Technology and Analytics in Emerging Markets (TAEM)
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Item Detection of Interaction-based Knowledge for Reclassification of Service Robots: Big Data Analytics Perspective(2023-01-03) Lyu, Fang; Wang, Ming; Choi, JaewonWith the advancement of artificial intelligence technology, the robot industry in human- robot interactive service has rapidly developed. The purpose of this paper is to uncover user acceptance of human-robot interactive service robots based on online reviews. Extract reviews the public service robots and the domestic service robots from YouTube uses word2vec, sentiment classification, and LDA (Latent Dirichlet Allocation) analysis methods for research. The results show that in the interactive technology, the public service robots, the domestic service robots, and the service robots can well receive the user’s speech, gestures, and understanding of emotional states and navigating with and around. However, collaborating with humans, users may be more fearful and worried. At the same time, the positive topic of the public service robots is experience value, and the negative topic is system quality. The positive topic of the domestic service robots is anthropomorphism, and the negative topic is perceived intelligence.Item Introduction to the Minitrack on Technology and Analytics in Emerging Markets (TAEM)(2023-01-03) Park, Sungho; Han, Sang Pil; Oh, Wonseok; Lee, Gene MooItem What's to Automate? A Task Analysis of AI-enabled Start-ups(2023-01-03) Schulte-Althoff, MatthiasAutomation of tasks as a result of advances in Artificial Intelligence (AI) is currently one of the major economical drivers. However, the varying effectiveness of AI usage across occupations and industries suggests that the impact of AI diffusion is uneven. Thus, it is imperative to understand which types of tasks are more or less prevalent in AI-enabled businesses. Using a cross-sectional dataset of 27,700 start-ups and occupation data, we utilize word embedding to link start-ups to their respective underlying tasks. We compare the task types of AI-enabled with non-AI start-ups in the services and platforms domain using a suitability for machine learning metric. The results show that analytical, logistical, and statistical tasks predominate among AI-enabled start-ups while services with customer proximity have a smaller share and the overall task diversity is lower. The implications of our findings are discussed in the light of labor theory and the economies of scale of AI start-ups.Item Conversation Analytics: Can Machines Read between the Lines in Real-Time Strategic Conversations?(2023-01-03) Chen, Yanzhen; Rui, Huaxia; Whinston, AndrewStrategic conversations involve one party with an informational advantage and the other with an interest in the information. This paper proposes machine-learning based measures to quantify the degrees of evasiveness and incoherence of the informed party during real-time strategic conversations. The specific empirical context is the questions and answers (Q&A) part of earnings conference calls during which managers endure high pressure as they face analysts’ scrutinizing questions. Being reluctant to disclose adverse information, managers may resort to evasive answers and sometimes respond less coherently due to increased cognitive load. Using data from the earnings calls of the S&P 500 companies from 2006 to 2018, we show that the proposed measures predict worse next-quarter earnings. Moreover, the stock market perceives incoherence as a negative signal. This paper contributes methodologically by developing two novel machine-powered measures to automatically evaluate behavioral cues during real-time strategic conversations. The proposed analytical tools are particularly beneficial to resource-constrained and informationally disadvantaged parties such as retail investors who may not be able to effectively trade on signals buried deep in unstructured conversational data.