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    • All HBS Web  (2,579)
      • Faculty Publications  (55)

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      Spreadsheet Supplement to Artea Teaching Note
      Artea (D): Discrimination through Algorithmic Bias in Targeting
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      • 2021
      • Working Paper

      Time Dependency, Data Flow, and Competitive Advantage

      By: Ehsan Valavi, Joel Hestness, Marco Iansiti, Newsha Ardalani, Feng Zhu and Karim R. Lakhani
      Data is fundamental to machine learning-based products and services and is considered strategic due to its externalities for businesses, governments, non-profits, and more generally for society. It is renowned that the value of organizations (businesses, government...  View Details
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      Valavi, Ehsan, Joel Hestness, Marco Iansiti, Newsha Ardalani, Feng Zhu, and Karim R. Lakhani. "Time Dependency, Data Flow, and Competitive Advantage." Harvard Business School Working Paper, No. 21-099, March 2021.
      • March 2021
      • Supplement

      Artea (A), (B), (C), and (D): Designing Targeting Strategies

      By: Eva Ascarza and Ayelet Israeli
      Power Point Supplement to Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on...  View Details
      Keywords: Targeted Advertising; Targeting; Algorithmic Data; Bias; A/b Testing; Experiment; Gender; Race; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School PowerPoint Supplement 521-719, March 2021.
      • March 2021
      • Case

      VideaHealth: Building the AI Factory

      By: Karim R. Lakhani and Amy Klopfenstein
      Florian Hillen, co-founder and CEO of VideaHealth, a startup that used artificial intelligence (AI) to detect dental conditions on x-rays, spent the early years of his company laying the groundwork for an AI factory. A process for quickly building and iterating on new...  View Details
      Keywords: Artificial Intelligence; Innovation and Invention; Disruptive Innovation; Technological Innovation; Technology; Software; Technology Adoption; Technology Platform; Entrepreneurship; Technology Industry; Medical Devices and Supplies Industry; North and Central America; United States; Massachusetts; Cambridge
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      Lakhani, Karim R., and Amy Klopfenstein. "VideaHealth: Building the AI Factory." Harvard Business School Case 621-021, March 2021.
      • January 2021
      • Case

      Anodot: Autonomous Business Monitoring

      By: Antonio Moreno and Danielle Golan
      Autonomous business monitoring platform Anodot leveraged machine learning to provide real-time alerts regarding business anomalies. Anodot’s solution was used in various industries in order to primarily monitor business health, such as revenue and payments, product...  View Details
      Keywords: Technology Platform; Online Technology; Knowledge Sharing; Information Management; Sales; Value Creation; Product Positioning; Israel
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      Moreno, Antonio, and Danielle Golan. "Anodot: Autonomous Business Monitoring." Harvard Business School Case 621-084, January 2021.
      • January 2021 (Revised March 2021)
      • Case

      THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)

      By: Jill Avery, Ayelet Israeli and Emma von Maur
      THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on...  View Details
      Keywords: Data; Data Analytics; Artificial Intelligence; Ai; Ai Algorithms; Ai Creativity; Fashion; Retail; Retail Analytics; Digital Marketing; E-commerce; E-commerce Strategy; Platform; Platforms; Big Data; Preference Elicitation; Preference Prediction; Predictive Analytics; App Development; "marketing Analytics"; Advertising; Mobile App; Mobile Marketing; Apparel; Referral Rewards; Referrals; Female Ceo; Female Entrepreneur; Female Protagonist; Data and Data Sets; Analysis; Creativity; Marketing Strategy; Brands and Branding; Consumer Behavior; Demand and Consumers; Forecasting and Prediction; Marketing Channels; Online Advertising; Online Technology; Mobile Technology; Fashion Industry; Retail Industry; Apparel and Accessories Industry; Consumer Products Industry; United States
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      Avery, Jill, Ayelet Israeli, and Emma von Maur. "THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)." Harvard Business School Case 521-070, January 2021. (Revised March 2021.)
      • January 2021
      • Article

      Machine Learning for Pattern Discovery in Management Research

      By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
      Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect...  View Details
      Keywords: Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; Roc Curve; Confusion Matrix; Partial Dependence Plots
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      Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Strategic Management Journal 42, no. 1 (January 2021): 30–57.
      • Article

      Soul and Machine (Learning)

      By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
      Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to...  View Details
      Keywords: Machine Learning; Marketing Applications; Knowledge; Technological Innovation; Core Relationships; Marketing
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      Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Marketing Letters 31, no. 4 (December 2020): 393–404.
      • September 2020 (Revised March 2021)
      • Exercise

      Artea: Designing Targeting Strategies

      By: Eva Ascarza and Ayelet Israeli
      This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...  View Details
      Keywords: Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; "marketing Analytics"; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analytics; Data Analysis; E-commerce; E-commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Targeting; Pricing Algorithms; A/b Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; Retail Industry; Apparel and Accessories Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised March 2021.)
      • September 2020
      • Exercise

      Artea (B): Including Customer-level Demographic Data

      By: Eva Ascarza and Ayelet Israeli
      This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...  View Details
      Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Demographics; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Artea (B): Including Customer-level Demographic Data." Harvard Business School Exercise 521-022, September 2020.
      • September 2020
      • Exercise

      Artea (C): Potential Discrimination through Algorithmic Targeting

      By: Eva Ascarza and Ayelet Israeli
      This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...  View Details
      Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Artea (C): Potential Discrimination through Algorithmic Targeting." Harvard Business School Exercise 521-037, September 2020.
      • September 2020 (Revised March 2021)
      • Exercise

      Artea (D): Discrimination through Algorithmic Bias in Targeting

      By: Eva Ascarza and Ayelet Israeli
      This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...  View Details
      Keywords: Targeted Advertising; Discrimination; Algorithmic Data; Bias; Advertising; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Artea (D): Discrimination through Algorithmic Bias in Targeting." Harvard Business School Exercise 521-043, September 2020. (Revised March 2021.)
      • September 2020
      • Supplement

      Spreadsheet Supplement to Artea (B) and (C)

      By: Eva Ascarza and Ayelet Israeli
      Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting"  View Details
      Keywords: Gender; Race; Diversity; Marketing; Customer Relationship Management; Demographics; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Spreadsheet Supplement to Artea (B) and (C)." Harvard Business School Spreadsheet Supplement 521-704, September 2020.
      • September 2020 (Revised December 2020)
      • Teaching Note

      Artea (A), (B), (C), and (D): Designing Targeting Strategies

      By: Eva Ascarza and Ayelet Israeli
      Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and...  View Details
      Keywords: Targeted Advertising; Targeting; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School Teaching Note 521-041, September 2020. (Revised December 2020.)
      • September 2020 (Revised March 2021)
      • Supplement

      Spreadsheet Supplement to Artea Teaching Note

      By: Eva Ascarza and Ayelet Israeli
      Spreadsheet Supplement to Artea Teaching Note 521-041. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and...  View Details
      Keywords: Targeted Advertising; Algorithmic Data; Bias; Advertising; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Spreadsheet Supplement to Artea Teaching Note." Harvard Business School Spreadsheet Supplement 521-705, September 2020. (Revised March 2021.)
      • September 2020
      • Case

      True North: Pioneering Analytics, Algorithms and Artificial Intelligence

      By: Karim R. Lakhani, Kairavi Dey and Hannah Mayer
      True North was a private equity fund that specialized in the growth and buyout of mid-market, India-centric companies. The leadership team initially believed that technology was not core to traditional businesses and steered clear of new age technology-oriented...  View Details
      Keywords: Artificial Intelligence; Technology; Management; Operations; Organizations; Leadership; Innovation and Invention; Business Model; Computer Industry; Technology Industry
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      Lakhani, Karim R., Kairavi Dey, and Hannah Mayer. "True North: Pioneering Analytics, Algorithms and Artificial Intelligence." Harvard Business School Case 621-042, September 2020.
      • Article

      Conversational Receptiveness: Expressing Engagement with Opposing Views

      By: M. Yeomans, J. Minson, H. Collins, H. Chen and F. Gino
      We examine “conversational receptiveness”—the use of language to communicate one’s willingness to thoughtfully engage with opposing views. We develop an interpretable machine-learning algorithm to identify the linguistic profile of receptiveness (Studies 1A-B). We then...  View Details
      Keywords: Receptiveness; Natural Language Processing; Disagreement; Interpersonal Communication; Relationships; Conflict Management
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      Yeomans, M., J. Minson, H. Collins, H. Chen, and F. Gino. "Conversational Receptiveness: Expressing Engagement with Opposing Views." Organizational Behavior and Human Decision Processes 160 (September 2020): 131–148.
      • 2020
      • Working Paper

      Time and the Value of Data

      By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
      This paper investigates the effectiveness of time-dependent data in improving the quality of AI-based products and services. Time-dependency means that data loses its relevance to problems over time. This loss causes deterioration in the algorithm's performance and,...  View Details
      Keywords: Economics Of Ai; Machine Learning; Non-stationarity; Perishability; Value Depreciation
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      Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020.
      • August 2020 (Revised March 2021)
      • Supplement

      Migros Turkey: Scaling Online Operations (B)

      By: Antonio Moreno and Gamze Yucaoglu
      The case opens in February 2020 as Ozgur Tort and Mustafa Bartin, CEO and chief large-format and online retail officer of Migros Ticaret A.S. (Migros), Turkey’s oldest and one of its largest supermarket chains, are looking over the results of the fulfillment pilot the...  View Details
      Keywords: Grocery; Business Model; Strategy; Technology Platform; Information Technology; Technology Adoption; Value Creation; Globalization; Competition; Expansion; Logistics; Profit; Resource Allocation; Corporate Strategy; Retail Industry; Turkey
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      Moreno, Antonio, and Gamze Yucaoglu. "Migros Turkey: Scaling Online Operations (B)." Harvard Business School Supplement 621-027, August 2020. (Revised March 2021.)
      • Article

      Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

      By: Michael G. Endres, Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland and Robert A. Gaudin
      Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts, and tumors. In this study, we seek to investigate the...  View Details
      Keywords: Artificial Intelligence; Diagnosis; Computer-assisted; Image Interpretation; Machine Learning; Radiography; Panoramic Radiograph
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      Endres, Michael G., Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland, and Robert A. Gaudin. "Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs." Diagnostics 10, no. 6 (June 2020).
      • April 2020
      • Case

      Ment.io: Knowledge Analytics for Team Decision Making

      By: Yael Grushka-Cockayne, Jeffrey T. Polzer, Susie L. Ma and Shlomi Pasternak
      Ment.io was a software platform that used proprietary data analytics technology to help organizations make informed and transparent decisions based on team input. Ment was born out of founder Joab Rosenberg’s frustration that, while organizations collected ever...  View Details
      Keywords: Decision Making; Technology; Knowledge; Knowledge Acquisition; Knowledge Management; Operations; Information Management; Product; Product Development; Entrepreneurship; Business Startups; Communications Industry; Information Industry; Information Technology Industry; Web Services Industry; Middle East; Israel
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      Grushka-Cockayne, Yael, Jeffrey T. Polzer, Susie L. Ma, and Shlomi Pasternak. "Ment.io: Knowledge Analytics for Team Decision Making." Harvard Business School Case 420-078, April 2020.
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      Spreadsheet Supplement to Artea Teaching Note
      Artea (D): Discrimination through Algorithmic Bias in Targeting
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