Analyzing electronic word of mouth: A social commerce construct |
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| Institution: | 1. Department of Business Administration, Earl G. Graves School of Business and Management, Morgan State University, 1700 East Cold Spring Lane, Baltimore, MD, 21251, USA;2. Department of Marketing, John Molson School of Business, Concordia University, 1455 de Maisonneuve West, Montréal, Québec, H3G 1M8, Canada;1. Lecturer in Marketing, School of Business and Management, Queen Mary University of London, Mile End, London E1 4NS, UK;2. Goldman Chair of Innovation, Newcastle University Business School, 5 Barrack Road, Newcastle upon Tyne NE1 4SE, UK;3. Head of Supply Chain Research Centre, Cranfield School of Management, Cranfield, Bedford MK43 0AL, UK;1. Department of Aviation and Supply Chain Management, Raymond J. Harbert College of Business, Auburn University, 403 Lowder Business Building, 405 W. Magnolia Ave, Auburn, AL 36849, USA;2. Graduate Institute of Technology, Innovation & Intellectual Property Management, National Chengchi University, Taiwan, ROC |
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| Abstract: | Due to the proliferation of Web 2.0 technology, e-commerce has evolved into social commerce. In this social commerce era, consumers are increasingly dependent on each other and look for social support (informational and emotional) online even before making purchases. This study examines the content of consumer reviews, a fundamental construct of social commerce. Topics expressed in consumer reviews (collected from Amazon.com) are explored using a machine learning technique (i.e. latent semantic analysis). This study documents the thematic differences between positive and negative reviews and finds that negative reviews report service-related failures while positive reviews relate more to the product, among other things. Next, the informational support aspect of social commerce is explored by identifying the topics expressed in reviews that are helpful in purchase decisions. The findings demonstrate that potential customers (i.e. those who would like to purchase a product in the near future and currently are reading reviews with the intention to decide whether or not to buy that product) find the negative reviews containing service failure information and the positive reviews containing information on core functionalities, technical aspects, and aesthetics to be more helpful. Theoretical and managerial implications are discussed. |
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| Keywords: | Social commerce Reviews Informational support eWOM Latent semantic analysis |
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