Does ChatGPT change artificial intelligence-enabled marketing capability? Social media investigation of public sentiment and usage

Abstract

The recent advent of ChatGPT has stirred substantial attention and debates, potentially altering the dynamics across various industries, notably marketing. This pioneering study delves into the public reactions and applications of ChatGPT within marketing realms. Leveraging a text-mining methodology, a corpus of over 600,000 tweets harvested before and after ChatGPT’s launch from January 2021 to April 2023 was scrutinized to gauge public sentiment towards AI-incorporated tools in marketing, and to unearth the predominant themes within public discourse. Initial findings unveiled a buoyant public sentiment towards AI-facilitated tools, which however, ebbed in January 2023, driven by apprehensions regarding AI technology’s limitations and potential perils. Subsequent months witnessed a rebound in sentiment, stabilizing above the positive threshold, as Twitter users increasingly acknowledged ChatGPT’s prospective merits on employment and daily lives. However, compared to the period before the introduction of ChatGPT, there has been a decline in the general public’s sentiment towards AI in marketing. Furthermore, the analysis discerned a convergence in the core topics broached by the public concerning AI and ChatGPT’s ramifications on marketing. While the automation of mundane tasks and heightened customer experience were lauded, trepidations surrounding job displacement and the ethical quandaries of supplanting human labor with machines surfaced. This exposition recommends that enterprises meticulously assess the prospective impact of AI on their personnel, advocating for the judicious and ethical deployment of such emergent technologies.

Keywords

Artificial intelligence-enabled marketing capability big data ChatGPT public sentiment text-mining Twitter

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References

Amoako-Gyampah

(2007). Perceived usefulness, user involvement and behavioral intention: An empirical study of ERP implementation. Computers in Human Behavior, 23(3), 1232–1248. https://doi.org/10.1016/j.chb.2004.12.002

Awa

H. O.

Ojiabo

O. U.

Emecheta

B. C.

(2015). Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science & Technology Policy Management, 6(1), 76–94. https://doi.org/10.1108/jstpm-04-2014-0012

Barney

(1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

Bastian

Heymann

Jacomy

(2009). Gephi: An open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. San Jose, CA: IEEE.

Blei

Lafferty

(2006). Correlated topic models. Advances in Neural Information Processing Systems, 18, 147.

Buhalis

Cheng

E. S. Y.

(2020). Exploring the use of chatbots in hotels: Technology providers’ perspective. In Information and Communication Technologies in Tourism 2020: Proceedings of the International Conference in Surrey, United Kingdom, January 08–10, 2020 (pp. 231–242). Springer International Publishing.

Cade

Gregory

(2023). Elon Musk and others call for pause on A.I., citing ‘profound risks to society’. The New York Times. Accesed at 8th April 2023. https://www.nytimes.com/2023/03/29/technology/ai-artificial-intelligence-musk-risks.html

Champion

(2018). Inbound content: A step-by-step guide to doing content marketing the inbound way, John Wiley & Sons.

Chatterjee

Rana

N. P.

Dwivedi

Y. K.

Baabdullah

A. M.

(2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880. https://doi.org/10.1016/j.techfore.2021.120880

10.

Chen

(2020). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36–68. https://doi.org/10.1080/23270012.2020.1852895

11.

Cheung

(2019). Understanding visitor–resident relations in overtourism: Developing resilience for sustainable tourism. Journal of Sustainable Tourism, 27(8), 1197–1216. https://doi.org/10.1080/09669582.2019.1606815

12.

Chum

Nielsen

Bellows

Farrell

Durette

Banda

Cupchik

(2021). Changes in public response associated with various Covid-19 restrictions in Ontario, Canada: Observational infoveillance study using social media time series data. Journal of Medical Internet Research, 23(8), e28716. https://doi.org/10.2196/28716

13.

Cui

Razzaq

(2022). Can application of artificial intelligence in enterprises promote the corporate governance? Frontiers in Environmental Science, 10, 944467. https://doi.org/10.3389/fenvs.2022.944467

14.

Davis

F. D.

Bagozzi

R. P.

Warshaw

P. R.

(1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

15.

Devlin

Chang

M. W.

Lee

Toutanova

(2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

16.

Farrokhi

Shirazi

Hajli

Tajvidi

(2020). Using artificial intelligence to detect crisis related to events: Decision making in B2B by artificial intelligence. Industrial Marketing Management, 91, 257–273. https://doi.org/10.1016/j.indmarman.2020.09.015

17.

Featherman

Jia

S. J.

Califf

C. B.

Hajli

(2021). The impact of new technologies on consumers beliefs: Reducing the perceived risks of electric vehicle adoption. Technological Forecasting and Social Change, 169, 120847. https://doi.org/10.1016/j.techfore.2021.120847

18.

Financial Times . (2023). Elon Musk and other tech experts call for ‘pause’ on advanced AI systems. Financial Times. Accesed at 8th April 2023. https://www.ft.com/content/3f584019-7c51-4c9c-b18f-0e0ac0821bf7

19.

Fortunato

Barthelemy

(2007). Resolution limit in community detection. Proceedings of the national academy of sciences, 104(1), 36–41.

20.

Frost

Strauss

(2016). E-marketing, Routledge.

21.

Fusi

Feeney

M. K.

(2018). Social media in the workplace: Information exchange, productivity, or waste? The American Review of Public Administration, 48(5), 395–412. https://doi.org/10.1177/0275074016675722

22.

Gill

S. S.

Ottaviani

Patros

Bahsoon

Shaghaghi

Golec

Stankovski

Abraham

Singh

Mehta

Ghosh

S. K.

Baker

Parlikad

A. K.

Lutfiyya

Kanhere

S. S.

Sakellariou

Dustdar

Uhlig

(2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514. https://doi.org/10.1016/j.iot.2022.100514

23.

Gillis

W. E.

Combs

J. G.

Yin

(2020). Franchise management capabilities and franchisor performance under alternative franchise ownership strategies. Journal of Business Venturing, 35(1), 105899. https://doi.org/10.1016/j.jbusvent.2018.09.004

24.

Golshan

Halevy

Mihaila

Tan

W. C.

(2017, May). Data integration: After the teenage years. In Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI symposium on principles of database systems, Chicago, IL (pp. 101–106).

25.

Grewal

Guha

Satornino

C. B.

Schweiger

E. B.

(2021). Artificial intelligence: The light and the darkness. Journal of Business Research, 136, 229–236. https://doi.org/10.1016/j.jbusres.2021.07.043

26.

Hagen

(2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing & Management, 54(6), 1292–1307. https://doi.org/10.1016/j.ipm.2018.05.006

27.

Haseeb

Mihardjo

L. W. W.

Gill

A. R.

Jermsittiparsert

(2019). Economic impact of artificial intelligence: New look for the macroeconomic assessment in Asia-Pacific region. International Journal of Computational Intelligence Systems, 12(2), 1295. https://doi.org/10.2991/ijcis.d.191025.001

28.

Hashimoto

Kontonatsios

Miwa

Ananiadou

(2016). Topic detection using paragraph vectors to support active learning in systematic reviews. Journal of Biomedical Informatics, 62, 59–65. https://doi.org/10.1016/j.jbi.2016.06.001

29.

Helfat

C. E.

Peteraf

M. A.

(2015). Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strategic Management Journal, 36(6), 831–850. https://doi.org/10.1002/smj.2247

30.

Ibrahim

Wang

Bourne

(2017). Exploring the effect of user engagement in online brand communities: Evidence from Twitter. Computers in Human Behavior, 72(72), 321–338. https://doi.org/10.1016/j.chb.2017.03.005

31.

Jin

(2018). How the frequency and amount of corporate donations affect consumer perception and behavioral responses. Journal of the Academy of Marketing Science, 46(6), 1072–1088. https://doi.org/10.1007/s11747-018-0584-7

32.

John

Scheer

L. K.

(2021). Commentary: Governing technology-enabled omnichannel transactions. Journal of Marketing, 85(1), 126–130. https://doi.org/10.1177/0022242920972071

33.

Jurek

Mulvenna

(2015). Improved lexicon-based sentiment analysis for social media analytics. Security Informatics, 4(1), 9. https://doi.org/10.1186/s13388-015-0024-x

34.

Karami

Gangopadhyay

Zhou

Kharrazi

(2018). Fuzzy approach topic discovery in health and medical corpora. International Journal of Fuzzy Systems, 20(4), 1334–1345. https://doi.org/10.1007/s40815-017-0327-9

35.

Karami

Lundy

Webb

Dwivedi

Y. K.

(2020). Twitter and research: A systematic literature review through text mining. IEEE Access, 8, 67698–67717. https://doi.org/10.1109/access.2020.2983656

36.

Karami

Pendergraft

N. M.

(2018). Computational analysis of insurance complaints: Geico case study. arXiv preprint arXiv:1806.09736.

37.

Krystal

(2023). ChatGPT sets record for fastest-growing user base - analyst note. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ (accessed at 8th April 2023).

38.

Kushwaha

A. K.

Kar

A. K.

Dwivedi

Y. K.

(2021). Applications of big data in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights, 1(2), 100017. https://doi.org/10.1016/j.jjimei.2021.100017

39.

Larsson

(2020). On the governance of artificial intelligence through ethics guidelines. Asian Journal of Law and Society, 7(3), 437–451. https://doi.org/10.1017/als.2020.19

40.

Huang

Meng

(2023). Official media as emotional valves. Asian Survey, 63(4), 611–640. https://doi.org/10.1525/as.2023.1831404

41.

Mei

Zhai

(2011). Investigating task performance of probabilistic topic models: An empirical study of PLSA and LDA. Information Retrieval, 14(2), 178–203. https://doi.org/10.1007/s10791-010-9141-9

42.

Luo

Qin

M. S.

Fang

(2021). Artificial intelligence coaches for sales agents: Caveats and solutions. Journal of Marketing, 85(2), 14–32. https://doi.org/10.1177/0022242920956676

43.

Shi

Kang

(2022). Intelligent manufacturing and carbon emissions reduction: Evidence from the use of industrial robots in China. International Journal of Environmental Research and Public Health, 19(23), 15538. https://doi.org/10.3390/ijerph192315538

44.

Jin

Zhu

(2023). Quantified multidimensional public sentiment characteristics on social media for public opinion management: Evidence from the covid-19 pandemic. Frontiers in Public Health, 11(11), 1097796. https://doi.org/10.3389/fpubh.2023.1097796

45.

Manis

K. T.

Madhavaram

(2023). AI-enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues. Journal of Business Research, 157, 113485. https://doi.org/10.1016/j.jbusres.2022.113485

46.

Mao

Shi-Kupfer

(2023). Online public discourse on artificial intelligence and ethics in China: Context, content, and implications. AI & Society, 38(1), 373–389. https://doi.org/10.1007/s00146-021-01309-7

47.

McLean

Osei-Frimpong

Barhorst

(2021). Alexa, do voice assistants influence consumer brand engagement? Examining the role of AI powered voice assistants in influencing consumer brand engagement. Journal of Business Research, 124, 312–328. https://doi.org/10.1016/j.jbusres.2020.11.045

48.

Morgan

N. A.

(2019). Researching marketing capabilities: Reflections from academia. AMS Review, 9(3-4), 381–385, https://doi.org/10.1007/s13162-019-00158-4

49.

Munoko

Brown-Liburd

H. L.

Vasarhelyi

(2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/s10551-019-04407-1

50.

Nguyen

Hekman

(2022). A ‘new arms race’? Framing China and the USA in AI news reporting: A comparative analysis of the Washington Post and South China Morning Post. Global Media and China, 7(1), 58–77. https://doi.org/10.1177/20594364221078626

51.

Nguyen

Q. N.

Sidorova

Torres

(2022). User interactions with chatbot interfaces vs. Menu-based interfaces: An empirical study. Computers in Human Behavior, 128, 107093. https://doi.org/10.1016/j.chb.2021.107093

52.

Pereira

Bamel

(2021). Extending the resource and knowledge based view: A critical analysis into its theoretical evolution and future research directions. Journal of Business Research, 132, 557–570. https://doi.org/10.1016/j.jbusres.2021.04.021

53.

Pindado

Barrena

(2021). Using Twitter to explore consumers’ sentiments and their social representations towards new food trends. British Food Journal, 123(3), 1060–1082. https://doi.org/10.1108/bfj-03-2020-0192

54.

Puschmann

Powell

(2018). Turning words into consumer preferences: How sentiment analysis is framed in research and the news media. Social Media + Society, 4(3), 205630511879772. https://doi.org/10.1177/2056305118797724

55.

Kim

(2022). Dynamic capabilities perspective on innovation ecosystem of China’s universities in the age of artificial intelligence: Policy-based analysis. Journal of Infrastructure Policy and Development, 6(2), 1661. https://doi.org/10.24294/jipd.v6i2.1661

56.

Rajkhowa

Dsouza

Kharel

Cauvery

Mallya

Raksha

Brand

Mrinalini

Sharma

Pattanshetty

Narayanan

Lahariya

Brand

(2023). Factors influencing monkeypox vaccination: A cue to policy implementation. Journal of Epidemiology and Global Health, 13(2), 226–238. https://doi.org/10.1007/s44197-023-00100-9

57.

Rana

N. P.

Chatterjee

Dwivedi

Y. K.

Akter

(2022). Understanding dark side of artificial intelligence (AI) integrated business analytics: Assessing firm’s operational inefficiency and competitiveness. European Journal of Information Systems, 31(3), 364–387. https://doi.org/10.1080/0960085x.2021.1955628

58.

Rivas

Zhao

(2023). Marketing with ChatGPT: Navigating the ethical terrain of gpt-based chatbot technology. AI, 4(2), 375–384. https://doi.org/10.3390/ai4020019

59.

Roberts

Cowls

Morley

Taddeo

Wang

Floridi

(2020). The Chinese approach to artificial intelligence: An analysis of policy, ethics, and regulation. AI & Society, 36(1), 59–77. https://doi.org/10.1007/s00146-020-00992-2

60.

Schuetzler

R. M.

Grimes

G. M.

Giboney

J. S.

Rosser

H. K.

(2021). Deciding whether and how to deploy chatbots. MIS Quarterly Executive, 20(1), 1–15. https://doi.org/10.17705/2msqe.00039

61.

Shen

Y. C.

Huang

C. Y.

Chu

C. H.

Hsu

C. T.

(2010). A benefit–cost perspective of the consumer adoption of the mobile banking system. Behaviour & Information Technology, 29(5), 497–511. https://doi.org/10.1080/01449290903490658

62.

Flew

(2020). The rise of baidu, alibaba and tencent (bat) and their role in China’s belt and road initiative (bri). Global Media and Communication, 17(1), 67–86. https://doi.org/10.1177/1742766520982324

63.

Swaminathan

Moorman

(2009). Marketing alliances, firm networks, and firm value creation. Journal of Marketing, 73(5), 52–69. https://doi.org/10.1509/jmkg.73.5.52

64.

The CMO Survey . (2020). Highlights and insights report. The CMO Survey. https://cmosurvey.org/wpcontent/uploads/2020/02/The_CMO_Survey-Highlights-and_Insights_Report-Feb-2020.pdf

65.

Thorp

H. H.

(2023). ChatGPT is fun, but not an author. Science, 379(6630), 313–313. https://doi.org/10.1126/science.adg7879

66.

Timoshenko

Hauser

J. R.

(2019). Identifying customer needs from user-generated content. Marketing Science, 38(1), 1–20. https://doi.org/10.1287/mksc.2018.1123

67.

Valdez

Pickett

A. C.

Goodson

(2018). Topic modeling: Latent semantic analysis for the social sciences. Social Science Quarterly, 99(5), 1665–1679. https://doi.org/10.1111/ssqu.12528

68.

Van Esch

Stewart Black

(2021). Artificial intelligence (AI): Revolutionizing digital marketing. Australasian Marketing Journal, 29(3), 199–203. https://doi.org/10.1177/18393349211037684

69.

Vishwanath

(2005). Impact of personality on technology adoption: An empirical model. Journal of the American Society for Information Science and Technology, 56(8), 803–811. https://doi.org/10.1002/asi.20169

70.

Wang

(2022). Business risk of the artificial intelligence industry in China. BCP Business & Management, 34, 1430–1436. https://doi.org/10.54691/bcpbm.v34i.3195

71.

Webb

Karami

Kitzie

(2018). Characterizing diseases and disorders in gay users' tweets. arXiv preprint arXiv:1803.09134.

72.

Wedel

Kannan

P. K.

(2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413

73.

Wernerfelt

(1984). A resource‐based view of the firm. Strategic Management Journal, 5(2), 171–180. https://doi.org/10.1002/smj.4250050207

74.

Huang

Gong

(2020). Ethical principles and governance technology development of ai in China. Engineering, 6(3), 302–309. https://doi.org/10.1016/j.eng.2019.12.015

75.

Zeng

Tai

Hao

(2022). Public attention and sentiment toward intimate partner violence based on Weibo in China: A text mining approach. Healthcare, 10(2), 198. https://doi.org/10.3390/healthcare10020198

76.

Xue

Lin

Cheng

(2019). Psychosocial factors affecting artificial intelligence adoption in health care in China: Cross-sectional study. Journal of Medical Internet Research, 21(10), Article e14316. https://doi.org/10.2196/14316

77.

Zahra

S. A.

Neubaum

D. O.

Larrañeta

(2007). Knowledge sharing and technological capabilities: The moderating role of family involvement. Journal of Business Research, 60(10), 1070–1079. https://doi.org/10.1016/j.jbusres.2006.12.014

78.

Zarifhonarvar

(2023). Economics of ChatGPT: a labor market view on the occupational impact of artificial intelligence. Journal of Electronic Business & Digital Economics. In Press . https://doi.org/10.1108/JEBDE-10-2023-0021

79.

Zhou

Mavondo

F. T.

Saunders

S. G.

(2019). The relationship between marketing agility and financial performance under different levels of market turbulence. Industrial Marketing Management, 83, 31–41. https://doi.org/10.1016/j.indmarman.2018.11.008