The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Using artificial intelligence to create value in insurance
Abstract
Purpose: Recent technological and digital developments have opened new avenues for customer data utilization in insurance services. One form of this data transformation is automated chatbots that provide convenient access to data leveraged through a discussion-like interface. The purpose of this paper is to uncover how insurance chatbots support customers’ value creation. Design/methodology/approach: Three complementary theoretical perspectives – artificial intelligence, service logic, and reverse use of customer data – are briefly discussed and integrated into a conceptual framework. The suggested framework is further shown through illustrative case examples that characterize different ways of supporting customers’ value creation. Findings: Chatbots represent a new type of interaction through which companies can influence customers’ value creation by providing them with additional resources. Based on the proposed conceptual framework and the illustrative case examples, four metaphors are identified that characterize how insurance chatbots can support customers’ value creation. Research limitations/implications: The study is conceptual in nature, and the case examples are used for illustrative purposes. No representative data from those users who will eventually determine whether chatbots are of value was used. Practical implications: Using the suggested framework, which is aligned with provider service logic, insurance companies can consider what kind of a role they wish to play in customers’ value-creating processes. Originality/value: Automated chatbots provide convenient access to data leveraged through a discussion-like interface. This study is among the earliest to address their value-creating potential in insurance.
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Evolving to a New Dominant Logic for Marketing
Vargo, S. L., & Lusch, R. F. (2004) · Journal of Marketing · doi:10.1509/jmkg.68.1.1.24036 · Cited by 14,779
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
Service-dominant logic: continuing the evolution
Vargo, S. L., & Lusch, R. F. (2008) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-007-0069-6 · Cited by 7,824
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
Bag of Tricks for Efficient Text Classification
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers · doi:10.18653/v1/e17-2068 · Cited by 4,545
ELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452
A Framework for Conceptual Contributions in Marketing
MacInnis, D. J. (2011) · Journal of Marketing · doi:10.1509/jmkg.75.4.136 · Cited by 1,547
The Decline of Conceptual Articles and Implications for Knowledge Development
Yadav, M. S. (2010) · Journal of Marketing · doi:10.1509/jmkg.74.1.1 · Cited by 578
Cited by
Artificial Intelligence and Business Value: a Literature Review
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022) · Information Systems Frontiers · doi:10.1007/s10796-021-10186-w · Cited by 1,046
“By using AI, companies can learn more about their customers ’ behaviors and, in turn, use this enhanced understanding to proactively prevent any negative experiences (Riikkinen et al., 2018).”
BackgroundAlexa, what do we know about conversational commerce? Insights from a systematic literature review.
Lim, W. M., Kumar, S., Verma, S., & Chaturvedi, R. (2022) · Psychology & Marketing · doi:10.1002/mar.21654 · Cited by 293
Nicolescu, L., & Tudorache, M. T. (2022) · Electronics · doi:10.3390/electronics11101579 · Cited by 280
Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot
Rajaobelina, L., Prom Tep, S., Arcand, M., & Ricard, L. (2021) · Psychology & Marketing · doi:10.1002/mar.21548 · Cited by 252
Wang, C., Teo, T. S. H., & Janssen, M. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102401 · Cited by 161
Eling, M., Nuessle, D., & Staubli, J. (2022) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-020-00201-7 · Cited by 156
Zarifis, A., Kawalek, P., & Azadegan, A. (2021) · Journal of Internet Commerce · doi:10.1080/15332861.2020.1832817 · Cited by 89
Syvänen, S., & Valentini, C. (2020) · Journal of Communication Management · doi:10.1108/jcom-11-2019-0145 · Cited by 56
Opportunities and Challenges for AI-Based Support for Speech-Language Pathologists
Suh, H., Dangol, A., Meadan, H., Miller, C. A., & Kientz, J. A. (2024) · Proceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work · doi:10.1145/3663384.3663387 · Cited by 43
Pathak, A., & Bansal, V. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100094 · Cited by 40
Metadata
- Authors
- Riikkinen, M., Saarijärvi, H., Sarlin, P., & Lähteenmäki, I.
- Venue
- International Journal of Bank Marketing
- Publisher
- Emerald
- Published
- 2018-06-01
- Volume
- 36
- Issue
- 6
- Pages
- 1145-1168
- ISSN
- 0265-2323 (ISSN-L)
- OpenAlex
- W2806410959
- Cited by
- 258
- References
- 11
- FWCI
- 8.06
- Type
- journal article
- Language
- en
- Open access
- closed
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
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.
