Cited by 24AI in Service InteractionsDigital Marketing and Social MediaCustomer Service Quality and Loyalty

Conversational marketing as a framework for interaction with the customer: Development & validation of the conversational agent's usage scale

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Israfilzade, Khalil·2021·JOURNAL OF LIFE ECONOMICS

Abstract

Conversational agents are becoming an essential part of a growing number of personal and commercial encounters, bringing the issue of Conversational Marketing to a broader audience. A conversational agent is a developing technology that will be used in various fields throughout life, including e-commerce. The common characteristics of any conversational agent in whatever area are their capacity to engage in one-to-one personalised real-time dialogue with a human user and their availability 24 hours a day. Scale items for conversational agent phenomena have not been created scientifically or managerially in a business environment. The primary goal of this study was to develop and validate a new scale for conversational agents that could be used to quantify individual interactions in conversational marketing. As a result, the creation of a new scale for conversational agents with the objective of measuring individual customer interactions in conversational marketing was separated into two phases: Scale Development and Scale Validation. The Conversational Agent Usage Scale was developed and validated as a consequence of pilot studies. Additionally, this article discusses the practical consequences of conversational marketing, which can now be accomplished through the use of the Conversational Agent Usage Scale, which may be used by Customer Service & Support, Marketing, and Sales departments.

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Research works printed but not linked: 9

  1. AGUIRRE, E., ROGGEVEEN, A. L., Grewal, D., & Wetzels, M. (2016). The personalisation-privacy paradox: implications for new media. Journal of Consumer Marketing.
  2. CALDER, B. J., ISAAC, M. S., & MALTHOUSE, E. C. (2016). How to capture consumer experiences: A context-specific approach to measuring engagement: Predicting consumer behavior across qualitatively different experiences. Journal of Advertising Re- search, 56(1), 39-52.
  3. CAMBRIDGE ENGLISH DICTIONARY (2020). Conversation. CONVERSATION | meaning in the Cambridge English Dictio- nary. https://dictionary.cambridge.org/dictionary/english/conversa- tion?q=Conversation.
  4. FITZPATRICK, G. (2018). A short history of human computer in- teraction: A people-centred perspective. In Proceedings of the 2018 ACM SIGUCCS Annual Conference (pp. 3-3).
  5. LOCKE, C., LEVINE, R., SEARLS, D. and WEINBERGER, D., (2001). The cluetrain manifesto. Cambridge: Perseus Pub.
  6. SO, K. K. F., KING, C., SPARKS, B. A., & WANG, Y. (2016). En- hancing customer relationships with retail service brands: The role of customer engagement. Journal of Service Management, 27(2), 170-193.
  7. TOURÉ-TILLERY, M., & MCGILL, A. L. (2015). Who or what to believe: Trust and the differential persuasiveness of human and an- thropomorphised messengers. Journal of Marketing, 79(4), 94-110.
  8. YIN, Z., CHANG, K. H., & ZHANG, R. (2017). Deepprobe: Infor- mation directed sequence understanding and chatbot design via re- current neural networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Min- ing (pp. 2131-2139).
  9. ZADROZNY, W., BUDZIKOWSKA, M., CHAI, J., KAMBHAT- LA, N., LEVESQUE, S., & NICOLOV, N. (2000). Natural lan- guage dialogue for personalised interaction. Communications of the ACM, 43(8), 116-120.

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Authors
Israfilzade, K.
Affiliations
Vytautas Magnus University (LT)
Corresponding author
Israfilzade, Khalil
Venue
JOURNAL OF LIFE ECONOMICS
Published
2021-10-31
Volume
8
Issue
4
Pages
533-546
ISSN
2148-4139 (ISSN-L)
OpenAlex
W3213429103
Cited by
23
References
55
Citations by year
2026: 3 · 2025: 6 · 2024: 9 · 2023: 3 · 2022: 2
FWCI
1.24
Type
journal article
Language
en
Open access
Locations
  • Journal of Life Economics (publishedVersion, cc-by)
  • DergiPark (Istanbul University) (submittedVersion)
  • Vytautas Magnus University (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
Keywords
conversational marketing, chatbots, scale development, scale validation, customer interaction, customer service, e-commerce, personalized dialogue

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