Cited by 11AI in Service InteractionsDigital Marketing and Social MediaMarketing and Advertising Strategies

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Transforming marketing landscapes: a systematic literature review of generative AI using the TCCM model framework

Prasanna, Akshara, Kushwaha, Bijay Prasad·2026·Management Review Quarterly

Overview

Integrity signals

Registry

Crossref · 10.1007/s11301-025-00486-9

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Citations

Cites

Show all 6 references

Research works printed but not linked: 82

  1. Acar OA (2024) Commentary: reimagining marketing education in the age of generative AI. Int J Res Mark 41(3):489–495. https://doi.org/10.1016/j.ijresmar.2024.06.004 · DOI
  2. Alabed A, Javornik A, Gregory-Smith D, Casey R (2024) More than just a chat: a taxonomy of consumers’ relationships with conversational AI agents and their well-being implications. Eur J Mark 58(2):373–409. https://doi.org/10.1108/EJM-01-2023-0037 · DOI
  3. Alsharhan A, Al-Emran M, Shaalan K (2023) Chatbot adoption: a multiperspective systematic review and future research agenda. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2023.3298360 · DOI
  4. Ameen N, Sharma GD, Tarba S, Rao A, Chopra R (2022) Toward advancing theory on creativity in marketing and artificial intelligence. Psychol Mark 39(9):1802–1825 · DOI
  5. Bakkouri BE, Raki S, Belgnaoui T (2022) The role of chatbots in enhancing customer experience: literature review. Procedia Comput Sci 203:432–437. https://doi.org/10.1016/j.procs.2022.07.057 · DOI
  6. Bălan C (2023) Chatbots and voice assistants: digital transformers of the company-customer interface—a systematic review of the business research literature. J Theor Appl Electron Commer Res 18(2):995–1019. https://doi.org/10.3390/jtaer18020051 · DOI
  7. Balasudarsun NL, Sathish M, Gowtham K (2018) Optimal ways for companies to use facebook messenger chatbot as a marketing communication channel. Asian J Bus Res 8(2):1–17. https://doi.org/10.14707/ajbr.180046 · DOI
  8. Bilgihan A, Dogru T, Hanks L, Line N, Mody M (2024) The GAI marketing model: a conceptual framework and future research directions. Int J Hosp Manag 123:103929. https://doi.org/10.1016/j.ijhm.2024.103929 · DOI
  9. Block J, Kuckertz A (2024) What is the future of human-generated systematic literature reviews in an age of artificial intelligence? Manag Rev Q 74:1–6 · DOI
  10. Cai D, Li H, Law R (2022) Anthropomorphism and OTA chatbot adoption: a mixed methods study. J Travel Tour Mark 39(2):228–255. https://doi.org/10.1080/10548408.2022.2061672 · DOI
  11. Calvaresi D, Ibrahim A, Calbimonte JP, Fragniere E, Schegg R, Schumacher MI (2023) Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies. J Tour Futur 9(3):311–337. https://doi.org/10.1108/JTF-01-2021-0009/FULL/PDF · DOI
  12. Camilleri MA, Troise C (2023) Live support by chatbots with artificial intelligence: a future research agenda. Serv Bus 17(1):61–80. https://doi.org/10.1007/s11628-022-00513-9 · DOI
  13. Carvalho I, Ivanov S (2024) ChatGPT for tourism: applications, benefits and risks. Tour Rev 79(2):290–303. https://doi.org/10.1108/TR-02-2023-0088 · DOI
  14. Chen JV, Le HT, Tran ST (2021) Understanding automated conversational agent as a decision aid_ matching agent’s conversation with customer’s shopping task. Internet Res 31(4):1376–1404. https://doi.org/10.1108/INTR-11-2019-0447 · DOI
  15. Cheng X, Bao Y, Zarifis A, Gong MJ (2022) Exploring consumers’ response to text-based chatbots in e-commerce: the moderating role of task complexity and chatbot disclosure. Internet Res 32(2):496–517. https://doi.org/10.1108/INTR-08-2020-0460 · DOI · Paper page
  16. Chung M, Ko E, Joung H, Kim SJ (2020) Chatbot e-service and customer satisfaction regarding luxury brands. J Bus Res 117:587–595. https://doi.org/10.1016/j.jbusres.2018.10.004 · DOI
  17. Darani MM, Mirahmad H, Raoofpanah I, Groening C (2023) Managerial responses to online communication: the role of mimicry in affecting third-party observers’ purchase intentions. J Bus Res 166:113979. https://doi.org/10.1016/j.jbusres.2023.113979 · DOI
  18. David-Ignatieff A, Buzeta C, De Pelsmacker P, Ben DahmaneMouelhi N (2023) This embodied conversational agent looks very human and as old as I feel! The effect of perceived agent anthropomorphism and consumer-agent age difference on brand attitude. J Mark Commun. https://doi.org/10.1080/13527266.2023.2199026 · DOI
  19. Dhiman N, Jamwal M (2023) Tourists’ post-adoption continuance intentions of chatbots: integrating task–technology fit model and expectation–confirmation theory. Foresight 25(2):209–224. https://doi.org/10.1108/FS-10-2021-0207 · DOI
  20. Dinh CM, Park S (2023) How to increase consumer intention to use Chatbots? An empirical analysis of hedonic and utilitarian motivations on social presence and the moderating effects of fear across generations. Electron Commer Res 6:1–41. https://doi.org/10.1007/s10660-022-09662-5 · DOI
  21. Dipaola S, Gabora L, McCaig G (2018) Informing artificial intelligence generative techniques using cognitive theories of human creativity. Procedia Comput Sci 145:158–168. https://doi.org/10.1016/j.procs.2018.11.024 · DOI
  22. Dogru T, Line N, Mody M, Hanks L, Abbott JA, Acikgoz F, Assaf A, Bakir S, Berbekova A, Bilgihan A, Dalton A (2023) Generative artificial intelligence in the hospitality and tourism industry: developing a framework for future research. J Hosp Tour Res 10:10963480231188664. https://doi.org/10.1177/10963480231188663 · DOI
  23. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res. https://doi.org/10.1016/j.jbusres.2021.04.070 · DOI · Paper page
  24. Dwivedi YK, Kshetri N, Hughes L, Slade EL, Jeyaraj A, Kar AK, Baabdullah AM, Koohang A, Raghavan V, Ahuja M, Albanna H (2023a) “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int J Inf Manage 71:102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 · DOI · Paper page
  25. Dwivedi YK, Pandey N, Currie W, Micu A (2023b) Leveraging ChatGPT and other generative artificial intelligence (AI)-based applications in the hospitality and tourism industry: practices, challenges and research agenda. Int J Contemp Hosp Manag 36(1):1–12. https://doi.org/10.1108/IJCHM-05-2023-0686 · DOI
  26. Flandrin P, Hellemans C, Van der Linden J, Van de Leemput C (2021) Smart technologies in hospitality:effects on activity, work design and employment. a case study about chatbot usage.In: Proceedings of the 17th“Ergonomie et Informatique Avancée” conference. https://doi.org/10.1145/3486812.3486838 · DOI
  27. Fu J, Mouakket S, Sun Y (2023) The role of chatbots’ human-like characteristics in online shopping. Electron Commer Res Appl 61:101304. https://doi.org/10.1016/j.elerap.2023.101304 · DOI
  28. Gursoy D, Li Y, Song H (2023) ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions. J Hosp Market Manag 32(5):579–592. https://doi.org/10.1080/19368623.2023.2211993 · DOI
  29. Han MC (2021) The impact of anthropomorphism on consumers’ purchase decision in chatbot commerce. J Internet Commer 20(1):46–65. https://doi.org/10.1080/15332861.2020.1863022 · DOI · Paper page
  30. Jiang Y, Yang X, Zheng T (2023) Make chatbots more adaptive: Dual pathways linking human-like cues and tailored response to trust in interactions with chatbots. Comput Hum Behav 138:107485. https://doi.org/10.1016/j.chb.2022.107485 · DOI
  31. Jin E, Eastin MS (2023) Birds of a feather flock together: matched personality effects of product recommendation chatbots and users. J Res Interact Mark 17(3):416–433. https://doi.org/10.1108/JRIM-03-2022-0089 · DOI
  32. Jin SV, Youn S (2023) Social presence and imagery processing as predictors of chatbot continuance intention in human-AI-interaction. Int J Hum-Comput Interact 39(9):1874–1886. https://doi.org/10.1080/10447318.2022.2129277 · DOI · Paper page
  33. Johnson EK, Hong SC (2023) Instagramming social presence: a test of social presence theory and heuristic cues on instagram sponsored posts. Int J Bus Commun 60(2):543–559. https://doi.org/10.1177/2329488420944462 · DOI
  34. Kar AK, Varsha PS, Rajan S (2023) Unravelling the impact of generative artificial intelligence (GAI) in industrial applications: a review of scientific and grey literature. Glob J Flex Syst Manag 24(4):659–689. https://doi.org/10.1007/s40171-023-00356-x · DOI
  35. Klein K, Martinez LF (2022) The impact of anthropomorphism on customer satisfaction in chatbot commerce: an experimental study in the food sector. Electron Commer Res 23(4):2789–2825. https://doi.org/10.1007/s10660-022-09562-8 · DOI · Paper page
  36. Koc E, Hatipoglu S, Kivrak O, Celik C, Koc K (2023) Houston, we have a problem!: the use of ChatGPT in responding to customer complaints. Technol Soc 74:102333. https://doi.org/10.1016/j.techsoc.2023.102333 · DOI
  37. Korzynski P, Mazurek G, Altmann A, Ejdys J, Kazlauskaite R, Paliszkiewicz J, Wach K, Ziemba E (2023) Generative artificial intelligence as a new context for management theories: analysis of ChatGPT. Cent Eur J Manag 31(1):3–13. https://doi.org/10.1108/cemj-02-2023-0091 · DOI
  38. Kull AJ, Romero M, Monahan L (2021) How may I help you? Driving brand engagement through the warmth of an initial chatbot message. J Bus Res 135:840–850. https://doi.org/10.1016/j.jbusres.2021.03.005 · DOI · Paper page
  39. Kumar A, Gupta N, Bapat G (2023) Who is making the decisions? How retail managers can use the power of ChatGPT. J Bus Strategy 45(3):161–169. https://doi.org/10.1108/JBS-04-2023-0067 · DOI
  40. Kushwaha AK, Kumar P, Kar AK (2021) What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from big data analytics. Ind Mark Manag 98:207–221. https://doi.org/10.1016/j.indmarman.2021.08.011 · DOI
  41. Lappeman J, Marlie S, Johnson T, Poggenpoel S (2023) Trust and digital privacy: willingness to disclose personal information to banking chatbot services. J Financ Ser Mark 28(2):337–357. https://doi.org/10.1057/s41264-022-00154-z · DOI
  42. Law R, Lin KJ, Ye H, Fong DKC (2023) Artificial intelligence research in hospitality: a state-of-the-art review and future directions. Int J Contemp Hosp Manag 36(6):2049–2068. https://doi.org/10.1108/IJCHM-02-2023-0189 · DOI
  43. Lee CT, Pan LY, Hsieh SH (2022) Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach. Internet Res 32(4):1329–1356. https://doi.org/10.1108/INTR-01-2021-0030 · DOI
  44. Li J, Wu L, Qi J, Zhang Y, Wu Z, Hu S (2023) Determinants affecting consumer trust in communication with ai chatbots: the moderating effect of privacy concerns. J Organ End User Comput 35(1):1–24. https://doi.org/10.4018/JOEUC.328089 · DOI
  45. Lim Y, Lim J, Cho N (2020) An experimental comparison of the usability of rule-based and natural language processing-based chatbots. Asia Pac J Inf Syst 30(4):832–846. https://doi.org/10.14329/APJIS.2020.30.4.832 · DOI
  46. Lin JS, Wu L (2023) Examining the psychological process of developing consumer-brand relationships through strategic use of social media brand chatbots. Comput Hum Behav 140:107488. https://doi.org/10.1016/j.chb.2022.107488 · DOI
  47. Liu Y, Cai L, Ma F, Wang X (2023) Revenge buying after the lockdown: based on the SOR framework and TPB model. J Retail Consum Serv 72:103263. https://doi.org/10.1016/J.JRETCONSER.2023.103263 · DOI
  48. Lou C, Kang H, Tse CH (2022) Bots versus humans: how schema congruity, contingency-based interactivity, and sympathy influence consumer perceptions and patronage intentions. Int J Advert 41(4):655–684. https://doi.org/10.1080/02650487.2021.1951510 · DOI
  49. Maragno G, Tangi L, Gastaldi L, Benedetti M (2022) AI as an organizational agent to nurture: effectively introducing chatbots in public entities. Public Manag Rev 25(11):2103–2165. https://doi.org/10.1080/14719037.2022.2063935 · DOI
  50. Meng LM, Li T, Huang X (2023) Double-sided messages improve the acceptance of chatbots. Ann Tour Res 102:103644. https://doi.org/10.1016/j.annals.2023.103644 · DOI
  51. Ngai EW, Lee MC, Luo M, Chan PS, Liang T (2021) An intelligent knowledge-based chatbot for customer service. Electron Commer Res Appl 50:101098. https://doi.org/10.1016/j.elerap.2021.101098 · DOI
  52. Nguyen VT, Phong LT, Chi NTK (2023) The impact of AI chatbots on customer trust: an empirical investigation in the hotel industry. CBTH 18(3):293–305. https://doi.org/10.1108/CBTH-06-2022-0131 · DOI
  53. Orchard T, Tasiemski L (2023) The rise of Generative AI and possible effects on the economy. Econ Bus Rev 9(2):9–26. https://doi.org/10.18559/ebr.2023.2.732 · DOI
  54. Paul J, Rosado-Serrano A (2019) Gradual Internationalization versus born-global/international new venture models: a review and research agenda. Int Market Rev. https://doi.org/10.1108/IMR-10-2018-0280 · DOI
  55. Paul J, Khatri P, Kaur Duggal H (2023) Frameworks for developing impactful systematic literature reviews and theory building: what, why and how? J Decis Syst. https://doi.org/10.1080/12460125.2023.2197700 · DOI
  56. Pavone G, Meyer-Waarden L, Munzel A (2023) Rage against the machine: experimental insights into customers’ negative emotional responses, attributions of responsibility, and coping strategies in artificial intelligence-based service failures. J Interact Mark 58(1):52–71. https://doi.org/10.1177/10949968221134492 · DOI
  57. Pentina I, Hancock T, Xie T (2023) Exploring relationship development with social chatbots: a mixed-method study of replika. Comput Hum Behav 140:107600 · DOI
  58. Peres R, Schreier M, Schweidel D, Sorescu A (2023) Editorial: on ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. Int J Res Mark 40(2):269–275. https://doi.org/10.1016/j.ijresmar.2023.03.001 · DOI · Paper page
  59. Ramesh A, Chawla V (2022) Chatbots in marketing: a literature review using morphological and Cooccurrence analyses. J Interact Mark 57(3):472–496. https://doi.org/10.1177/10949968221095549 · DOI
  60. Reeves B, Nass C (1996) The media equation: How people treat computers, television, and new media like real people. Cambridge University Press, Cambridge, pp 19–36
  61. Rese A, Ganster L, Baier D (2020) Chatbots in retailers’ customer communication: how to measure their acceptance? J Retail Consum Serv 56:102176. https://doi.org/10.1016/j.jretconser.2020.102176 · DOI
  62. Sands S, Campbell C, Ferraro C, Demsar V, Rosengren S, Farrell J (2024) Principles for advertising responsibly using generative AI. Organ Dyn 53(2):101042. https://doi.org/10.1016/j.orgdyn.2024.101042 · DOI
  63. Saydam MB, Arici HE, Koseoglu MA (2022) How does the tourism and hospitality industry use artificial intelligence? A review of empirical studies and future research agenda. J Hosp Market Manag 31(8):908–936. https://doi.org/10.1080/19368623.2022.2118923 · DOI
  64. Selamat MA, Windasari NA (2021) Chatbot for SMEs: integrating customer and business owner perspectives. Technol Soc 66:101685. https://doi.org/10.1016/j.techsoc.2021.101685 · DOI
  65. Sharma S, Singh G, Islam N, Dhir A (2022) Why do SMEs adopt artificial intelligence-based chatbots? IEEE Trans Eng Manag 71:1773–1786. https://doi.org/10.1109/TEM.2022.3203469 · DOI
  66. Shin H, Bunosso I, Levine LR (2023) The influence of chatbot humour on consumer evaluations of services. Int J Consum Stud 47(2):545–562. https://doi.org/10.1111/ijcs.12849 · DOI
  67. Silva FA, Shojaei AS, Barbosa B (2023) Chatbot-based services: a study on customers’ reuse intention. J Theor Appl Electron Commer Res 18(1):457–474. https://doi.org/10.3390/jtaer18010024 · DOI
  68. Song SW, Shin M (2022) Uncanny valley effects on chatbot trust, purchase intention, and adoption intention in the context of E-commerce: the moderating role of avatar familiarity. Int J Hum-Comput Interact 40(2):441–456. https://doi.org/10.1080/10447318.2022.2121038 · DOI · Paper page
  69. Song M, Du J, Xing X, Mou J (2022) Should the chatbot “save itself” or “be helped by others”? The influence of service recovery types on consumer perceptions of recovery satisfaction. Electron Commer Res Appl 55:101199. https://doi.org/10.1016/j.elerap.2022.101199 · DOI
  70. Srivastava S, Prabhakar T (2019) Hospitality of chatbot building platforms. In: Proceedings of the 2nd ACM SIGSOFT international workshop on software qualities and their dependencies. https://doi.org/10.1145/3340495.3342751 · DOI
  71. Theres C, Strohmeier S, Theres C, Strohmeier S (2023) Consolidating the theoretical foundations of digital human resource management acceptance and use research: a meta-analytic validation of UTAUT. Manag Rev Quart 2023:1–33. https://doi.org/10.1007/S11301-023-00367-Z · DOI
  72. Vassilakopoulou P, Haug A, Salvesen LM, Pappas IO (2023) Developing human/AI interactions for chat-based customer services: lessons learned from the Norwegian government. Eur J Inf Syst 32(1):10–22. https://doi.org/10.1080/0960085X.2022.2096490 · DOI
  73. Xin G, Hui G, Jianfeng W, Qifeng W (2021) Digital competitiveness of tourism SMEs. Turk J Comput Math Edu (TURCOMAT) 12(11):1707–1714. https://doi.org/10.17762/TURCOMAT.V12I11.6105 · DOI
  74. Yoon J, Yu H (2022) Impact of customer experience on attitude and utilization intention of a restaurant-menu curation chatbot service. J Hosp Tour Technol 13(3):527–541. https://doi.org/10.1108/jhtt-03-2021-0089 · DOI
  75. Youn K, Cho M (2023) Business types matter: new insights into the effects of anthropomorphic cues in AI chatbots. J Serv Mark 37(8):1032–1045. https://doi.org/10.1108/JSM-04-2022-0126 · DOI
  76. Zhang Y, Prebensen N (2024) Co-creating with ChatGPT for tourism marketing materials. Ann Tour Res Empir Insights. https://doi.org/10.31219/osf.io/nvbyj · DOI
  77. Zhang T, Feng C, Chen H, Xian J (2022) Calming the customers by AI: investigating the role of chatbot acting-cute strategies in soothing negative customer emotions. Electron Mark 32(4):2277–2292. https://doi.org/10.1007/s12525-022-00596-2 · DOI
  78. Zhang JJ, Følstad A, Bjørkli CA (2023) Organizational factors affecting successful implementation of chatbots for customer service. J Internet Commer 22(1):122–156. https://doi.org/10.1080/15332861.2021.1966723 · DOI
  79. Zhang J, Chen Q, Lu J, Wang X, Liu L, Feng Y (2024) Emotional expression by artificial intelligence chatbots to improve customer satisfaction: underlying mechanism and boundary conditions. Tour Manag. https://doi.org/10.1016/j.tourman.2023.104835 · DOI
  80. Zhu J, Jiang Y, Wang X, Huang S (2023a) Social- or task-oriented: how does social crowding shape consumers’ preferences for chatbot conversational styles? J Res Interact Mark 17(5):641–662. https://doi.org/10.1108/JRIM-01-2022-0007 · DOI
  81. Zhu Y, Zhang J, Liang J (2023b) Concrete or abstract: How chatbot response styles influence customer satisfaction. Electron Commer Res Appl 62:101317. https://doi.org/10.1016/j.elerap.2023.101317 · DOI · Paper page
  82. Zhu Y, Zhang R, Zou Y, Jin D (2023c) Investigating customers’ responses to artificial intelligence chatbots in online travel agencies: the moderating role of product familiarity. J Hosp Tour Technol 14(2):208–224. https://doi.org/10.1108/JHTT-02-2022-0041 · DOI

Cited by

Sorted by citations

Metadata

Authors
Prasanna, A., & Kushwaha, B. P.
Affiliations
Vellore Institute of Technology University (IN)
Corresponding author
Kushwaha, Bijay Prasad
Venue
Management Review Quarterly
Published
2025-01-29
Volume
76
Issue
1
Pages
421-455
ISSN
  • 2198-1620 (ISSN-L)
  • 2198-1639
OpenAlex
W4406937656
Cited by
11
References
6
Citations by year
2026: 7 · 2025: 4
FWCI
1.07
Type
journal article
Language
en
Open access
closed
Locations
Management Review Quarterly (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
  • Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
Keywords
generative AI, marketing, TCCM framework, innovation adoption, human-computer interaction, cognitive theory, research directions, conceptual framework

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