Factors influencing pneumatic precision corn planter adoption in the Philippines: An empirical study using the Technology Acceptance Model (TAM) and Partial Least Squares Structural Equation Modeling (PLS-SEM)
Authors
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Duan P. Mary Rose
Cebu Technological University-Danao Campus, Danao City, Philippines
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Gonzales G. Gamaliel
Cebu Technological University-Danao Campus, Danao City, Philippines
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Papaya Iway Harold Jay
Cebu Technological University-Danao Campus, Danao City, Philippines
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Buot Pisao Amadito Jr.
Cebu Technological University-Danao Campus, Danao City, Philippines
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Montebon Defensor Vincet Rhey
Cebu Technological University-Danao Campus, Danao City, Philippines
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Moya S. Emmanuel
Cebu Technological University-Danao Campus, Danao City, Philippines
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Mata Dumanacal Marlon
marlon.mata@ctu.edu.ph
Cebu Technological University-Danao Campus, Danao City, Philippineshttps://orcid.org/0009-0006-2823-3393
DOI:
10.46223/HCMCOUJS.econ.en.15.5.3964.2025Keywords:
compatibility; observability; personal innovativeness; pneumatic precision corn planter; PLS-SEM; Technology Acceptance Model (TAM)JEL Classification:
O3; O5; Q1Abstract
This study applies the Technology Acceptance Model (TAM) to evaluate the factors influencing the adoption of pneumatic precision planters in corn farming in the Philippines. Da-ta from 393 farmers were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between Perceived Usefulness (PU), Perceived Ease of Use (PEU), Behavioral Intention (BI), and three extended TAM constructs: Compatibility (CO), Observability (OB), and Personal Innovativeness (PI). The model was validated for reliability and discriminant validity, with the Average Variance Extracted (AVE) ranging from 0.664 to 0.823. Statistical significance was observed in ten out of twelve hypothesized relationships, indicating a high likelihood of adoption. This study extends TAM by incorporating external factors such as CO, OB, and PI, offering a deeper understanding of how these variables influence farmers’ perceptions of the technology’s usefulness and ease of use. The findings suggest that, for successful adoption, policymakers should focus on enhancing the visibility of the technology’s benefits, ensuring compatibility with existing farming practices, and promoting openness to innovation through targeted education and support. The results highlight the need for practical interventions, such as educational programs and demonstration projects, which could significantly improve technology adoption, productivity, and sustainability in Philippine agriculture.Downloads
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Abdollahzadeh, G., Sharifzadeh, M. S., & Damalas, C. A. (2016). Motivations for adopting biological control among Iranian rice farmers. Crop Protection, 80(1), 42-50. https://doi.org/10.1016/j.cropro.2015.10.021
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.
Agarwal, S., Gupta, R., & Singh, P. (2020). Mobile-based agricultural advisory services in rural India: Exploring the role of TAM. Journal of Extension Education, 45(2), 215-230. https://doi.org/10.1080/20505639.2020.1840195
Agriculture and Fisheries Mechanization Law. (2013). Republic Act No. 10601. https://doe.gov.ph/laws-and-issuances/republic-act-no-10601
Agriculture and Fisheries Modernization Act. (1997). Republic Act No. 8435. https://lawphil.net/statutes/repacts/ra1997/ra_8435_1997.html
Ahmad, F., Adeel, M., Qiu, B., Ma, J., Shoaib, M., Shakoor, A., & Chandio, F. A. (2021). Sowing uniformity of bed-type pneumatic maize planter at various seedbed preparation levels and machine travel speeds. International Journal of Agricultural and Biological Engineering, 14(1), 165-171. https://doi.org/10.25165/j.ijabe.20211401.5054
Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50. https://doi.org/10.1016/j.aci.2014.09.001
Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers’ adoption decision of precision agriculture technology. Decision Support Systems, 54(1), 510-520. https://doi.org/10.1016/j.dss.2012.07.002
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (PLS) approach to casual modeling: Personal computer adoption ans use as an illustration. Technology Studies, 2(2), 285-309.
Barut, Z. B. (n.d.). Effect of different operating parameters on seed holding in the single seed metering unit of a pneumatic planter. TÜBİTAK Academic Journals, 28(6), 435-411.
Bautista, E. G., Kim, J. S., Kim, Y. J., & Panganiban, M. E. (2017). Farmer’s perception on farm mechanization and land reformation in the Philippines. Journal of the Korean Society of International Agriculture, 29(3), 242-250.
Bautista. (2003). Farm power available for utilization in Philippine agriculture. https://www.researchgate.net/publication/315783506_Farm_Power_Available_for_Utilization_in_Philippine_Agriculture
Cabanilla, V. L., & Dar, W. D. (2015). Effects of the extensive use of mechanization on farm labor use and patterns in rice and corn production systems in the Philippines. Philippine Journal of Agriculture and Forestry, 50(3), 1-17.
Caffaro, F., Cremasco, M. M., Roccato, M., & Cavallo, E. (2020). Drivers of farmers’ intention to adopt technological innovations in Italy: The role of information sources, perceived usefulness, and perceived ease of use. Journal of Rural Studies, 76, 264-271. https://doi.org/10.1016/j.jrurstud.2020.04.028
Calantone, R. J., Griffith, D. A., & Yalcinkaya, G. (2006). An empirical examination of a technology adoption model for the context of China. Journal of International Marketing, 14(4), 1-27. https://doi.org/10.1509/jimk.14.4.1
Chau, P. Y., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699-719.
Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Erlbaum.
Cruz, R. D., & Malanon, H. G. (2018). State of on-farm maize mechanization in the Philippines. https://cigrjournal.org/index.php/Ejounral/article/view/4224
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral dissertation, Massachusetts Institute of Technology, Sloan School of Management]. MIT DSpace. http://hdl.handle.net /1721.1 /15192
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. https://doi.org/10.2307/249008
Dela Cruz, A. S., & Garcia, A. C. (2014). State of on-farm maize mechanization in the Philippines. Crop Environment, Bio & Food Engineering, 5(1), 1-1.
Din-Sue, F. (2005). Technology development process and experiences on small farm mechanization (Extension Bulletin No. 569). Food and Fertilizer Technology Center for the Asian and Pacific Region. https://www.fftc.org.tw/article/773
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440-452.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Go, H., Kang, M., & Suh, S. (2020). Machine learning of robots in tourism and hospitality: Interactive Technology Acceptance Model (iTAM) - Cutting edge. Tourism Review, 75(4), 625-636. https://doi.org/10.1108/tr-02-2019-0062
Guarella, P., Pellerano, A., & Pascuzzi, S. (1996). Experimental and theoretical performance of a vacuum seeder nozzle for vegetable seeds. Journal of Agricultural Engineering Research, 64(1), 29-36. https://doi.org/10.1006/jaer.1996.0043
Guarín, A., Rivera, M. D. G., Pinto-Correia, T., Guiomar, N., Šūmane, S., & Moreno-Pérez, O. M. (2020). A new typology of small farms in Europe. Global Food Security, 26, Article 100389. https://doi.org/10.1016/j.gfs.2020.100389
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal Of Marketing Theory And Practice, 19(2), 139-152.
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566-584.
Hair, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), New challenges to international marketing (pp. 277-319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014
Hwang, Y. (2014). User experience and personal innovativeness: An empirical study on the enterprise resource planning systems. Computers in Human Behavior, 34, 227-234.
Jiménez, I. A. C., García, L. C. C., Marcolin, F., Violante, M. G., & Vezzetti, E. (2021). Validation of a TAM extension in agriculture: Exploring the determinants of acceptance of an e-Learning platform. Applied Sciences, 11(10), Article 4672. https://doi.org/10.3390/ app11 104672
Kante, M., Oboko, R., & Chepken, C. (2019). An ICT model for increased adoption of farm input information in developing countries: A case in Sikasso, Mali. Information Processing in Agriculture, 6(1), 26-46. https://doi.org/10.1016/j.inpa.2018.09.002
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.
Kumar, A., Verma, S., & Singh, R. (2021). Adoption of drone technology in precision agriculture: A TAM approach. Journal of Agricultural Technology, 17(3), 453-467. https://doi.org/10.1080/09702082.2021.1823404
Kurkinen, E. (2012). On the exploration of mobile technology acceptance among law enforcement officers using Structural Equation Modelling (SEM): A multi-group analysis of the Finnish Police Force [Doctoral dissertation, University of Jyväskylä]. https://jyx.jyu.fi/handle/123456789/40655
Lan, Y., Kocher, M. F., & Smith, J. A. (1999). Opto-electronic sensor system for laboratory measurement of planter seed spacing with small seeds. Journal of Agricultural Engineering Research, 72(2), 119-127. https://doi.org/10.1006/jaer.1998.0353
Lee, J., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(1), 752-780. https://doi.org/10.17705/1CAIS.01250
Lee, M. S. (2019). Effects of personal innovativeness on mobile device adoption by older adults in South Korea: The moderation effect of mobile device use experience. International Journal of Mobile Communications, 17(6), Article 682. doi: 10.1504/ijmc.2019.102719.
Li, Y., Bingxin, Y., Yiming, Y., Xiantao, H., Quanwei, L., Zhijie, L., Xiaowei, Y., Tao, C., & Dongxing, Z. (2016). Global overview of research progress and development of precision maize planters. International Journal of Agricultural and Biological Engineering, 9(1), 9-26. https://doi.org/10.3965/j.ijabe.20160901.2285
Lowder, S. K., Skoet, J., & Raney, T. (2016). The number, size, and distribution of farms, smallholder farms, and family farms worldwide. World Development, 87, 16-29. https://doi.org/10.1016/j.worlddev.2015.10.041
Majid, D., & Moslem, N. (2016). Development and evaluation of a pneumatic dibble punch planter for precision planting. https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201 602261184214082
Mandal, S., Kumar, G. P., Tanna, H., & Kumar, A. (2018). Design and evaluation of a pneumatic metering mechanism for power tiller operated precision planter. Current Science, 115(6), 1106-1114.https://www.jstor.org/stable/26978363?typeAccessWork flow=login
MARA (Ministry of Agriculture and Rural Affairs). (2020). National data. Ministry of Agriculture and Rural Affairs.
Mata, M., Ancheta, R., Batucan, G., & Gonzales, G. G. (2024). Exploring technology acceptance model with system characteristics to investigate sustainable building information modeling adoption in the architecture, engineering, and construction industry: The case of the Philippines. Social Sciences & Humanities Open, 10, Article 100967.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222. https://doi.org/10.1287/isre.2.3.192
Müller, B., Schmidt, L., & Hoffmann, M. (2021). Factors influencing the adoption of precision irrigation systems: A PLS-SEM approach. Journal of Agricultural Systems, 188, Article 102951. https://doi.org/10.1016/j.agsy.2021.102951
Namjoo, M., Khoshnam, F., Golbakhshı, H., & Dowlatı, M. (2016). Physical and mechanical changes in ripening melon fruits. Yuzuncu Yıl University Journal of Agricultural Sciences, 26(2), 135-144.
National Economic and Development Authority (NEDA). (2017). Philippine development plan 2017-2022. https://pdp.neda.gov.ph/philippine-development-plan-2017-2022/
Naval, R. C., & Dolojan, F. M. (2020). Determinants of Bt corn (zea mays L.) adoption in Cagayan valley, Philippines. Journal of Critical Reviews 7(11), 9-13. https://doi.org/10.31838/jcr.07.11.03
Nawi, N. S. M., Deros, B. M., Nordin, N., Rahman, M. N. A., & Sukadarin, E. H. (2017). Critical factors for oil palm plantation workers acceptance and use of mechanization technovation tools. Journal of Business, Economics and Finance, 4(1), 218-223. https://doi.org/10.17261/pressacademia.2017.537
Neuberg, L. G. (2003). Causality: Models, reasoning, and inference. Cambridge University Press. https://doi.org/10.1017/s0266466603004109
OECD. (2021). OECD-FAO agricultural outlook 2021 - 2030. OECD Publishing. https://doi.org/10.1787/19428846-en
Paras, F. O., & Amongo, M. C. (2005). Technology transfer strategies for small farm mechanization technologies in the Philippines. https://energimasadepan.wordpress.com /wp-content/uploads/2010/04/paras-jr-ptp-eb570.pdf
Peng, R., Xiong, L., & Yang, Z. (2012). Exploring tourist adoption of tourism mobile payment: An empirical analysis. Journal of Theoretical and Applied Electronic Commerce Research, 7(1), 21-33. https://doi.org/10.4067/S0718-18762012000100004
Qu, W., Jing, X., Ge, Y., Sun, X., & Zhang, K. (2019). Development and validation of a questionnaire to assess public receptivity toward autonomous vehicles and its relation with the traffic safety climate in China. Accident Analysis & Prevention, 128, 78-86. https://doi.org/10.1016/j.aap.2019.04.006
Radif, M., Fan, D. I. S., & McLaughlin, D. P. (2016). Employment Technology Acceptance Model (TAM) to adopt Learning Management System (LMS) in Iraqi Universities (pp. 7120-7130). INTED2016 Proceedings.
Ramirez-Cabral, N. Y. Z., Kumar, L., & Shabani, F. (2017). Global alterations in areas of suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX). Scientific Reports 7(1), 1-13. https://doi.org/10.1038/s41598-017-05804-0
Rezaei, R., Safa, L., & Ganjkhanloo, M. M. (2020). Understanding farmers’ ecological conservation behavior regarding the use of integrated pest management-an application of the technology acceptance model. Global Ecology and Conservation, 22, Article e00941. https://doi.org/10.1016/j.gecco.2020.e00941
Rezaei-Moghaddam, K., & Salehi, S. (2010). Agricultural specialists intention toward precision agriculture technologies: Integrating innovation characteristics to technology acceptance model. African Journal of Agricultural Research, 5(11), 1191-1199. https://doi.org/10.5897/ajar09.506
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. https://www.smartpls.com
Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). Free Press.
Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Publisher The Free Press.
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105-115.
Schepers, J. J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103. https://doi.org/10.1016/j.im.2006.10.007
Shafii, S., & Holmes, R. G. (1990). Air-jet seed metering, a theoretical and experimental study. Transactions of the ASAE, 33(5), Article 1432. https://doi.org/10.13031/2013.31489
Sharifzadeh, M. S., Damalas, C. A., Abdollahzadeh, G., & Ahmadi-Gorgi, H. (2017). Predicting adoption of biological control among Iranian rice farmers: An application of the extended Technology Acceptance Model (TAM2). Crop Protection, 96, 88-96. https://doi.org/10.1016/j.cropro.2017.01.014
Silva, A. G., Canavari, M., & Sidali, K. L. (2017). A technology acceptance model of common bean growers’ intention to adopt integrated production in the Brazilian central region. ˜Die œBodenkultur, 68(3), 131-143. https://doi.org/10.1515/boku-2017-0012
Sovacool, B. K. (2017). Experts, theories, and electric mobility transitions: Toward an integrated conceptual framework for the adoption of electric vehicles. Energy Research & Social Science, 27, 78-95. https://doi.org/10.1016/j.erss.2017.02.014
Statista. (2023). Corn production in the Philippines 2022. https://www.statista.com/statistics/751372/philippines-corn-production/
Steffen, R. B., Wolff, R. L., Iltis, R., Albers, M. A., & Becker, D. S. (1999). Effect of two seed treatment coatings on corn planter seeding rate and monitor accuracy. Applied Engineering in Agriculture, 15(6), 605-608. https://doi.org/10.13031/2013.5824
Taylor, S., & Todd, P. A. (1995). Decomposition and cross effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, 20, 679-686.
USDA. (2021). International baseline data. https://www.ers.usda.gov/data-products/international baseline-data/
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly, 36(1), Article 157. https://doi.org/10.2307/41410412
Wang, X., Yuen, K. F., Wong, Y. D., & Teo, C. C. (2018). An innovation diffusion perspective of e-consumers’ initial adoption of self-collection service via automated parcel station. The International Journal of Logistics Management 29(1), 237-260. https://doi.org/10.1108/IJLM-12-2016-0302
Wu, J. -H., Wang, S. -C., & Lin, L. -M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International Journal of Medical Informatics, 76(1), 66-77. https://doi.org/10.1016/j.ijmedinf.2006.06.00610.1016 /j.ijmedinf. 2006.06.006
Xing, H., Wang, Z. M., Luo, X. W., Cao, X. M., Liu, C. B., & Zang, Y. (2017). General structure design and field experiment of pneumatic rice direct seeder. International Journal of Agricultural and Biological Engineering, 10(6), 31-42.
Yang, L., Yan, B. X., Yu, Y. M., He, X. T., Liu, Q. W., Liang, Z. J., Yin, X., Cui, T., & Zhang, D. (2016). Global overview of research progress and development of precision maize planters. International Journal of Agricultural and Biological Engineering, 9(1), 9-26.
Yoon, C., Lim, D., & Park, C. (2020). Factors affecting adoption of smart farms: The case of Korea. Computers in Human Behavior, 108, Article 106309. https://doi.org/10.1016/j.chb.2020.106309
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: A meta-analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251-280. https://doi.org/10.1108/17465660710834453
Yuen, K. F., Cai, L., Qi, G., & Wang, X. (2020). Factors influencing autonomous vehicle adoption: An application of the technology acceptance model and innovation diffusion theory. Technology Analysis & Strategic Management, 33(5), 505-519. https://doi.org/10.1080/09537325.2020.1826423
Yuen, K. F., Wang, X., Ng, L. T. W., & Wong, Y. D. (2018). An investigation of customers’ intention to use self-collection services for last-mile delivery. Transport Policy, 66, 1-8. https://doi.org/10.1016/j.tranpol.2018.03.001
Zhang, X., Wang, Y., & Li, Z. (2021). User acceptance of machine learning models - Integrating several important external variables with technology acceptance model. International Journal of Electrical Engineering Education, 60(1_suppl), 3986-4005. https://doi.org/10.1177/00207209211005271
Zhang, Y., Liu, X., & Wang, F. (2022). Smart farming technology adoption in Europe: Examining the role of compatibility and social influence. Agricultural Innovation Journal, 34(1), 51-67. https://doi.org/10.1007/s11356-021-15613-1
Zheng, S., Wang, Z., & Wachenheim, C. J. (2018). Technology adoption among farmers in Jilin Province, China. China Agricultural Economic Review, 11(1), 206-216. https://doi.org/10.1108/caer-11-2017-0216
Received: 06-01-2025Accepted: 02-05-2025Published: 04-06-2025Statistics Views
Abstract: 711 PDF: 376 Appendix: 92How to Cite
Rose, D. P. M., Gamaliel, G. G., Jay, P. I. H., Amadito Jr., B. P., Rhey, M. D. V., Emmanuel, M. S., & Marlon, M. D. (2025). Factors influencing pneumatic precision corn planter adoption in the Philippines: An empirical study using the Technology Acceptance Model (TAM) and Partial Least Squares Structural Equation Modeling (PLS-SEM). HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, 15(5), 23–49. https://doi.org/10.46223/HCMCOUJS.econ.en.15.5.3964.2025License
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