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Instructor presentation for product 8B20M214.

In late 2020, an instructor at a Canadian university received an email from a concerned student about contract cheating (i.e., a student submitting someone else’s work for credit) in her class. As the instructor read the email, many questions without obvious answers came to mind. She wondered what her professional and personal responsibilities were in dealing with the issues raised in the student’s email. In particular, she was trying to come to grips with her responsibilities to her students, her colleagues, the program she taught in, and the university and society in general. She also wondered what she could do to make this type of cheating less prevalent and how she could better detect and discourage it. How could she and her colleagues support students to prevent them from seeking help from these types of companies, which promoted cheating under the premise of tutoring? Upon further considering the issues, the instructor wondered about potential risks to herself, her students, and the university that might result from various possible courses of action. She decided that, at the very least, she would talk to her students about contract cheating at the start of her next class, but she was not sure what she would say.

In late 2020, an instructor at a Canadian university received an email from a concerned student about contract cheating (i.e., a student submitting someone else's work for credit) in her class. As the instructor read the email, many questions without obvious answers came to mind. She wondered what her professional and personal responsibilities were in dealing with the issues raised in the student's email. In particular, she was trying to come to grips with her responsibilities to her students, her colleagues, the program she taught in, and the university and society in general. She also wondered what she could do to make this type of cheating less prevalent and how she could better detect and discourage it. How could she and her colleagues support students to prevent them from seeking help from these types of companies, which promoted cheating under the premise of tutoring? Upon further considering the issues, the instructor wondered about potential risks to herself, her students, and the university that might result from various possible courses of action. She decided that, at the very least, she would talk to her students about contract cheating at the start of her next class, but she was not sure what she would say.

When managers are unable to use quantifiable time series data to make forecasts or decide on uncertainties, they can either rely on their own intuition and judgment or resort to the insights of others. The Delphi technique is a well-known forecasting technique that relies on the pooled perspectives of experts to predict uncertain quantities or the outcomes of events. This relies on polling the opinions of experts, aggregating these opinions, feeding them back to the responding experts along with their own estimates, and having them repeat their judgment calls until some level of consensus is reached. More recently, however, the opinions of many others who are not experts have been sought on a range of topics in a loose assembly of similar techniques bundled under the title of crowdsourcing. This article compares Delphi and crowdsourcing as prediction and estimation tools for managers. It notes their differences and similarities, and provides a simple tool for executives to use in deciding whether or not to use these tools, and if so, which tool or combination of them will work best in a given situation.