The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Getting Smart: Learning From Technology-Empowered Frontline Interactions
Open accessAbstract
Smart technologies are rapidly transforming frontline employee-customer interactions. However, little academic research has tackled urgent, relevant questions regarding such technology-empowered frontline interactions. The current study conceptualizes (1) smart technology use in frontline employee-customer interactions, (2) smart technology–mediated learning mechanisms that elevate service effectiveness and efficiency performance to empower frontline interactions, and (3) stakeholder interaction goals as antecedents of smart technology–mediated learning. We propose that emerging smart technologies, which can substitute for or complement frontline employees’ (FLEs) efforts to deliver customized service over time, may help resolve the long-standing tension between service efficiency and effectiveness because they can learn or enable learning from and across customers, FLEs, and interactions. Drawing from pragmatic and deliberate learning theories, the authors conceptualize stakeholder learning mechanisms that mediate the effects of frontline interaction goals on FLEs’ and customers’ effectiveness and efficiency outcomes. This study concludes with implications for research and practice.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The role of deliberate practice in the acquisition of expert performance
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993) · Psychological Review · doi:10.1037/0033-295x.100.3.363 · Cited by 9,564 · cited 1×
“A parallel research stream on individual learning and expert performance (e.g., Baron and Henry 2010; Ericsson 2006; Ericsson and Charness 1994; Ericsson, Krampe, and Tesch-Romer 1993) similarly conceptualizes deliberate practice as intense, concentrated, repeated performance, compared against an…”
Hedonic Consumption: Emerging Concepts, Methods and Propositions
Hirschman, E. C., & Holbrook, M. B. (1982) · Journal of Marketing · doi:10.1177/002224298204600314 · Cited by 5,616
Parasuraman, A. (2000) · Journal of Service Research · doi:10.1177/109467050024001 · Cited by 3,328
Ericsson, K. A. (2006) · The Cambridge Handbook of Expertise and Expert Performance · doi:10.1017/cbo9780511816796.038 · Cited by 1,629
Service research priorities in a rapidly changing context
Ostrom, A. L., Parasuraman, A., Bowen, D. E., Patrício, L., & Voss, C. A. (2015) · Journal of Service Research · doi:10.1177/1094670515576315 · Cited by 1,469
Closing the Marketing Capabilities Gap
Day, G. S. (2011) · Journal of Marketing · doi:10.1509/jmkg.75.4.183 · Cited by 1,079
Eisingerich, A. B., & Bell, S. J. (2006) · Journal of Financial Services Marketing · doi:10.1057/palgrave.fsm.4760022 · Cited by 160
Cited by
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
Self-citationBrave new world: service robots in the frontline
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018) · Journal of Service Management · doi:10.1108/josm-04-2018-0119 · Cited by 2,315
Consumers acceptance of artificially intelligent (AI) device use in service delivery
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.03.008 · Cited by 1,437
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022) · The International Journal of Human Resource Management · doi:10.1080/09585192.2020.1871398 · Cited by 1,134
“Within this context, several authors support the view that AI can be of great value in facilitating service or sales and creating more favorable, customized and valued service interactions (Marinova et al., 2017; Singh et al., 2017).”
BackgroundDeveloping and validating a service robot integration willingness scale
Lu, L., Cai, R., & Gursoy, D. (2019) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2019.01.005 · Cited by 1,013
Metadata
- Authors
- Marinova, D., de Ruyter, K., Huang, M.-H., Meuter, M. L., & Challagalla, G.
- Venue
- Journal of Service Research
- Publisher
- SAGE Publications
- Published
- 2016-12-02
- Volume
- 20
- Issue
- 1
- Pages
- 29-42
- ISSN
- 1094-6705 (ISSN-L)
- 1552-7379
- OpenAlex
- W2558040734
- Cited by
- 363
- References
- 7
- FWCI
- 9.41
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Getting Smart: Learning From Technology-Empowered Frontline Interactions. https://app.cochat.ai/papers/10.1177/1094670516679273
- The paper
- Marinova, D., de Ruyter, K., Huang, M.-H., Meuter, M. L., & Challagalla, G. (2017). Getting Smart: Learning From Technology-Empowered Frontline Interactions. Journal of Service Research. https://doi.org/10.1177/1094670516679273
Data sources
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.
