Daniel C. Snow

Visiting Associate Professor of Business Administration

Daniel Snow is back at Harvard Business School where he will be teaching an elective course entitled “Supply Change Management” in the fall of 2016. Professor Snow was on the faculty of the Harvard Business School from 2004-2010, where he taught the core MBA course in Technology and Operations Management, and he co-developed and taught the Operations Strategy elective course. In 2010 he joined the faculty of the Marriott School of Management at Brigham Young University. He has served as a visiting lecturer at Wharton, Dartmouth, and Oxford University. He has taught in executive education programs at HBS, including Building Competitive Advantage through Operations (BCAO), Leading Product Innovation (LPI), and Senior Executive Program, China (SEPC). He has also taught executive education programs at UCLA, Tsinghua University, Chalmers University, University of Xiamen, Universidad de La Sabana (Bogotá), and others.

Professor Snow's research addresses two areas. The first seeks to improve our understanding of technological innovation, and specifically of the complex relationship between new and old technologies that exists during technology transitions. 

His second area of research is in Service Operations—both in the building of theoretical microfoundations to help define the field, and in empirical research on the impact of IT on service employee productivity.

He holds a Ph.D. from the University of California, Berkeley’s Haas School of Business and an MBA from BYU's Marriott School of Management. He has worked for Ford Motor Company as a financial analyst, and he advises firms facing decisions about Research and Development, technology transitions, and the issues associated with new technology adoption. He serves on the Boards of Directors of Ceramic Process Systems (NASDAQ: CPS-H) and the South Region of Intermountain Healthcare.

Journal Articles

  1. A Detailed Analysis of the Reduction Mammaplasty Learning Curve: A Statistical Process Model for Approaching Surgical Performance Improvement

    Matthew Carty MD, Rodney Chan, Robert S. Huckman, Daniel C. Snow and Dennis Orgill

    Background: The increased focus on quality and efficiency improvement within academic surgery has met with variable success among plastic surgeons. Traditional surgical performance metrics, such as morbidity and mortality, are insufficient to improve the majority of today's plastic surgical procedures. In-process analyses that allow rapid feedback to the surgeon based on surrogate markers may provide a powerful method for quality improvement.

    Methods: The authors reviewed performance data from all bilateral reduction mammaplasties performed at their institution by eight surgeons between 1995 and 2007. Multiple linear regression analyses were conducted to determine the relative impact of key factors on operative time. Explanatory learning curve models were generated, and complication data were analyzed to elucidate clinical outcomes and trends.

    Results: A total of 1,068 procedures were analyzed. The mean operative time for bilateral reduction mammaplasty was 134 ± 34 minutes, with a mean operative experience of 11 ± 4.7 years and total resection volume of 1,680 ± 930 g. Multiple linear regression analyses showed that operative time (R = 0.57) was most closely related to surgeon experience and resection volume. The complication rate diminished in a logarithmic fashion with increasing surgeon experience and in a linear fashion with declining operative time.

    Conclusions: The results of this study suggest a three-phase learning curve in which complication rates, variance in operative time, and operative time all decrease with surgeon experience. In-process statistical analyses may represent the beginning of a new paradigm in academic surgical quality and efficiency improvement in low-risk surgical procedures.

    Keywords: Experience and Expertise; Health Care and Treatment; Medical Specialties; Outcome or Result; Performance Efficiency; Performance Improvement;


    Carty, Matthew, MD, Rodney Chan, Robert S. Huckman, Daniel C. Snow, and Dennis Orgill. "A Detailed Analysis of the Reduction Mammaplasty Learning Curve: A Statistical Process Model for Approaching Surgical Performance Improvement." Plastic and Reconstructive Surgery 124, no. 3 (September 2009): 706–714. View Details

Working Papers

  1. Cleaning House: The Impact of Information Technology on Employee Corruption and Performance

    Lamar Pierce, Daniel Snow and Andrew McAfee

    This paper examines how firm investments in technology-based employee monitoring impact both misconduct and productivity. We use unique and detailed theft and sales data from 392 restaurant locations from five firms that adopt a theft monitoring information technology (IT) product. We use difference-in-differences (DD) models with staggered adoption dates to estimate the treatment effect of IT monitoring on theft and productivity. We find significant treatment effects in reduced theft and improved productivity that appear to be primarily driven by changed worker behavior rather than worker turnover. We examine four mechanisms that may drive this productivity result: economic and cognitive multitasking, fairness-based motivation, and perceived increases of general oversight. The observed productivity results represent substantial financial benefits to both firms and the legitimate tip-based earnings of workers. Our results suggest that employee misconduct is not solely a function of individual differences in ethics or morality, but can also be influenced by managerial policies that can benefit both firms and employees.

    Keywords: Management Practices and Processes; Information Technology; Ethics; Performance Productivity; Employees;


    Pierce, Lamar, Daniel Snow, and Andrew McAfee. "Cleaning House: The Impact of Information Technology on Employee Corruption and Performance." MIT Sloan Research Paper, No. 5029-13, October 2014. View Details