The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Service with Emoticons: How Customers Interpret Employee Use of Emoticons in Online Service Encounters
Abstract
Virtually no research has examined the role of emoticons in commercial relationships, and research outside the marketing domain reports mixed findings. This article aims to resolve these mixed findings by considering that emoticon senders are often simultaneously evaluated on two fundamental dimensions, warmth and competence, and the accessibility of one dimension over the other is critically contingent on salient relationship norms (communal vs. exchange norms) in customers’ minds due to individual and situational factors. Through laboratory and field experiments, the current research shows that customers perceive service employees who use emoticons as higher in warmth but lower in competence compared to those who do not (study 1). We further demonstrate that when a service employee uses emoticons, communal-oriented (exchange-oriented) customers are more likely to infer higher warmth (lower competence) and thus to be more (less) satisfied with the service (study 2). We also examine two practically important service situations that can make a certain type of relationship norm more salient: unsatisfactory services (study 3) and employees’ extra-role services (study 4). We speculate on possible mechanisms underlying these effects and discuss theoretical and practical implications along with opportunities for future research.
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SPSS and SAS procedures for estimating indirect effects in simple mediation models
Preacher, K. J., & Hayes, A. F. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03206553 · Cited by 17,383
Reconsidering Baron and Kenny: myths and truths about mediation analysis
Zhao, X., Lynch, J. G., & Chen, Q. (2010) · Journal of Consumer Research · doi:10.1086/651257 · Cited by 12,583
Universal dimensions of social cognition: warmth and competence
Fiske, S. T., Cuddy, A. J. C., & Glick, P. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.11.005 · Cited by 4,567
A Model of Customer Satisfaction with Service Encounters Involving Failure and Recovery
Smith, A. K., Bolton, R. N., & Wagner, J. (1999) · Journal of Marketing Research · doi:10.2307/3152082 · Cited by 2,058
Madge, C., Meek, J., Wellens, J., & Hooley, T. (2009) · Learning, Media and Technology · doi:10.1080/17439880902923606 · Cited by 1,157
Cited by
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To Err Is Human(-oid): How Do Consumers React to Robot Service Failure and Recovery?
Choi, S., Mattila, A. S., & Bolton, L. E. (2021) · Journal of Service Research · doi:10.1177/1094670520978798 · Cited by 502
Bots with Feelings: Should AI Agents Express Positive Emotion in Customer Service?
Han, E., Yin, D., & Zhang, H. (2023) · Information Systems Research · doi:10.1287/isre.2022.1179 · Cited by 307
How may I help you? Driving brand engagement through the warmth of an initial chatbot message
Kull, A. J., Romero, M., & Monahan, L. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.03.005 · Cited by 237
Engagement in digital interventions.
Nahum-Shani, I., Shaw, S. D., Carpenter, S. M., Murphy, S. A., & Yoon, C. (2022) · American Psychologist · doi:10.1037/amp0000983 · Cited by 177
Metadata
- Authors
- Li, X. (., Chan, K. W., & Kim, S.
- Venue
- Journal of Consumer Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-03-08
- Volume
- 45
- Issue
- 5
- Pages
- 973-987
- ISSN
- 0093-5301 (ISSN-L)
- 1537-5277
- OpenAlex
- W2908893473
- Cited by
- 268
- References
- 5
- FWCI
- 18.38
- Type
- journal article
- Language
- en
- Open access
- closed
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