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1 – 10 of 197Sagar Suresh Gupta and Jayant Mahajan
Introduction: Lending is an age-old concept, and Peer-to-Peer (P2P) lending is not new. The reduction in the issuing of loans by banks has made people switch from traditional to…
Abstract
Introduction: Lending is an age-old concept, and Peer-to-Peer (P2P) lending is not new. The reduction in the issuing of loans by banks has made people switch from traditional to online mode. The introduction of the online P2P lending industry is in its nascent stage of growth. As this industry is relatively new, understanding user experience, sentiments, and emotions would be helpful for the industry to innovate as per customer requirements.
Purpose: To explore the patterns in the sentiments expressed by users of ‘Cashkumar’ based on Google reviews.
Methodology: Sentiments have been analysed using user experience in risk, cost, ease of use, and loan processing time. Python application was used for sentiment analysis of Google reviews.
Findings: The sentiment analysis results showed that the average sentiment score was 0.7144, which indicates that the user sentiment towards ‘Cashkumar’ is positive. The reviews reflect that the users, especially borrowers were satisfied with the platform’s services and happy with loan processing time. The other factors – ease of use, cost, and risk – were not given much importance by users. Both lenders and borrowers faced a few issues, but the results of the lender’s sentiment analysis could not be generalised due to a smaller number of posted reviews.
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The concept of risk is often approached as if it is self-defining. Yet placing an event or activity in the category of “risk” is a categorization with consequences. Framing…
Abstract
The concept of risk is often approached as if it is self-defining. Yet placing an event or activity in the category of “risk” is a categorization with consequences. Framing normatively complex problems like immigration, terrorism, or monetary crisis as risks that require regulating suggests that certain cognitive tools are best suited for analyzing them. It suggests that the problems are measurable or quantifiable, that they lend themselves to utilitarian calculus, and that they have ascertainably correct solutions that require no value judgments. This article employs emotion theory to illustrate the difficulties with approaching normatively complex areas of governmental policy through the framework of risk regulation. It argues that interdisciplinary inquiry into the role of emotion in human behavior sheds light on how risks are assessed, prioritized, and ameliorated, on how the category of risk is constructed, and on how that categorization affects the cognitive tools and approaches we bring to normatively complex problems. The article begins with a brief discussion of behavioral law and economics, which styles itself a corrective to law and economics, but which replicates its fatal flaw: its unrealistic view of human behavior. Next it turns to two more specific problems with the standard notion of risk formulation. First, the standard notion reads out the essential role of emotion in deliberation about risk regulation and overvalues top-down expert knowledge. Second, it reads out the heuristics that erase patterns and maintain the status quo. Finally, the article will focus on two illustrative case studies, the Chicago heat wave of 1995, and Hurricane Katrina.
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Céleste M. Brotheridge and Ian Taylor
This study examines cross-cultural differences in the emotional labor performed by flight attendants working in a multi-cultural setting. There appears to be cultural variations…
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This study examines cross-cultural differences in the emotional labor performed by flight attendants working in a multi-cultural setting. There appears to be cultural variations in how workers perform emotional labor, notably in the extent to which they engage in deep acting and hide their feelings, but not in the extent to which they fake their emotional displays. The results generally suggest that collectivism, both vertical and horizontal, is associated with deep acting.
Jochen Hartmann and Oded Netzer
The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing…
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The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.
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Anat Rafaeli, Galit Bracha Yom Tov, Shelly Ashtar and Daniel Altman
Purpose: To outline recent developments in digital service delivery in order to encourage researchers to pursue collaborations with computer science, operations research, and data…
Abstract
Purpose: To outline recent developments in digital service delivery in order to encourage researchers to pursue collaborations with computer science, operations research, and data science colleagues and to show how such collaborations can expand the scope of research on emotion in service delivery.
Design/methodology/approach: Uses archived resources available at http://LivePerson.com to extract data based in genuine service conversations between agents and customers. We refer to these as “digital traces” and analyze them using computational science models.
Findings: Although we do not test significance or causality, the data presented in this chapter provide a unique lens into the dynamics of emotions in service; results that are not obtainable using traditional research methods.
Research limitations/implications: This is a descriptive study where findings unravel new dynamics that should be followed up with more research, both research using traditional experimental methods, and digital traces research that allows inferences of causality.
Practical implications: The digital data and newly developed tools for sentiment analyses allow exploration of emotions in large samples of genuine customer service interactions. The research provides objective, unobtrusive views of customer emotions that draw directly from customer expressions, with no self-report intervention and biases.
Originality/value: This is the first objective and detailed depiction of the actual emotional encounters that customers express, and the first to analyze in detail the nature and content of customer service work.
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Mu-Yen Chen, Min-Hsuan Fan, Ting-Hsuan Chen and Ren-Pao Hsieh
Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of…
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Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of documents on public and social network websites. Therefore, this chapter integrates political blogs and news articles to develop a public mood dynamic prediction model for the stock market, while referencing the behavioral finance perspective and online political community characteristics. The goal of this chapter is to apply a big data and opinion mining approach to a sentiment analysis for the relationship between political status and economic development in Taiwan. The proposed model is verified using experimental datasets collected from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news, covering the period from January 1, 2016 to June 30, 2017. The empirical results indicate the accuracy rate with which the proposed model forecasts stock prices.
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Chapman J. Lindgren, Wei Wang, Siddharth K. Upadhyay and Vladimer B. Kobayashi
Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text…
Abstract
Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.
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Concepts equip the mind with thought, provide our theories with ideas, and assign variables for testing our hypotheses. Much of contemporary research deals with narrowly…
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Concepts equip the mind with thought, provide our theories with ideas, and assign variables for testing our hypotheses. Much of contemporary research deals with narrowly circumscribed concepts, termed simple concepts herein, which are the grist for much empirical inquiry in the field. In contrast to simple concepts, which exhibit a kind of unity, complex concepts are structures of simple concepts, and in certain instances unveil meaning going beyond simple concepts or their aggregation. When expressed in hylomorphic structures, complex concepts achieve unique ontological status and serve particular explanatory capabilities. We develop the philosophical foundation for hylomorphic structures and show how they are rooted in dispositions, dispositional causality, and various mind–body trade-offs. Examples are provided for this emerging perspective on “Big concepts” or “Big Ideas.”
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