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Abstract

Artificial intelligence (AI) is a collection of rapidly evolving disruptive technologies that radically alter various aspects of people, business, society, and the environment. AI increasingly provides significant advertising opportunities for society and business organizations. However, AI could be used to spread disinformation if it were deliberately programmed to produce misleading advertising content. Using cognitive appraisal theory and information quality theory to study how consumers assess threats and develop AI marketing coping strategies from the information generated by AI, this study examines the outcome of the dark side of AI advertising. We collected data from 451 AI-advertising users in Vietnam. The results based on PLS-SEM showed interesting and novelty results. The statistical analysis showed a negative correlation between contextual, representational, accessibility, and threat appraisals. There was also a statistically significant positive correlation between contextual, representational, accessibility, and coping appraisals. Threat appraisals were positively correlated with anger and anxiety but not loneliness. Coping appraisal was significant and negatively correlated with anxiety but not anger or loneliness. This study advances theory and management.

Keywords

AI advertising dark sides cognitive appraisal theory information quality theory Vietnam

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References

Al-Adwan

A. S.

Al-Horani

M. A.

(2019). Boosting customer E-loyalty: An extended scale of online service quality. Information, 10(12), 327–380. https://doi.org/10.3390/info10120380

Al-Adwan

A. S.

Yaseen

(2023). Solving the product uncertainty hurdle in social commerce: The mediating role of seller uncertainty. International Journal of Information Management Data Insights, 3(1), 100169. https://doi.org/10.1016/j.jjimei.2023.100169

Aladwani

A. M.

Dwivedi

Y. K.

(2018). Towards a theory of SocioCitizenry: Quality anticipation, trust configuration, and approved adaptation of governmental social media. International Journal of Information Management, 43(6), 261–272. https://doi.org/10.1016/j.ijinfomgt.2018.08.009

Andrews

J. E.

Ward

Yoon

J. W.

(2021). UTAUT as a model for understanding intention to adopt AI and related technologies among librarians. The Journal of Academic Librarianship, 47(6), 102437. https://doi.org/10.1016/j.acalib.2021.102437

Baabdullah

A. M.

(2024). Generative conversational AI agent for managerial practices: The role of IQ dimensions, novelty seeking and ethical concerns. Technological Forecasting and Social Change, 198(1), 122951. https://doi.org/10.1016/j.techfore.2023.122951

BDEX . (2023). How Google ads uses machine learning to improve ad targeting and ROAS. BDEX. https://www.bdex.com/blog/how-google-ads-uses-machine-learning-to-improve-ad-targeting-and-roas/

Campbell

Plangger

Sands

Kietzmann

(2022). Preparing for an era of deepfakes and AI-generated ads: A framework for understanding responses to manipulated advertising. Journal of Advertising, 51(1), 22–38. https://doi.org/10.1080/00913367.2021.1909515

Campbell-Sills

Cohan

S. L.

Stein

M. B.

(2006). Relationship of resilience to personality, coping, and psychiatric symptoms in young adults. Behaviour Research and Therapy, 44(4), 585–599. https://doi.org/10.1016/j.brat.2005.05.001

Chen

Xie

Dong

Wang

(2019). Understanding programmatic creative: The role of AI. Journal of Advertising, 48(4), 347–355. https://doi.org/10.1080/00913367.2019.1654421

10.

Cho

Ngien

Tan

M. G.

Chen

Nekmat

(2023). The bright and dark sides of social media use during COVID-19 lockdown: Contrasting social media effects through social liability vs. social support. Computers in Human Behavior, 146(9), 107795. https://doi.org/10.1016/j.chb.2023.107795

11.

Dang

T. Q.

Tan

G. W. H.

E. C. X.

Ooi

K. B.

Metri

Dwivedi

Y. K.

(2023a). How to generate loyalty in mobile payment services? An integrative dual SEM-ANN analysis. International Journal of Bank Marketing, 41(6), 1177–1206. https://doi.org/10.1108/IJBM-05-2022-0202

12.

Dang

T.-Q.

Tran

P.-T.

Nguyen

L.-T.

(2023b). Are you ready for tapping into the metaverse in higher education? Integrated by dual PLS-SEM and ANN approach BT - current and future trends on intelligent technology adoption: Volume 1. In Al-Sharafi

M. A.

Al-Emran

Tan

G. W.-H.

Ooi

K.-B.

(Eds.), Current and future trends on intelligent technology adoption (pp. 63–84). Springer. https://doi.org/10.1007/978-3-031-48397-4_4

13.

Dang Quan

Wei-Han Tan

E. C. X.

Cham

T. H.

Basu

Ooi

K. B.

(2024). Can you resist the virtual temptations? Unveiling impulsive buying in metaverse retail. Asia Pacific Journal of Marketing and Logistics. https://doi.org/10.1108/APJML-09-2023-0911

14.

Darke

P. R.

Ritchie

R. J. B.

Darke

P. R.

Ritchie

R. J. B.

(2007). The defensive consumer: Advertising deception, defensive processing, and distrust. Journal of Marketing Research, 44(1), 114–127. https://doi.org/10.1509/jmkr.44.1.114

15.

Dencheva

(2023). Global AI in marketing revenue 2028 | Statista, Statista. https://www.statista.com/statistics/1293758/ai-marketing-revenue-worldwide/

16.

Ding

H. M.

Hung

K. P.

(2021). The antecedents of visitors’ flow experience and its influence on memory and behavioral intentions in the music festival context. Journal of Destination Marketing & Management, 19(3), 100551. https://doi.org/10.1016/j.jdmm.2020.100551

17.

Dwivedi

Y. K.

Hughes

Ismagilova

Aarts

Coombs

Crick

Duan

Dwivedi

Edwards

Eirug

Galanos

Ilavarasan

P. V.

Janssen

Jones

Kar

A. K.

Kizgin

Kronemann

Lal

Lucini

Williams

M. D.

(2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57(7), 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

18.

Dwivedi

Y. K.

Sharma

Rana

N. P.

Giannakis

Goel

Dutot

(2023). Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technological Forecasting and Social Change, 192(5), 122579. https://doi.org/10.1016/j.techfore.2023.122579

19.

Fernandez

(2022). Facebook, Meta, the metaverse and libraries. Library Hi Tech News, 39(4), 1–3. https://doi.org/10.1108/LHTN-03-2022-0037

20.

Gao

Wang

Xie

(2023). Artificial intelligence in advertising: Advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. Sage Open, 13(4), 1–20. https://doi.org/10.1177/21582440231210759

21.

Helfert

(2013). Impact of information quality on supply chain decisions. Journal of Computer Information Systems, 53(4), 59–67. https://doi.org/10.1080/08874417.2013.11645651

22.

Ghasemaghaei

(2020). The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage. International Journal of Information Management, 50(C), 395–404. https://doi.org/10.1016/j.ijinfomgt.2018.12.011

23.

Görçün

Ö. F.

Pamucar

Biswas

(2023). The blockchain technology selection in the logistics industry using a novel MCDM framework based on Fermatean fuzzy sets and Dombi aggregation. Information Sciences, 635(C), 345–374. https://doi.org/10.1016/j.ins.2023.03.113

24.

Guo

Yuan

Guo

Jin

(2023). Applications of metaverse-related technologies in the services of US urban libraries (18). Library Hi Tech. https://doi.org/10.1108/LHT-10-2022-0486

25.

Gupta

Dennehy

Parra

C. M.

Mäntymäki

Dwivedi

Y. K.

(2023). Fake news believability: The effects of political beliefs and espoused cultural values. Information & Management, 60(2), 103745. https://doi.org/10.1016/j.im.2022.103745

26.

Hair

Hollingsworth

C. L.

Randolph

A. B.

Chong

A. Y. L.

(2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130

27.

Hair

J. F.

Jr Hult

G. T. M.

Ringle

C. M.

Rstedt

M. S.

(2014). A prımer on partıal least squares structural equation modelling. In Practical assessment, research and evaluation. Sage.

28.

Hair

J. F.

Ringle

C. M.

Sarstedt

(2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/mtp1069-6679190202

29.

Haleem

Javaid

Asim Qadri

Pratap Singh

Suman

(2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3(1), 119–132. https://doi.org/10.1016/j.ijin.2022.08.005

30.

Henseler

Dijkstra

T. K.

Sarstedt

Ringle

C. M.

Diamantopoulos

Straub

D. W.

Ketchen

D. J.

Hair

J. F.

Hult

G. T. M.

Calantone

R. J.

Calantone

R. J.

(2014a). Common beliefs and reality about PLS: Comments on rönkkö and evermann. Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928

31.

Henseler

Hubona

Ray

P. A.

(2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382

32.

Henseler

Ringle

C. M.

Sarstedt

(2014b). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

33.

Hew

T. S.

Kadir

S. L. S. A.

(2016). Predicting the acceptance of cloud-based virtual learning environment: The roles of Self Determination and Channel Expansion Theory. Telematics and Informatics, 33(4), 990–1013. https://doi.org/10.1016/j.tele.2016.01.004

34.

S. M.

Hancock

J. T.

Booth

Liu

(2016). Computer-Mediated deception: Strategies revealed by language-action cues in spontaneous communication. Journal of Management Information Systems, 33(2), 393–420. https://doi.org/10.1080/07421222.2016.1205924

35.

Hoang

(2024). Vietnam poised for strong growth in 2024 - Vietnam economic times | VnEconomy. VnEconomy. https://en.vneconomy.vn/vietnam-poised-for-strong-growth-in-2024.htm

36.

Horani

O. M.

Al-Adwan

A. S.

Yaseen

Hmoud

Al-Rahmi

W. M.

Alkhalifah

(2023). The critical determinants impacting artificial intelligence adoption at the organizational level. Information Development, 0(0), 026666692311668. https://doi.org/10.1177/02666669231166889

37.

Innolab.asia . (2023). VIETNAM’S innovation and tech investment landscape in 2023. https://innolab.asia/2023/05/19/vietnams-innovation-and-tech-investment-landscape-in-2023/

38.

Jabeen

Tandon

Sithipolvanichgul

Srivastava

Dhir

(2023). Social media-induced fear of missing out (FoMO) and social media fatigue: The role of narcissism, comparison and disclosure. Journal of Business Research, 159(2), 113693. https://doi.org/10.1016/j.jbusres.2023.113693

39.

Keltner

Haidt

(2003). Approaching awe, a moral, spiritual, and aesthetic emotion. Cognition & Emotion, 17(2), 297–314. https://doi.org/10.1080/02699930302297

40.

Khare

Kautish

Khare

(2023). The online flow and its influence on awe experience: An AI-enabled e-tail service exploration. International Journal of Retail & Distribution Management, 51(6), 713–735. https://doi.org/10.1108/IJRDM-07-2022-0265

41.

Kim

J. M.

Park

K. K. C.

Mariani

M. M.

(2023). Do online review readers react differently when exposed to credible versus fake online reviews? Journal of Business Research, 154(1), 113377. https://doi.org/10.1016/j.jbusres.2022.113377

42.

Kim

S. E.

Lee

K. Y.

Shin

S.Il

Yang

S. B.

(2017). Effects of tourism information quality in social media on destination image formation: The case of Sina Weibo. Information & Management, 54(6), 687–702. https://doi.org/10.1016/j.im.2017.02.009

43.

Köbis

N. C.

Doležalová

Soraperra

(2021). Fooled twice: People cannot detect deepfakes but think they can. iScience, 24(11), 103364. https://doi.org/10.1016/j.isci.2021.103364

44.

Lazarus

R. S.

Folkman

(1984). Managing stress: Mindfulness-based strategies. In Stress apraisal and coping. Springer Publishing Company.

45.

Lee

Y. W.

Strong

D. M.

Kahn

B. K.

Wang

R. Y.

(2002). Aimq: A methodology for information quality assessment. Information & Management, 40(2), 133–146. https://doi.org/10.1016/S0378-7206(02)00043-5

46.

T. G.

Zhang

C. B.

Chang

Zheng

(2024). The impact of AI identity disclosure on consumer unethical behavior: A social judgment perspective. Journal of Retailing and Consumer Services, 76(6), 103606. https://doi.org/10.1016/j.jretconser.2023.103606

47.

MacKenzie

S. B.

Podsakoff

P. M.

Podsakoff

N. P.

(2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293. https://doi.org/10.2307/23044045

48.

Morales-Serazzi

González-Benito

Ó.

Martos-Partal

(2023). A new perspective of BDA and information quality from final users of information: A multiple study approach. International Journal of Information Management, 73(C), 102683. https://doi.org/10.1016/j.ijinfomgt.2023.102683

49.

Mustak

Salminen

Mäntymäki

Rahman

Dwivedi

Y. K.

(2023). Deepfakes: Deceptions, mitigations, and opportunities. Journal of Business Research, 154(2), 113368. https://doi.org/10.1016/j.jbusres.2022.113368

50.

Neumann

Tucker

C. E.

Whitfield

(2019). Frontiers: How effective is third-party consumer profiling? Evidence from field studies. Marketing Science, 38(6), 918–926. https://doi.org/10.1287/mksc.2019.1188

51.

Nguyen

B.-H. T.

Dang

T.-Q.

Nguyen

L.-T.

Tran

T.-T. T.

(2024). Are we ready for education in metaverse? PLS-SEM analysis. Edelweiss Applied Science and Technology, 8(2), 73–83. https://doi.org/10.55214/25768484.v8i2.693

52.

Nguyen

B. H. T.

T. H.

Dang

T. Q.

Nguyen

L. T.

Nguyen

L. T.

(2023a). What role does AI chatbot perform in the F&B industry? Perspective from loyalty and value Co-creation: Integrated PLS-SEM and ANN techniques. Journal of Law and Sustainable Development, 11(4), e739–e794. https://doi.org/10.55908/sdgs.v11i4.794

53.

Nguyen

(2024). AI will change the game of tech business in Vietnam by 2024 (E27). https://e27.co/ai-will-change-the-game-of-tech-business-in-vietnam-by-2024-this-is-what-you-need-to-know-about-it-20240304/

54.

Nguyen

L. T.

Dwivedi

Y. K.

Tan

G. W. H.

E. C. X.

P. S.

Ooi

K. B.

(2022). Unlocking pathways to mobile payment satisfaction and commitment. Journal of Computer Information Systems, 63(4), 998–1015. https://doi.org/10.1080/08874417.2022.2119444

55.

Nguyen

L.-T.

Nguyen

V. P.

Dang

D. T. V.

(2022). Critical factors affecting the adoption of artificial intelligence: An empirical study in Vietnam. The Journal of Asian Finance, Economics and Business(JAFEB), 9(5), 225–237. https://doi.org/10.13106/jafeb.2022.vol9.no5.225

56.

Nguyen

L.-T.

Phan

T.-T. C.

Dang

D.-V. T.

Tran

T.-T. T.

(2023c). Mobile payment adoption in Vietnam: A two-staged SEM-ANN approach BT - current and future trends on intelligent technology adoption: Volume 1. In Al-Sharafi

M. A.

Al-Emran

Tan

G. W.-H.

Ooi

K.-B.

(Eds.), Current and future trends on intelligent technology adoption (pp. 209–228). Springer. https://doi.org/10.1007/978-3-031-48397-4_11

57.

Paul

(2020). The dark side of artificial intelligence. Idees. https://www.marketingaiinstitute.com/blog/dark-side-of-artificial-intelligence-in-marketing

58.

Phi

(2024). Vietnam innovation challenge 2024: Sparking innovation for semiconductor and AI industries. Hanoi Times. https://hanoitimes.vn/vietnam-innovation-challenge-2024-sparking-innovation-for-semiconductor-and-ai-industries-326360.html

59.

Podsakoff

P. M.

MacKenzie

S. B.

Lee

J. Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

60.

Rasool

S. F.

Wang

Zhang

Samma

(2020). Sustainable work performance: The roles of workplace violence and occupational stress. International Journal of Environmental Research and Public Health, 17(3), 912. https://doi.org/10.3390/ijerph17030912

61.

Rizvanović

Zutshi

Grilo

Nodehi

(2023). Linking the potentials of extended digital marketing impact and start-up growth: Developing a macro-dynamic framework of start-up growth drivers supported by digital marketing. Technological Forecasting and Social Change, 186(PA), 122128. https://doi.org/10.1016/j.techfore.2022.122128

62.

Roni

S. M.

Djajadikerta

Ahmad

M. A. N.

(2015). PLS-SEM approach to second-order factor of deviant behaviour: Constructing perceived behavioural control. Procedia Economics and Finance, 28(2015), 249–253. https://doi.org/10.1016/s2212-5671(15)01107-7

63.

Seppänen

Virrantaus

(2015). Shared situational awareness and information quality in disaster management. Safety Science, 77(2015), 112–122. https://doi.org/10.1016/j.ssci.2015.03.018

64.

Shamala

Ahmad

Zolait

Sedek

(2017). Integrating information quality dimensions into information security risk management (ISRM). Journal of Information Security and Applications, 36(C), 1–10. https://doi.org/10.1016/j.jisa.2017.07.004

65.

Shannon

Lee

K. A.

(2008). HIV-infected mothers’ perceptions of uncertainty, stress, depression and social support during HIV viral testing of their infants. Archives of Women's Mental Health, 11(4), 259–267. https://doi.org/10.1007/s00737-008-0023-8

66.

Sonu Yadav . (2023). How to apply AI in your digital marketing strategy. SEo Vendor. https://seovendor.co/how-to-apply-ai-in-your-digital-marketing-strategy/

67.

Suh

(2023). How users cognitively appraise and emotionally experience the metaverse: Focusing on social virtual reality. Information Technology and People. https://doi.org/10.1108/ITP-06-2022-0461

68.

Trinh

(2023). The dramatic rise of artificial intelligence in Vietnam. Neurond. https://www.neurond.com/blog/dramatic-rise-of-artificial-intelligence-in-vietnam

69.

Victor

Grenade

Boldy

(2005). Measuring loneliness in later life: A comparison of differing measures. Reviews in Clinical Gerontology, 15(1), 63–70. https://doi.org/10.1017/S0959259805001723

70.

Vincent

(2023). How we can protect against deceptive AI political ads. Forbes. https://www.forbes.com/sites/subramaniamvincent/2023/08/29/why-generative-ai-political-ads-are-an-opportunity-to-bring-ai-literacy-to-everyone/?sh=4cb871624536

71.

Wang

R. Y.

Strong

D. M.

(1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33. https://doi.org/10.1080/07421222.1996.11518099

72.

Wang

S. T.

(2020). The effects of risk appraisal and coping appraisal on the adoption intention of m-payment. International Journal of Bank Marketing, 38(1), 21–33. https://doi.org/10.1108/IJBM-10-2018-0272

73.

Watts

Adriano

(2021). Uncovering the sources of machine-learning mistakes in advertising: Contextual bias in the evaluation of semantic relatedness. Journal of Advertising, 50(1), 26–38. https://doi.org/10.1080/00913367.2020.1821411

74.

Wong

L. W.

Tan

G. W. H.

Lee

V. H.

Ooi

K. B.

Sohal

(2021). Psychological and system-related barriers to adopting blockchain for operations management: An artificial neural network approach. IEEE Transactions on Engineering Management, 70(1), 67–81. https://doi.org/10.1109/TEM.2021.3053359

75.

Wright

K. B.

(1999). Computer-mediated support groups: An examination of relationships among social support, perceived stress, and coping strategies. Communication Quarterly, 47(4), 402–414. https://doi.org/10.1080/01463379909385570

76.

Yan

L. Y.

Tan

G. W. H.

Loh

X. M.

Hew

J. J.

Ooi

K. B.

(2021). QR code and mobile payment: The disruptive forces in retail. Journal of Retailing and Consumer Services, 58(C), 102300. https://doi.org/10.1016/j.jretconser.2020.102300

77.

Yuan

Liu

Zhang

(2020). Determining the antecedents of mobile payment loyalty: Cognitive and affective perspectives. Electronic Commerce Research and Applications, 41(49), 100971. https://doi.org/10.1016/j.elerap.2020.100971

78.

Zafar

A. U.

Shen

Shahzad

Islam

(2021). Relation of impulsive urges and sustainable purchase decisions in the personalized environment of social media. Sustainable Production and Consumption, 25(C), 591–603. https://doi.org/10.1016/j.spc.2020.11.020

The Dark Sides of AI Advertising: The Integration of Cognitive Appraisal Theory and Information Quality Theory

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