This study examined the factors influencing the adoption of loyalty programs powered by blockchain (BBLPs) among older adult consumers in the United States. Drawing on established technology acceptance models—such as the Unified Theory of Acceptance and Use of Technology (UTAUT), the Technology Acceptance Model 3 (TAM3), and the Cognitive–Affective–Normative (CAN) framework—a conceptual model is proposed that integrates six explanatory variables: perceived usefulness (PU), perceived ease of use (PEoU), perceived external control (PEC), positive emotions (PEM), negative emotions (NEM), and subjective norm (SN). To assess the model's explanatory power and predictive accuracy regarding the intention to use BBLPs, five multilayer perceptron neural networks with different architectures were implemented. The study pursues two main objectives: (1) to evaluate the explanatory and predictive capabilities of the proposed model using deep learning techniques, and (2) to determine the relative importance of each explanatory variable using the permutation feature importance method. The results show that all neural network (NN) models achieved high explanatory power and strong predictive performance under Monte Carlo cross-validation. A single-hidden-layer network based on Kolmogorov's theorem (13 neurons) offered the best balance between fit and predictive ability. PU consistently emerged as the most influential predictor of usage intention, whereas NEM and SN were the least relevant factors across all configurations. The relative importance of PEoU, PEC, and PEM varied across architectures. These findings confirm the primacy of cognitive variables over affective and normative factors in explaining the acceptance of BBLPs and highlight the usefulness of NNs for modeling technology adoption in marketing research. They also demonstrate that explainable NNs can simultaneously enhance prediction and provide transparent, ranked insights for managerial prioritization in consumers' loyalty settings.
Citation: Jorge de Andrés-Sánchez, Mario Arias-Oliva, Mar Souto-Romero, Miguel Llorens-Marín. Evaluating explanatory factors of the acceptance of blockchain-based loyalty programs with neural networks[J]. Data Science in Finance and Economics, 2026, 6(1): 58-84. doi: 10.3934/DSFE.2026003
This study examined the factors influencing the adoption of loyalty programs powered by blockchain (BBLPs) among older adult consumers in the United States. Drawing on established technology acceptance models—such as the Unified Theory of Acceptance and Use of Technology (UTAUT), the Technology Acceptance Model 3 (TAM3), and the Cognitive–Affective–Normative (CAN) framework—a conceptual model is proposed that integrates six explanatory variables: perceived usefulness (PU), perceived ease of use (PEoU), perceived external control (PEC), positive emotions (PEM), negative emotions (NEM), and subjective norm (SN). To assess the model's explanatory power and predictive accuracy regarding the intention to use BBLPs, five multilayer perceptron neural networks with different architectures were implemented. The study pursues two main objectives: (1) to evaluate the explanatory and predictive capabilities of the proposed model using deep learning techniques, and (2) to determine the relative importance of each explanatory variable using the permutation feature importance method. The results show that all neural network (NN) models achieved high explanatory power and strong predictive performance under Monte Carlo cross-validation. A single-hidden-layer network based on Kolmogorov's theorem (13 neurons) offered the best balance between fit and predictive ability. PU consistently emerged as the most influential predictor of usage intention, whereas NEM and SN were the least relevant factors across all configurations. The relative importance of PEoU, PEC, and PEM varied across architectures. These findings confirm the primacy of cognitive variables over affective and normative factors in explaining the acceptance of BBLPs and highlight the usefulness of NNs for modeling technology adoption in marketing research. They also demonstrate that explainable NNs can simultaneously enhance prediction and provide transparent, ranked insights for managerial prioritization in consumers' loyalty settings.
| [1] |
Afifa MM, Vo Van H, Le Hoang Van T (2023) Blockchain adoption in accounting by an extended UTAUT model: empirical evidence from an emerging economy. J Financ Report Account 21: 5–44. https://doi.org/10.1108/JFRA-12-2021-0434 doi: 10.1108/JFRA-12-2021-0434
|
| [2] |
Agarwal R, Mehrotra A, Misra D (2022) Customer happiness as a function of perceived loyalty program benefits—A quantile regression approach. J Retail Consum Serv 64: 102770. https://doi.org/10.1016/j.jretconser.2021.102770 doi: 10.1016/j.jretconser.2021.102770
|
| [3] |
Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Dec Processes 50: 179–211. https://doi.org/10.1016/0749-5978(91)90020-T doi: 10.1016/0749-5978(91)90020-T
|
| [4] |
Ajzen I (2002) Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol 32: 665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x doi: 10.1111/j.1559-1816.2002.tb00236.x
|
| [5] |
Akçayır M, Dündar H, Akçayır G (2016) What makes you a digital native? Is it enough to be born after 1980? Comput Hum Behav 60: 435–440. https://doi.org/10.1016/j.chb.2016.02.089 doi: 10.1016/j.chb.2016.02.089
|
| [6] |
Alazab M, Alhyari S, Awajan A, et al. (2021) Blockchain technology in supply chain management: an empirical study of the factors affecting user adoption/acceptance. Cluster Comput 24: 83–101. https://doi.org/10.1007/s10586-020-03200-4 doi: 10.1007/s10586-020-03200-4
|
| [7] |
Albayati H, Kim SK, Rho JJ (2020) Accepting financial transactions using blockchain technology and cryptocurrency: A customer perspective approach. Technol Soc 62: 101320. https://doi.org/10.1016/j.techsoc.2020.101320 doi: 10.1016/j.techsoc.2020.101320
|
| [8] |
Al-Hattami HM (2024) What factors influence the intention to adopt blockchain technology in accounting education? Humanit Soc Sci Commun 11: 787. https://doi.org/10.1057/s41599-024-03315-8 doi: 10.1057/s41599-024-03315-8
|
| [9] |
Almuraqab NA (2020) Predicting determinants of the intention to use digital currency in the UAE: an empirical study. Electron J Inf Syst Dev Ctries 86: e12125. https://doi.org/10.1002/isd2.12125 doi: 10.1002/isd2.12125
|
| [10] |
Alwabel ASA, Zeng XJ (2021) Data-driven modeling of technology acceptance: A machine learning perspective. Expert Syst Appl 185: 115584. https://doi.org/10.1016/j.eswa.2021.115584 doi: 10.1016/j.eswa.2021.115584
|
| [11] |
Anbari M, Talebzadeh H, Talebzadeh M, et al. (2024) Understanding the drivers of adoption for blockchain-enabled intelligent transportation systems. Tech J 18: 598–608. https://doi.org/10.31803/tg-20240411223559 doi: 10.31803/tg-20240411223559
|
| [12] |
Arias-Oliva M, Gené-Albesa J, Andrés-Sánchez JD, et al. (2024) Fuzzy Set Qualitative Comparative Analysis of Loyalty Programmes Powered with Blockchain via an UTAUT2 Framework. Information 15: 757. https://doi.org/10.3390/info15120757 doi: 10.3390/info15120757
|
| [13] |
Arias-Oliva M, Pelegrín-Borondo J, Matías-Clavero G (2019) Variables influencing cryptocurrency use: a technology acceptance model in Spain. Front Psychol 10: 475. https://doi.org/10.3389/fpsyg.2019.00475 doi: 10.3389/fpsyg.2019.00475
|
| [14] |
Bandinelli R, Scozzafava G, Bindi B, et al. (2023) Blockchain and consumer behaviour: Results of a Technology Acceptance Model in the ancient wheat sector. Cleaner Logist Supply Chain 8: 100117. https://doi.org/10.1016/j.clscn.2023.100117 doi: 10.1016/j.clscn.2023.100117
|
| [15] |
Berkowsky R, Sharit J, Czaja S (2017) Factors predicting decisions about technology adoption among older adults. Innov Aging 1: igy002. https://doi.org/10.1093/geroni/igy002 doi: 10.1093/geroni/igy002
|
| [16] |
Bommer WH, Milevoj E, Rana S (2023) The intention to use cryptocurrency: A meta-analysis of what we know. Emerg Mark Rev 55: 100962. https://doi.org/10.1016/j.ememar.2022.100962 doi: 10.1016/j.ememar.2022.100962
|
| [17] |
Bouebdallah N, Ben Youssef WA, El Bouhali M (2025) Factors determining big four auditors' intentions to use blockchain technology: a mixed-methods approach. Asian Rev Account. https://doi.org/10.1108/ARA-01-2025-0005 doi: 10.1108/ARA-01-2025-0005
|
| [18] |
Bui HT (2022) Exploring and explaining older consumers' behaviour in the boom of social media. Int J Consum Stud 46: 601–620. https://doi.org/10.1111/ijcs.12715 doi: 10.1111/ijcs.12715
|
| [19] |
Chawla S, Prakash G, Singh G, et al. (2024) Assessing the factors influencing blockchain adoption intention in higher education institutes: A mixed method approach. Educ Inf Technol 29: 22651–22679. https://doi.org/10.1007/s10639-024-12716-w doi: 10.1007/s10639-024-12716-w
|
| [20] |
Chen CH (2023) Extending the Technology Acceptance Model: A new perspective on the adoption of blockchain technology. Hum Behav Emerg Technol 2023: 4835896. https://doi.org/10.1155/2023/4835896 doi: 10.1155/2023/4835896
|
| [21] |
Cheung GW, Cooper-Thomas HD, Lau RS, et al. (2024) Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pac J Manag 41: 745–783. https://doi.org/10.1007/s10490-023-09871-y doi: 10.1007/s10490-023-09871-y
|
| [22] | Choi HW, Kim N, Kim HR, et al. (2007) Effects of loyalty program failures on consumer negative emotions: Implications for establishing customer loyalty strategies. J Korean Mark Assoc 22: 103–120. |
| [23] |
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13: 319–340. https://doi.org/10.2307/249008 doi: 10.2307/249008
|
| [24] |
Di Giacomo D, Guerra F, Perilli E, et al. (2020) Technophobia as emerging risk factor in aging: investigation on computer anxiety dimension. Health Psychol Res 8: 1–4. https://doi.org/10.4081/hpr.2020.8207 doi: 10.4081/hpr.2020.8207
|
| [25] |
Dimitriadis KA, Koursaros D, Savva CS (2024) Evaluating the sophisticated digital assets and cryptocurrencies capacities of substituting international currencies in inflationary eras. Int Rev Financ Anal 96: 103693. https://doi.org/10.1016/j.irfa.2024.103693 doi: 10.1016/j.irfa.2024.103693
|
| [26] |
Dimitriadis KA, Koursaros D, Savva CS (2025a) The influential impacts of international dynamic spillovers in forming investor preferences: a quantile-VAR and GDCC-GARCH perspective. Appl Econ 57: 7175–7195. https://doi.org/10.1080/00036846.2024.2387868 doi: 10.1080/00036846.2024.2387868
|
| [27] |
Dimitriadis KA, Koursaros D, Savva CS (2025b) Exploring the dynamic nexus of traditional and digital assets in inflationary times: The role of safe havens, tech stocks, and cryptocurrencies. Econ Model 151: 107195. https://doi.org/10.1016/j.econmod.2025.107195 doi: 10.1016/j.econmod.2025.107195
|
| [28] |
DiStefano C, Zhu M, Mindrilă D (2009) Understanding and Using Factor Scores: Considerations for the Applied Researcher. Pract Assess Res Eval 14: 1–11. https://doi.org/10.7275/da8t-4g52 doi: 10.7275/da8t-4g52
|
| [29] |
Dong S, Abbas K, Li M, et al. (2023) Blockchain technology and application: an overview. Peer J Comput Sci 9: e1705. https://doi.org/10.7717/peerj-cs.1705 doi: 10.7717/peerj-cs.1705
|
| [30] |
Faul F, Erdfelder E, Buchner A, et al. (2009) Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav Res Methods 41: 1149–1160. https://doi.org/10.3758/BRM.41.4.1149 doi: 10.3758/BRM.41.4.1149
|
| [31] |
Ferguson R, Brohaugh B (2010) The Aging of Aquarius. J Consum Mark 27: 76–81. https://doi.org/10.1108/07363761011012976 doi: 10.1108/07363761011012976
|
| [32] | Fisher A, Rudin C, Dominici F (2019) All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously. J Mach Learn Res 20: 1–81. |
| [33] |
Gan Q, Lau RYK (2024) Trust in a 'trust-free' system: Blockchain acceptance in the banking and finance sector. Technol Forecast Soc Change 199: 123050. https://doi.org/10.1016/j.techfore.2023.123050 doi: 10.1016/j.techfore.2023.123050
|
| [34] |
Gao S, Li Y (2021) An empirical study on the adoption of blockchain-based games from users' perspectives. Electron Libr 39: 596–614. https://doi.org/10.1108/EL-01-2021-0009 doi: 10.1108/EL-01-2021-0009
|
| [35] |
Gbongli K, Xu Y, Amedjonekou KM (2019) Extended technology acceptance model to predict mobile-based money acceptance and sustainability. Sustainability 11: 3639. https://doi.org/10.3390/su11133639 doi: 10.3390/su11133639
|
| [36] |
Gil-Cordero E, Ledesma-Chaves P, Arteaga Sánchez R, et al. (2024) Crypto-wallets revolution! Key factors driving behavioral intention to adopt the Coinbase Wallet using mixed PLS-SEM/fsQCA methodology in the Spanish environment. Int J Bank Mark 42: 536–570. https://doi.org/10.1108/IJBM-01-2023-0035 doi: 10.1108/IJBM-01-2023-0035
|
| [37] | Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010: 249–256. |
| [38] | Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press. |
| [39] | Gschnaidtner C, Dehghan R, Hottenrott H, et al. (2024) Adoption and diffusion of blockchain technology. ZEW Discuss Pap 24–018. |
| [40] |
Hair JF, Risher JJ, Sarstedt M, et al. (2019) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31: 2–24. https://doi.org/10.1108/EBR-11-2018-0203 doi: 10.1108/EBR-11-2018-0203
|
| [41] | Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley. |
| [42] |
Hwang I, Kwon H (2016) Effects of demotion in loyalty programs on brand-switching intentions. Serv Bus 10: 489–505. https://doi.org/10.1007/s11628-015-0279-9 doi: 10.1007/s11628-015-0279-9
|
| [43] |
Ishmaev G (2021) Sovereignty, privacy, and ethics in blockchain-based identity management systems. Ethics Inf Technol 23: 239–252. https://doi.org/10.1007/s10676-020-09563-x doi: 10.1007/s10676-020-09563-x
|
| [44] |
Jang H, Han SH, Kim JH (2020) User perspectives on blockchain technology: user-centered evaluation and design strategies for apps. IEEE Access 8: 226213–226223. https://doi.org/10.1109/ACCESS.2020.3042822 doi: 10.1109/ACCESS.2020.3042822
|
| [45] |
Jegerson D, Mertzanis C, Khan M (2023) Investigating the unexpected determinants of cryptocurrency adoption in the UAE. Int J Emerg Mark 20: 2524-2544. https://doi.org/10.1108/IJOEM-06-2023-0924 doi: 10.1108/IJOEM-06-2023-0924
|
| [46] |
Kamble S, Angappa G, Arha H (2019) Understanding the blockchain technology adoption in supply chains—Indian context. Int J Prod Res 57: 2009–2033. https://doi.org/10.1080/00207543.2018.1518610 doi: 10.1080/00207543.2018.1518610
|
| [47] |
Khalil MM, Ahmed W (2024) Analyzing the drivers of blockchain adoption for supply chain in Pakistan. J Sci Technol Policy Manag 16: 1248–1270. https://doi.org/10.1108/JSTPM-10-2023-0178 doi: 10.1108/JSTPM-10-2023-0178
|
| [48] |
Khelil A, Manzli YS, Ben Hassine AL (2024) Blockchain use in high-tech supply chain: a new perspective of the unified theory of acceptance and use of technology. J Sci Technol Policy Manag. https://doi.org/10.1108/JSTPM-12-2023-0226 doi: 10.1108/JSTPM-12-2023-0226
|
| [49] |
Kim JJ, Steinhoff L, Palmatier RW (2021) An emerging theory of loyalty program dynamics. J Acad Mark Sci 49: 71–95. https://doi.org/10.1007/s11747-020-00719-1 doi: 10.1007/s11747-020-00719-1
|
| [50] |
Legesse A, Beshah B, Berhan E, et al. (2024) Exploring the influencing factors of blockchain technology adoption in national quality infrastructure. Cogent Eng 11. https://doi.org/10.1080/23311916.2024.2369220 doi: 10.1080/23311916.2024.2369220
|
| [51] |
Lerner JS, Ye L, Valdesolo P, et al. (2015) Emotion and decision making. Annu Rev Psychol 66: 799–823. https://doi.org/10.1146/annurev-psych-010213-115043 doi: 10.1146/annurev-psych-010213-115043
|
| [52] |
Leung SO (2011) A comparison of psychometric properties and normality in 4-, 5-, 6-, and 11-point Likert scales. J Soc Serv Res 37: 412–421. https://doi.org/10.1080/01488376.2011.580697 doi: 10.1080/01488376.2011.580697
|
| [53] |
Li W, Song R, Yu K (2025) Consumer adoption of food blockchain traceability. Front Sustain Food Syst 9. https://doi.org/10.3389/fsufs.2025.1515188 doi: 10.3389/fsufs.2025.1515188
|
| [54] |
Li X, Lai PL, Yang CC, et al. (2021) Determinants of blockchain adoption in the aviation industry. J Air Transp Manag 97: 102139. https://doi.org/10.1016/j.jairtraman.2021.102139 doi: 10.1016/j.jairtraman.2021.102139
|
| [55] | Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30: 4765–4774. |
| [56] |
Maduku DK, Rana NP, Mpinganjira M, et al. (2024) Do AI-powered digital assistants influence customer emotions, engagement and loyalty? Asia Pac J Mark Logist 36: 2849–2868. https://doi.org/10.1108/APJML-09-2023-0935 doi: 10.1108/APJML-09-2023-0935
|
| [57] |
Mannonov KM, Myeong S (2024) Citizens' perception of blockchain-based e-voting systems. Sustainability 16: 4387. https://doi.org/10.3390/su16114387 doi: 10.3390/su16114387
|
| [58] |
Mohammad SM, Turney PD (2013) Crowdsourcing a word–emotion association lexicon. Comput Intell 29: 436–465. https://doi.org/10.1111/j.1467-8640.2012.00460.x doi: 10.1111/j.1467-8640.2012.00460.x
|
| [59] |
Moisescu OI, Gică OA (2020) The impact of environmental and social responsibility on customer loyalty. Int J Environ Res Public Health 17: 6466. https://doi.org/10.3390/ijerph17186466 doi: 10.3390/ijerph17186466
|
| [60] |
Nguyen ODY, Lee J, Ngo LV, et al. (2022) Impacts of crisis emotions on negative word-of-mouth. J Prod Brand Manag 31: 536–550. https://doi.org/10.1108/JPBM-05-2020-2901 doi: 10.1108/JPBM-05-2020-2901
|
| [61] |
Norbu T, Park JY, Wong KW, et al. (2024) Factors affecting trust and acceptance for blockchain adoption. Future Internet 16: 106. https://doi.org/10.3390/fi16030106 doi: 10.3390/fi16030106
|
| [62] |
Nudin S, Suvajdžić M, Lukovac P, et al. (2024) Testing readiness of adoption of blockchain technology. Facta Univ Electron Energ 37: 53–73. https://doi.org/10.2298/FUEE2401053N doi: 10.2298/FUEE2401053N
|
| [63] |
Nuryyev G, Wang YP, Achyldurdyyeva J, et al. (2020) Blockchain technology adoption behavior and sustainability. Sustainability 12: 1256. https://doi.org/10.3390/su12031256 doi: 10.3390/su12031256
|
| [64] |
Ornelas Herrera SI, Baba Y, Erraach Y, et al. (2025) Analysing blockchain adoption in beekeeping. Front Sustain Food Syst 9. https://doi.org/10.3389/fsufs.2025.1566341 doi: 10.3389/fsufs.2025.1566341
|
| [65] | Palfrey J, Gasser U (2011) Born digital: Understanding the first generation of digital natives. ReadHowYouWant.com. |
| [66] |
Pelegrín-Borondo J, Reinares-Lara E, Olarte-Pascual C (2017) Assessing the acceptance of technological implants. Comput Hum Behav 70: 104–112. https://doi.org/10.1016/j.chb.2016.12.063 doi: 10.1016/j.chb.2016.12.063
|
| [67] |
Purwanto S, Hartini S, Premananto GC (2019) Understanding consumer intention to use Go-Pay. EKSIS 14: 1–16. https://doi.org/10.26533/eksis.v14i1.423 doi: 10.26533/eksis.v14i1.423
|
| [68] |
Rad D, Magulod GC Jr, Balas E, et al. (2022) A radial basis function neural network approach. Front Psychol 13: 880753. https://doi.org/10.3389/fpsyg.2022.880753 doi: 10.3389/fpsyg.2022.880753
|
| [69] |
Rejeb A, Keogh JG, Treiblmaier H (2020) How blockchain technology can benefit marketing. Front Blockchain 3: 3. https://doi.org/10.3389/fbloc.2020.00003 doi: 10.3389/fbloc.2020.00003
|
| [70] |
Salifu I, Arthur F, Arkorful V, et al. (2024) Economics students' behavioural intention and usage of ChatGPT. Cogent Soc Sci 10: 2300177. https://doi.org/10.1080/23311886.2023.2300177 doi: 10.1080/23311886.2023.2300177
|
| [71] |
Santos AF, Marinho J, Bernardino J (2023) Blockchain-based loyalty management system. Future Internet 15: 161. https://doi.org/10.3390/fi15050161 doi: 10.3390/fi15050161
|
| [72] |
Septianto F, An J, Chiew TM, et al. (2019) The similar versus divergent effects of pride and happiness. J Bus Res 99: 12–22. https://doi.org/10.1016/j.jbusres.2019.02.021 doi: 10.1016/j.jbusres.2019.02.021
|
| [73] |
Sharma A, Sharma A, Singh RK, et al. (2023) Blockchain adoption in agri-food supply chain management. Bus Process Manag J 29: 737–756. https://doi.org/10.1108/BPMJ-10-2022-0543 doi: 10.1108/BPMJ-10-2022-0543
|
| [74] |
Shrestha AK, Vassileva J (2019) User acceptance of usable blockchain-based research data sharing system. Proc IEEE TPS-ISA: 203–208. https://doi.org/10.1109/TPS-ISA48467.2019.00033 doi: 10.1109/TPS-ISA48467.2019.00033
|
| [75] |
Souto-Romero M, Arias-Oliva M, Andrés-Sánchez JD, et al. (2025) The relevance of cognitive and affective factors to explain the acceptance of blockchain use. Computers 14. https://doi.org/10.3390/computers14010008 doi: 10.3390/computers14010008
|
| [76] |
Steinhoff L, Palmatier RW (2016) Understanding loyalty program effectiveness. J Acad Mark Sci 44: 88–107. https://doi.org/10.1007/s11747-014-0405-6 doi: 10.1007/s11747-014-0405-6
|
| [77] |
Subero-Navarro Á, Pelegrín-Borondo J, Reinares-Lara E, et al. (2022) Proposal for modeling social robot acceptance. J Retail Consum Serv 64: 102813. https://doi.org/10.1016/j.jretconser.2021.102813 doi: 10.1016/j.jretconser.2021.102813
|
| [78] |
Taherdoost H (2022) A critical review of blockchain acceptance models. Computers 11. https://doi.org/10.3390/computers11020024 doi: 10.3390/computers11020024
|
| [79] |
Teng YC (2021) Towards trustworthy blockchains. Ethics Inf Technol 23: 385–397. https://doi.org/10.1007/s10676-021-09581-3 doi: 10.1007/s10676-021-09581-3
|
| [80] |
Teruel MA, Trujillo J (2020) Easing ĐApp interaction for non-blockchain users. Appl Sci 10: 4280. https://doi.org/10.3390/app10124280 doi: 10.3390/app10124280
|
| [81] |
Thomas NO, Singh S, Gangwar M (2023) Customer retention using loyalty cards program. Int J Bus Innov Res 30: 200–217. https://doi.org/10.1504/IJBIR.2023.128590 doi: 10.1504/IJBIR.2023.128590
|
| [82] |
Treiblmaier H, Petrozhitskaya E (2023) A new loyalty paradigm through blockchain technology. J Bus Res 159: 113725. https://doi.org/10.1016/j.jbusres.2023.113725 doi: 10.1016/j.jbusres.2023.113725
|
| [83] |
Tripathi G, Ahad MA, Casalino G (2023) A comprehensive review of blockchain technology. Decis Anal J 9: 100344. https://doi.org/10.1016/j.dajour.2023.100344 doi: 10.1016/j.dajour.2023.100344
|
| [84] |
Tu SF, Hsu CS, Wu YT (2022) A loyalty system incorporated with blockchain and call auction. J Theor Appl Electron Commer Res 17: 1107–1123. https://doi.org/10.3390/jtaer17030056 doi: 10.3390/jtaer17030056
|
| [85] |
Utz M, Johanning S, Roth T, et al. (2023) From ambivalence to trust. Int J Inf Manag 68: 102496. https://doi.org/10.1016/j.ijinfomgt.2022.102496 doi: 10.1016/j.ijinfomgt.2022.102496
|
| [86] |
Venkatesh V (2000) Determinants of perceived ease of use. Inf Syst Res 11: 342–365. https://doi.org/10.1287/isre.11.4.342.11872 doi: 10.1287/isre.11.4.342.11872
|
| [87] |
Venkatesh V, Bala H (2008) Technology acceptance model 3. Decis Sci 39: 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x doi: 10.1111/j.1540-5915.2008.00192.x
|
| [88] |
Venkatesh V, Morris MG, Davis GB, et al. (2003) User acceptance of information technology. MIS Q 27: 425–478. https://doi.org/10.2307/30036540 doi: 10.2307/30036540
|
| [89] |
Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance and use of information technology. MIS Q 36: 157–178. https://doi.org/10.2307/41410412 doi: 10.2307/41410412
|
| [90] |
Venkatesh V, Thong JY, Xu X (2016) Unified theory of acceptance and use of technology. J Assoc Inf Syst 17: 328–376. https://doi.org/10.17705/1jais.00428 doi: 10.17705/1jais.00428
|
| [91] |
Wasiq M, Bashar A, Akmal S, et al. (2023) Adoption and applications of blockchain technology in marketing. Sustainability 15: 3279. https://doi.org/10.3390/su15043279 doi: 10.3390/su15043279
|
| [92] |
Zhang GP (2000) Neural networks for classification: A survey. IEEE Trans Syst Man Cybern C 30: 451–462. https://doi.org/10.1109/5326.897072 doi: 10.1109/5326.897072
|
| [93] | Zwitter A, Hazenberg J (2021) Cyberspace, blockchain, governance. In: Cappiello, B., Carullo, G. (eds) Blockchain, Law and Governance. Springer, Cham, 87–97. https://doi.org/10.1007/978-3-030-52722-8_6 |
Figures(2) / Tables(10)
Jorge de Andrés-Sánchez, Mario Arias-Oliva, Mar Souto-Romero, Miguel Llorens-Marín. Evaluating explanatory factors of the acceptance of blockchain-based loyalty programs with neural networks[J]. Data Science in Finance and Economics, 2026, 6(1): 58-84. doi: 10.3934/DSFE.2026003
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