The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Service Personnel, Technology, and Their Interaction in Influencing Customer Satisfaction*
Abstract
Managing both the technologies and the personnel needed for providing high‐quality, multichannel customer support creates a complex and persistent operational challenge. Adding to this difficulty, it is still unclear how service personnel and these new communication technologies interact to influence the customer's perceptions of the service being provided. Motivated by both practical importance and inconsistent findings in the academic literature, this exploratory research examines the interaction of media richness, represented by three different technology contexts (telephone, e‐mail, and online chat), with six customer service representative (CSR) characteristics and their influences on customer satisfaction. Using a large‐sample customer survey data set, the article develops a multigroup structural equation model to analyze these interactions. Results suggest that CSR characteristics influence customer service satisfaction similarly across all three technology‐mediated contexts. Of the characteristics studied, service representatives contribute to customer satisfaction more when they exhibit the characteristics of thoroughness, knowledgeableness, and preparedness, regardless of the richness of the medium used. Surprisingly, while three other CSR characteristics studied (courtesy, professionalism, and attentiveness) are traditionally believed to be important in face‐to‐face encounters, they had no significant impact on customer satisfaction in the technology‐mediated contexts studied. Implications for both practitioners and researchers are drawn from the results and future research opportunities are discussed.
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Citations
Cites
Organizational Information Requirements, Media Richness and Structural Design
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Parasuraman, A. (2000) · Journal of Service Research · doi:10.1177/109467050024001 · Cited by 3,328
AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE
MORRIS, M. G., & VENKATESH, V. (2000) · Personnel Psychology · doi:10.1111/j.1744-6570.2000.tb00206.x · Cited by 1,723 · cited 2×
“This was expected because younger people have been shown to be generally quicker to adopt new technologies, such as e-mail and instant messaging, than those who are older (Morris & Venkatesh, 2000; Evans et al., 2001; Burke, 2002).”
BackgroundEmpirical Evaluation of the Revised Technology Acceptance Model
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CHANNEL EXPANSION THEORY AND THE EXPERIENTIAL NATURE OF MEDIA RICHNESS PERCEPTIONS.
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Cited by
Technology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159
Verhagen, T., van Nes, J., Feldberg, F., & van Dolen, W. (2014) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12066 · Cited by 403
Wünderlich, N. V., Wangenheim, F. V., & Bitner, M. J. (2013) · Journal of Service Research · doi:10.1177/1094670512448413 · Cited by 334
Determinants of satisfaction and continuance intention towards self‐service technologies
Chen, S., Chen, H., & Chen, M. (2009) · Industrial Management & Data Systems · doi:10.1108/02635570911002306 · Cited by 288
Beyond virtuality: from engagement platforms to engagement ecosystems
F. Breidbach, C., Brodie, R., & Hollebeek, L. (2014) · Managing Service Quality · doi:10.1108/msq-08-2013-0158 · Cited by 271
Metadata
- Authors
- Froehle, C. M.
- Venue
- Decision Sciences
- Publisher
- Wiley
- Published
- 2006-02
- Volume
- 37
- Issue
- 1
- Pages
- 5-38
- ISSN
- 0011-7315 (ISSN-L)
- 1540-5915
- OpenAlex
- W2110996350
- Cited by
- 188
- References
- 7
- FWCI
- 16.65
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.