashley Palmarozzo is a graduating doctoral candidate in the Technology and Operations Management (TOM) unit at Harvard Business School. Her research uses econometric methods to understand how non-financial audit program design features influence the effectiveness of those programs. She has presented her research at several academic conferences (e.g., Academy of Management, INFORMS, and POMS) and to industry executives. In addition to this line of research, she is broadly interested in the implications of corporate social responsibility and climate risk on firm strategy and business outcomes. 

Prior to joining TOM, she worked for over three years as a senior research analyst in the energy practice area at The Brattle Group, an economic consulting firm, where she managed a long-term capacity planning optimization model used to forcast electricity prices under different energy policy scenarios. Ashley graduated magna cum laude from Villanova University in 2013 with a B.S. in Mathematics and Economics.
Research Summary
Overview
Downstream businesses that utilize global suppliers frequently use auditing programs to monitor their suppliers’ working conditions and are often deployed to address reputational concerns associated with procuring from unregulated suppliers. Despite their widespread use, it is unclear how audit program design decisions influence the effectiveness of those programs. My research explores features of audit programs that may affect program effectiveness.

First, I document the audit quality effects of four different auditor sourcing strategies. Using proprietary data from a global apparel brand, I find that insourced auditors yield higher quality audits, by recording more audit violations, than those done by outsourced auditors, and that third-party audit quality increases with the use of concurrent sourcing or rotational sourcing.

I also explore how differences in audit format can influence audit quality in the context of management system standard audits. This research finds that audits are of lower quality (resulting in fewer violations) when conducted remotely than when conducted in-person, possibly because remote auditors face greater difficulty obtaining information to document violations. It also finds that the remote audit quality decrement increases with multi-auditor teams and decreases when teams have more prior in-person site visits.

Finally, I identify two aspects of the buyer-supplier’s economic relationship that may influence the resolution of violations in supplier working conditions. I finds that the odds of violation resolution increase when a greater portion of supplier output goes to the buyer that is auditing them, and when there are more alternative suppliers for the buyer to choose from.
Keywords: Audit Quality; Remote Work; Outsourcing; Econometric Analysis; Switching Costs; Service Operations; Performance Effectiveness; Retail Industry; Service Industry
Teaching
Overview
I served as a Teaching Fellow for the Applied Business Analytics second-year MBA course. This course sought to teach MBA students how businesses can improve their strategic decisions using statistics and machine learning techniques. (e.g., regression models, random forest, nerual nets, natural language processing). My role primarily consisted of leading weekly coding labs to develop and test students' understanding of the material using a variety of software and languages (e.g., R, SQL, Tableau, Python).
Additional Information
  • LinkedIn
Area of Study
  • Technology and Operations Management
  • Innovation
Areas of Interest
  • behavioral operations
  • corporate social responsibility
  • innovation
  • service quality
  • strategy
Additional Information
LinkedIn

Area of Study

Technology and Operations Management
Innovation

Areas of Interest

behavioral operations
corporate social responsibility
innovation
service quality
strategy
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