Health Psychology Research / HPR / Online First / DOI: 10.14440/hpr.0430
RESEARCH ARTICLE

Personality and Adaptive Coping in the Digital Age: Psychological Pathways to Emerging Technology Adoption, with a Focus on Artificial Intelligence

Daniela-Elena Lițan*
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1 Department of Psychology, Faculty of Psychology and Educational Sciences, West University of Timișoara, Timișoara, Timiș 300223, Romania
Submitted: 12 December 2025 | Revised: 4 January 2026 | Accepted: 7 January 2026 | Published: 3 February 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Background

Rapidly developing technologies, including artificial intelligence (AI), the Internet of Things (IoT), and blockchain, continue to reshape social structures, professional domains, and daily life.

Objective

This study investigates how individual personality characteristics shape people’s willingness to adopt and engage with such innovations.

Methods

A cross-sectional survey was conducted with 202 Romanian adults (aged 18–61), who completed validated measures assessing personality traits (Big Five agency, beliefs, conscientiousness, dynamism, and morality), cognitive–emotional coping strategies (cognitive emotion regulation questionnaire), and the use of AI, IoT, and blockchain technologies (hours per day). Data were analyzed using Jamovi, applying descriptive statistics, correlations, multiple regression with Bonferroni corrections, and mediation/moderation analyses with bootstrap resampling.

Results

The analyses indicated no evidence of common method bias. Among the three tested models, only AI use was significantly predicted by personality factors, with extraversion exerting a positive effect and maturity a negative effect. Age moderated the extraversion–AI relationship, suggesting stronger effects among younger participants. Mediation analyses showed that adaptive coping strategies did not play a significant mediating role.

Conclusion

Personality factors, particularly extraversion and maturity, play a central role in the adoption of AI, while coping strategies showed limited explanatory power. The moderating effect of age suggests that younger individuals may benefit more from extraversion in engaging with digital technologies. These findings underscore the importance of considering psychological factors in understanding digital transformation and call for further research into how individual differences shape technology use.

Keywords
Personality factors
Cognitive–emotional coping
Artificial intelligence
Internet of Things
Blockchain
Funding
None.
Conflict of interest
The author declares no competing interests.
References
  1. Tussyadiah I. A review of research into auto­mation in tourism: Launching the curated annals of tourism research collection on artificial intel­ligence and robotics in tourism. Ann Tour Res. 2020;81:102883. doi: 10.1016/j.annals.2020.102883

 

  1. Papadakis S, Striuk AM, Kravtsov HM, Shyshkina MP, Marienko MV, Danylchuk HB. Embracing digital innovation and cloud technologies for transforma­tive learning experiences. Vol. 3679. In: Proceedings of the 11th Workshop on Cloud Technologies in Education (CTE 2023). CEUR Workshop Proceedings; 2024. p. 1-21. Available from: https://ceur-ws.org/ vol-3679/paper00.pdf [Last accessed on 2025 Aug 15]

 

  1. Woodcock J. Artificial intelligence at work: The problem of managerial control from call centers to transport platforms. Front Artif Intell. 2022;5:888817. doi: 10.3389/frai.2022.888817

 

  1. de Saint Laurent C. In defence of machine learn­ing: debunking the myths of artificial intelligence. Eur J Psychol. 2018;14(4):734-747. doi: 10.5964/ejop. v14i4.1823

 

  1. Haghi M, Thurow K, Stoll R. Wearable devices in medical Internet of Things: Scientific research and commercially available devices. Healthc Inform Res. 2017;23(1):4-15. doi: 10.4258/hir.2017.23.1.4

 

  1. Fernandez-Gago C, Ferraris D, Roman R, Lopez J. Trust interoperability in the inter­net of things. Internet Things. 2024;26:101226. doi: 10.1016/j.iot.2024.101226

 

  1. Ismail L, Hameed H, AlShamsi M, AlHammadi M, AlDhanhani N. Towards a blockchain deployment at UAE University. In: Proceedings of the 2019 International Conference on Blockchain Technology; 2019. p. 30-38. doi: 10.1145/3320154.3320156

 

  1. Roeck D, Schöneseiffen F, Greger M, Hofmann E. Analyzing the potential of DLT-based applications in smart factories. In: Digitalization Cases: How Organizations Rethink their Business for the Digital Age. Cham, Switzerland: Springer; 2020. p. 245-266. doi: 10.1007/978-3-030-44337-5_12

 

  1. Vinciarelli A, Mohammadi G. A survey of personality computing. IEEE Trans Affect Comput. 2014;5(3):273-291. doi: 10.1109/ TAFFC.2014.2330816

 

  1. Yang L, Li S, Luo X, et al. Computational per­sonality: A survey. Soft Comput. 2022;26(18): 9587-9605. doi: 10.1007/s00500-022-06786-6

 

  1. Cohen F, Lazarus RS. Coping with stresses of illness. In: Stone GC, Cohen F, Adler NE, editors. Health Psychology. San Francisco, CA: Jossey-Bass; 1979. p. 217-254.

 

  1. Nicoara RD, Coman HG, Cosman D. The rela­tionships between depression, suicide risk and emotional cognitive coping. BRAIN Broad Res Artif Intell Neurosci. 2022;13(3):85-103. doi: 10.18662/ brain/13.3/355

 

  1. Carver CS, Connor-Smith J. Personality and coping. Annu Rev Psychol. 2010;61:679-704. doi: 10.1146/annurev.psych.093008.100352

 

  1. Ragu-Nathan TS, Tarafdar M, Ragu-Nathan BS, Tu Q. The consequences of technostress for end users in organizations: Conceptual development and empirical validation. Inf Syst Res. 2008;19(4): 417-433. doi: 10.1287/isre.1070.0165

 

  1. Bordi L, Okkonen J, Mäkiniemi JP, Heikkilä- Tammi K. Communication in the digital work envi­ronment: Implications for wellbeing at work. Nord J Work Life Stud. 2018;8(S3):29-48. doi: 10.18291/ njwls.v8iS3.105275

 

  1. Köffer S. Designing the digital workplace of the future - what scholars recommend to practition­ers. In: Proceedings of the Thirty Sixth International Conference on Information Systems: Exploring the Information Frontier. Fort Worth, TX; 2015.

 

  1. Tomyuk ON, Dyachkova MA, Shutaleva AV. Issues of modeling smart personality - human image of the digital age. Econ Consult. 2020;31(3):115-124. doi: 10.46224/ecoc.2020.3.8

 

  1. Maslej N, Fattorini L, Perrault R, et al. The AI Index 2024 Annual Report. AI Index Steering Committee, Institute for Human-Centered AI. Stanford University; 2024. Available from: https://aiindex. stanford.edu/wp-content/uploads/2024/05/hai_ ai-index-report-2024.pdf [Last accessed on 2025 Aug 20].

 

  1. Chang PC, Zhang W, Cai Q, Guo H. Does AI-driven technostress promote or hinder employ­ees’ artificial intelligence adoption intention? A moderated mediation model of affective reac­tions and technical self-efficacy. Psychol Res Behav Manag. 2024;17:413-427. doi: 10.2147/PRBM. S441444

 

  1. Salah M, Alhalbusi H, Ismail MM, Abdelfattah F. Chatting with ChatGPT: Decoding the mind of chatbot users and unveiling the intri­cate connections between user perception, trust and stereotype perception on self-esteem and psy­chological well-being. Curr Psychol. 2024;43(9): 7843-7858. doi: 10.1007/s12144-023-04989-0

 

  1. Kim BJ, Kim MJ, Lee J. The impact of an unstable job on mental health: The critical role of self-efficacy in artificial intelligence use. Curr Psychol. 2024;43(18):16445-16462. doi: 10.1007/ s12144-023-05595-w

 

  1. Xu G, Xue M, Zhao J. The association between artificial intelligence awareness and employee depression: The mediating role of emotional exhaustion and the moderating role of per­ceived organizational support. Int J Environ Res Public Health. 2023;20(6):5147. doi: 10.3390/ ijerph20065147

 

  1. Wang K, Ruan Q, Zhang X, Fu C, Duan B. Pre-service teachers’ GenAI anxiety, technology self-efficacy, and TPACK: Their structural rela­tions with behavioral intention to design GenAI-assisted teaching. Behav Sci (Basel). 2024;14(5):373. doi: 10.3390/bs14050373

 

  1. Malle BF, Guglielmo S, Monroe AE. A the­ory of blame. Psychol Inq. 2014;25(2):147-186. doi: 10.1080/1047840X.2014.877340

 

  1. Shin D. The effects of explainability and causability on perception, trust, and accept­ance: Implications for explainable AI. Int J Hum Comput Stud. 2021;146:102551. doi: 10.1016/j. ijhcs.2020.102551

 

  1. Glikson E, Woolley AW. Human trust in artifi­cial intelligence: Review of empirical research. Acad Manage Ann. 2020;14(2):627-660. doi: 10.5465/ annals.2018.0057

 

  1. Chen S, Ebrahimi OV, Cheng C. New perspective on digital well-being by distinguishing digital com­petency from dependency: Network approach. J Med Internet Res. 2025;27:e70483. doi: 10.2196/70483

 

  1. Grassini S, Thorp S, Sævild Ree A, Sevic A, Cipriani E. Attitudes toward technology and artificial intelligence: The role of demographic and personality factors. In: Proceedings of the 36th Annual Conference of the European Association of Cognitive Ergonomics (ECCE 2025). ACM; 2025. doi: 10.1145/3746175.3747190

 

  1. Sandner P, Gross J, Richter R. Convergence of blockchain, IoT, and AI. Front Blockchain. 2020;3:522600. doi: 10.3389/fbloc.2020.522600

 

  1. Marikyan D, Papagiannidis S. Technology acceptance model: A review. In: Papagiannidis S, editor. TheoryHub Book. Newcastle: Open. NCL; 2023. Available from: https://open.ncl.ac.uk/ theory-library/technology-acceptance-model.pdf [Last accessed on 2025 Nov 11].

 

  1. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q. 2003;27(3):425-478. doi: 10.2307/30036540

 

  1. Marikyan D, Papagiannidis S. Unified theory of acceptance and use of technology: A review. In: Papagiannidis S, editor. TheoryHub Book. Newcastle: Open. NCL; 2023. Available from: https://open.ncl. ac.uk/theory-library/unified-theory-of-accept­ance-and-use-of-technology.pdf [Last accessed on 2025 Oct 10].

 

  1. Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 2012;36(1):157-178. doi: 10.2307/41410412

 

  1. Ozili PK. Technology impact model: A transi­tion from the technology acceptance model. AI Soc. 2025;40:1-3. doi: 10.1007/s00146-024-01896-1

 

  1. Ali D, Bowen D, Deininger KW. Personality traits, technology adoption, and technical efficiency: Evidence from smallholder rice farms in Ghana. Policy Research Working Paper Series. The World Bank; 2017. Available from: https://documents. worldbank.org/curated/en/727211486054089844/ pdf/wps7959.pdf [Last accessed on 2025 Oct 16].

 

  1. Costa PT, McCrae RR. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five Factor Inventory (NEO-FFI): Professional Manual. Odessa, FL: Psychological Assessment Resources; 1992.

 

  1. Goldberg LR. The development of markers for the Big-Five factor structure. Psychol Assess. 1992;4(1):26-42. doi: 10.1037/1040-3590.4.1.26

 

  1. Nießen D, Danner D, Spengler M, Lechner CM. Big five personality traits predict successful transi­tions from school to vocational education and train­ing: A large-scale study. Front Psychol. 2020;11:1827. doi: 10.3389/fpsyg.2020.01827

 

  1. Minulescu M. ABCD-M. Manual Tehnic Şi Interpretativ [ ABCD-M. Technical and Interpretative Manual]. Cluj-Napoca: Sinapsis; 2008. [In Romanian].

 

  1. McCrae RR, Costa PT. Personality in Adulthood: A Five-Factor Theory Perspective. 2nd ed. New York, NY: Guilford Press; 2003. doi: 10.4324/9780203428412

 

  1. Smith J, Guimond FA, Aucoin P, et al. Examining high school students’ personality traits of extra­version and emotional stability in relation to their academic expectation and value appraisals. Interdiscip Educ Psychol. 2021;2(3):6. doi: 10.31532/ InterdiscipEducPsychol.2.3.006

 

  1. Zhu Y, Jiang H, Zhou Z. Information adoption behavior in online healthcare communities from the perspective of personality traits. Front Psychol. 2022;13:973522. doi: 10.3389/fpsyg.2022.973522

 

  1. Doménech P, Tur-Porcar AM, Mestre-Escrivá V. Emotion regulation and self-efficacy: The medi­ating role of emotional stability and extraversion in adolescence. Behav Sci (Basel). 2024;14(3):206. doi: 10.3390/bs14030206

 

  1. Gross JJ. The emerging field of emotion reg­ulation: An integrative review. Rev Gen Psychol. 1998;2(3):271-299. doi: 10.1037/1089-2680.2.3.271

 

  1. Nolen-Hoeksema S. Emotion regulation and psychopathology: The role of gender. Annu Rev Clin Psychol. 2012;8:161-187. doi: 10.1146/ annurev-clinpsy-032511-143109

 

  1. Thompson RA. Emotion regulation: A theme in search of definition. Monogr Soc Res Child Dev. 1994;59(2-3):25-52. doi: 10.1111/j.1540-5834.1994. tb01276.x

 

  1. van den Heuvel MWH, Stikkelbroek YAJ, Bodden DHM, van Baar AL. Coping with stress­ful life events: Cognitive emotion regulation pro­files and depressive symptoms in adolescents. Dev Psychopathol. 2020;32(3):985-995. doi: 10.1017/ S0954579419000920

 

  1. Statista. Artificial intelligence (AI) Market Size Worldwide from 2020 to 2030 (in Billion U.S. Dollars). Statista; 2024. Available from: https://www.stati­sta.com/forecasts/1474143/global-ai-market-size [Last accessed on 2024 Nov 17].

 

  1. Ministerul Cercetării, Inovării și Digitalizării (MCID). Strategia Națională în Domeniul Inteligenței Artificiale 2024–2027. [National Strategy in the Field of Artificial Intelligence 2024– 2027]; 2024. Available from: https://www.mcid.gov. ro/programe-nationale/strategia-nationala-in-do­meniul-inteligentei-artificiale-2024-2027 [Last accessed on 2024 Nov 17]. [In Romanian].

 

  1. Lozano EB, Laurent SM. The effect of admitting fault versus shifting blame on expectations for oth­ers to do the same. PLoS One. 2019;14(3):e0213276. doi: 10.1371/journal.pone.0213276

 

  1. Garnefski N, Kraaij V, Spinhoven P. CERQ: Manual de Utilizare a Chestionarului de Coping Cognitiv-Emoțional [CERQ: Manual for the Use of the Cognitive Emotion Regulation Questionnaire]. Cluj-Napoca: ASCR; 2010. [In Romanian].

 

  1. Łukasiewicz J, Kaczmarek BLJ. Health care workers strategies for coping with stress. Acta Neuropsychol. 2023;21(4):387-394. doi: 10.5604/01.3001.0053.8853

 

  1. Theodoratou M, Argyrides M. Neuropsychological insights into coping strategies: Integrating the­ory and practice in clinical and therapeutic con­texts. Psychiatry Int. 2024;5(1):53-73. doi: 10.3390/ psychiatryint5010005

 

  1. Riepenhausen A, Wackerhagen C, Reppmann ZC, et al. Positive cognitive reap­praisal in stress resilience, mental health, and well-being: A comprehensive systematic review. Emotion Rev. 2022;14(4):310-331. doi: 10.1177/17540739221114642

 

  1. Maftei A, Măirean C. Put your phone down! perceived phubbing, life satisfaction, and psy­chological distress: the mediating role of loneli­ness. BMC Psychol. 2023;11(1):332. doi: 10.1186/ s40359-023-01359-0

 

  1. The Jamovi Project. Jamovi. Version 2.3; 2022. Available from: https://www.jamovi.org [Last accessed on 2024 Nov 24].

 

  1. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavio­ral research: A critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5): 879-903. doi: 10.1037/0021-9010.88.5.879

 

  1. Bagozzi RP, Yi Y, Phillips LW. Assessing con­struct validity in organizational research. Adm Sci Q. 1991;36(3):421. doi: 10.2307/2393203

 

  1. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a silver bullet. J Mark Theory Pract. 2011;19(2): 139-152. doi: 10.2753/MTP1069-6679190202

 

  1. Kock N. Common method bias: A full colline­arity assessment method for PLS-SEM. In: Partial Least Squares Path Modeling. Cham, Switzerland: Springer International Publishing; 2017. p. 245-257. doi: 10.1007/978-3-319-64069-3_11

 

  1. Măricuţoiu L, Zogmaister C. The mediating role of preference for online interactions in the rela­tionship between self-knowledge and problematic use of social networks. Cyberpsychol J Psychosoc Res Cyberspace. 2023;17(4):1. doi: 10.5817/CP2023-4-1

 

  1. Espeland A, Kristoffersen PM, Bråten LCH, et al. Longitudinal relationship between reduced Modic change edema and disability and pain in patients with chronic low back pain. Spine (Phila Pa 1976). 2023;48:1699-1708. doi: 10.1097/ BRS.0000000000004837

 

  1. Liu S, Hu W, Yang Y, Yang F. Body dis­satisfaction and smartphone addiction: The mediation role of intrusive imagery and fear of neg­ative evaluation. Front Psychol. 2023;14:1217220. doi: 10.3389/fpsyg.2023.1217220

 

  1. Yue H, Yue X, Zhang X, Liu B, Bao H. Exploring the relationship between social exclusion and smartphone addiction: The mediating roles of loneliness and self-control. Front Psychol. 2022;13:945631. doi: 10.3389/fpsyg.2022.945631

 

  1. Neves J, Turel O, Oliveira T. Explaining social media use reduction as an adap­tive coping mechanism: The roles of pri­vacy literacy, social media addiction and exhaustion. Inf Syst Manag. 2024;42(1):89-102. doi: 10.1080/10580530.2024.2332187

 

  1. Institutul Național de Statistică din România (INSR). Reasons for not Using Internet of Things (IoT) Devices in Romania in 2022 [Graph]. Statista; 2022. Available from: https://www.statista.com/ statistics/1394854/romania-reasons-for-not-us­ing-internet-of-things-iot-devices [Last accessed on 2024 Nov 23].

 

  1. Ipsos. Have You Heard of Cryptocurrency? [Graph]. Statista; 2022. Available from: https:// w w w.statista.com/statistics/1394958/roma­nia-awareness-and-ownership-of-cryptocurrency [Last accessed on 2024 Nov 23].

 

  1. Statista. Cryptocurrencies - Romania. Statista; 2024. Available from: https://0610zsm9f-y-https-www-statista-com.z.e-nformation.ro/outlook/ dmo/fintech/digital-assets/cryptocurrencies/roma­nia [Last accessed on 2024 Nov 23].

 

  1. Mohamed AM, Shaaban TS, Bakry SH, Guillén-Gámez FD, Strzelecki A. Empowering the faculty of education students: Applying AI’s potential for motivating and enhancing learning. Innov High Educ. 2025;50(2):587-609. doi: 10.1007/ s10755-024-09747-z

 

  1. Cox M, Cocks B, Watt SE, Temple EC. Personality, opinion strength, and social media use - not such a straightforward rela­tionship. Aust J Psychol. 2025;77(1):2451156. doi: 10.1080/00049530.2025.2451156

 

  1. Dilawar S, Liang G, Elahi MZ, et al. Interpreting the impact of extraversion and neuroticism on social media addiction among university students of Pakistan: A mediated and moderated model. Acta Psychol. 2022;230:103764. doi: 10.1016/j. actpsy.2022.103764

 

  1. Shanahan DE, Russell CA, Granados NF. Extraversion, technology proclivity, and partici­pation in technology-mediated, sharing economy markets: An abstract. In: Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Cham, Switzerland: Springer; 2023. p. 397-398. doi: 10.1007/978-3-031-24687-6_169

 

  1. Li Y, Wu B, Huang Y, Luan S. Developing trustworthy artificial intelligence: Insights from research on interpersonal, human-automation, and human-AI trust. Front Psychol. 2024;15:1382693. doi: 10.3389/fpsyg.2024.1382693

 

  1. Donnellan MB, Lucas RE. Age differences in the Big Five across the life span: Evidence from two national samples. Psychol Aging. 2008;23(3): 558-566. doi: 10.1037/a0012897

 

  1. Stephan Y, Sutin AR, Kornadt A, Canada B, Terracciano A. Personality and subjective age: Evidence from six samples. Psychol Aging. 2022;37(3):401-412. doi: 10.1037/pag0000678

 

  1. Aldasoro I, Armantier O, Doerr S, Gambacorta L, Oliviero T. The gen AI gender gap. Econ Lett. 2024;241:111814. doi: 10.1016/j. econlet.2024.111814

 

  1. Ofosu-Ampong K. Gender differences in perception of artificial intelligence-based tools. J Digit Art Humanit. 2023;4(2):52-56. doi: 10.33847/2712-8149.4.2_6

 

  1. Russo C, Romano L, Clemente D, et al. Gender differences in artificial intelligence: The role of artificial intelligence anxiety. Front Psychol. 2025;16:1559457. doi: 10.3389/fpsyg.2025.1559457

 

  1. Bowden-Green T, Hinds J, Joinson A. How is extraversion related to social media use? A liter­ature review. Pers Individ Dif. 2020;164:110040. doi: 10.1016/j.paid.2020.110040

 

  1. David LT, Nitu AG. Personality traits differ­ences in young people opting for online vs. tradi­tional dating. Bull Transilv Univ Brasov Ser VII Soc Sci Law. 2023;16(65):143–152. doi: 10.31926/but. ssl.2023.16.65.2.1

 

  1. Joshi A, Das S, Sekar S. How Big Five personality traits affect information and communication tech­nology use: A meta-analysis. Australas J Inf Syst. 2023;27:1-39. doi: 10.3127/ajis.v27i0.3985

 

  1. Carstensen LL, Turan B, Scheibe S, et al. Emotional experience improves with age: Evidence based on over 10 years of experience sampling. Psychol Aging. 2011;26(1):21-33. doi: 10.1037/ a0021285

 

  1. Landers RN, Lounsbury JW. An investigation of Big Five and narrow personality traits in rela­tion to Internet usage. Comput Human Behav. 2006;22(2):283-293. doi: 10.1016/j.chb.2004.06.001

 

  1. Conner D. Managing at the Speed of Change: How Resilient Managers Succeed and Prosper Where Others Fail. 1st ed. New York, NY: Villard Books; 1992.

 

  1. Oreg S. Resistance to change: Developing an individual differences meas­ure. J Appl Psychol. 2003;88(4):680-693. doi: 10.1037/0021-9010.88.4.680

 

  1. Bondarchuk O, Balakhtar V, Pinchuk N, Pustovalov I, Pavlenok K. Adaptation of cop­ing strategies to reduce the impact of stress and lonelines on the psychological well-being of adults. J Law Sustain Dev. 2023;11(10):e1852. doi: 10.55908/sdgs.v11i10.1852

 

  1. Heffer T, Willoughby T. A count of coping strategies: A longitudinal study investigating an alternative method to understanding coping and adjustment. PLoS One. 2017;12(10):e0186057. doi: 10.1371/journal.pone.0186057

 

  1. Stada. Share of People Who Experienced or Felt on the Verge of Burnout Europe in 2021, by Country [Graph]. Statista; 2022. Available from: https:// www.statista.com/statistics/1249649/experienc­es-of-burnout-in-europe [Last accessed on 2024 Nov 24].

 

  1. Pleșca M. Development of defense mechanisms and strategies efficient coping in adolescents. J Rom Lit Stud. 2022;31:68-76.

 

  1. Holahan CJ, Ragan JD, Moos RH. Stress. In: Reference Module in Neuroscience and Biobehavioral Psychology. Netherlands: Elsevier; 2017. doi: 10.1016/ B978-0-12-809324-5.05724-2
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