More from these Authors

• Article | Strategic Management Journal

  Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles

  Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson, Tarun Khanna, Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson and Tarun Khanna

  We demonstrate how a novel synthesis of three methods—(1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural network algorithm—can shed light on questions related to CEO oral communication. With videos and corresponding transcripts of interviews with emerging market CEOs, we employ this synthesis of methods to discover five distinct communication styles that incorporate both verbal and nonverbal aspects of communication. Our data are comprised of interviews that represent unedited expressions and content, making them especially suitable as data sources for the measurement of an individual’s communication style. We then perform a proof-of-concept analysis, correlating CEO communication styles to M&A outcomes, highlighting the value of combining text and videographic data to define styles. We also discuss the benefits of using our methods versus current research methods.

  Keywords: CEOs; communication style; Machine learning; Spoken Communication; Nonverbal Communication; Personal Characteristics; Analysis; Performance;

  Citation:

  Choudhury, Prithwiraj, Dan Wang, Natalie A. Carlson, and Tarun Khanna. "Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles." Strategic Management Journal 40, no. 11 (November 2019): 1705–1732.  View Details

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• Article | Strategic Management Journal

  Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles

  Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson, Tarun Khanna, Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson and Tarun Khanna

  We demonstrate how a novel synthesis of three methods—(1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural network algorithm—can shed light on questions related to CEO oral communication. With videos and corresponding transcripts of interviews with emerging market CEOs, we employ this synthesis of methods to discover five distinct communication styles that incorporate both verbal and nonverbal aspects of communication. Our data are comprised of interviews that represent unedited expressions and content, making them especially suitable as data sources for the measurement of an individual’s communication style. We then perform a proof-of-concept analysis, correlating CEO communication styles to M&A outcomes, highlighting the value of combining text and videographic data to define styles. We also discuss the benefits of using our methods versus current research methods.

  Keywords: CEOs; communication style; Machine learning; Spoken Communication; Nonverbal Communication; Personal Characteristics; Analysis; Performance;

  Citation:

  Choudhury, Prithwiraj, Dan Wang, Natalie A. Carlson, and Tarun Khanna. "Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles." Strategic Management Journal 40, no. 11 (November 2019): 1705–1732.  View Details

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• Case | HBS Case Collection | January 2019 (Revised October 2019)

  Rural Taobao: Alibaba's Expansion into Rural E-Commerce

  Tarun Khanna, Ryan Allen, Adam Frost and Wesley Koo

  Alibaba's Rural Taobao initiative had been launched in 2014 as a public service initiative to increase e-commerce adoption in China’s remote rural areas. In the first two iterations of the initiative, dubbed “1.0” and “2.0,” Alibaba had partnered with local businesses and young returnees to operate village stations. Village stations served as logistical and educational hubs to help villagers make online purchases. Despite significant growth in sales through village stations, the business model was operating at a loss, and competitors JD and Pinduoduo were growing quickly, threatening to challenge Alibaba by claiming the rural e-commerce markets. Bill Wang, the head of the initiative, was tasked with designing and implementing the next model for Alibaba’s future expansion into rural areas. The decision centers around whether Alibaba should copy the successes of competitors JD and Pinduoduo, escalate commitment to its original value proposition (hands-on service to villagers), or embrace an entirely new model. The case highlights the competing pressures of financial performance, CSR and corporate mission, government relations, and long-term competitive positioning that are common to major strategic decisions.

  Keywords: strategy; Business growth; ecommerce; corporate social responsibility; Business and Government; emerging market; Market Platforms; Online Technology; Emerging Markets; Rural Scope; Growth and Development Strategy; Competitive Strategy; Corporate Social Responsibility and Impact; Business and Government Relations; Decision Making; China;

  Citation:

  Khanna, Tarun, Ryan Allen, Adam Frost, and Wesley Koo. "Rural Taobao: Alibaba's Expansion into Rural E-Commerce." Harvard Business School Case 719-433, January 2019. (Revised October 2019.)  View Details

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