Adopción de Big Data Analitycs en las PyMEs Big Data Analytics Adoption in SMEs

Contenido principal del artículo

Luis Manuel Hernández-Pérez
Jessica Müller-Pérez

Resumen

El objetivo de la presente investigación fue identificar los factores que inciden en la adopción de Big Data Analitycs en las pequeñas y medianas empresas de Puebla, México y, su efecto en el rendimiento empresarial y en el análisis del comportamiento del consumidor basado en el modelo TOE. Para ello se aplicó un método de modelización de PLS-SEM. Los hallazgos revelaron que la ventaja relativa, compatibilidad y el soporte externo afectaron positivamente la adopción de BDA y, estos a su vez en el rendimiento empresarial y conocimiento del comportamiento del consumidor. Dicha investigación es original, ya que inicia un marco conceptual de la adopción de BDA para las pymes mexicanas y, las ventajas de adoptar dicha tecnología. Además, una de las limitaciones es que solo se realizó en el estado de Puebla y en PYMES, por lo que se puede replicar en otros estados y en empresas más grandes.

Detalles del artículo

Sección
Publicados

Citas

Aboelmaged, M., & Mouakket, S. (2020). Influencing models and determinants in big data analytics research: A bibliometric analysis. Information Processing & Management, 57(4), 102234. https://doi.org/10.1016/j.ipm.2020.102234

Abou-Shouk, M., & Soliman, M. (2021). The impact of gamification adoption intention on brand awareness and loyalty in tourism: The mediating effect of customer engagement. Journal of Destination Marketing & Management, 20, 100559. https://doi.org/10.1016/j.jdmm.2021.100559

Adam, I. O., Alhassan, M. D., & Afriyie, Y. (2020). What drives global B2C E-commerce? An analysis of the effect of ICT access, human resource development and regulatory environment. Technology Analysis & Strategic Management, 32(7), 835–850. https://doi.org/10.1080/09537325.2020.1714579

Akram, U., Fülöp, M. T., Tiron-Tudor, A., Topor, D. I., & Căpușneanu, S. (2021). Impact of digitalization on customers’ well-being in the pandemic period: Challenges and opportunities for the retail industry. International Journal of Environmental Research and Public Health, 18(14). https://doi.org/10.3390/ijerph18147533

Alam, S. S., Wang, C., Lin, C., Masukujjaman, M., & Ho, Y. (2022). Consumers’ buying intention towards healthy foods during the COVID-19 pandemic in an emerging economy. Cogent Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2135212

AlBar, A. M., & Hoque, M. R. (2019). Factors affecting the adoption of information and communication technology in small and medium enterprises: a perspective from rural Saudi Arabia. Information Technology for Development, 25(4), 715–738. https://doi.org/10.1080/02681102.2017.1390437

Ali Qalati, S., Li, W., Ahmed, N., Ali Mirani, M., & Khan, A. (2020). Examining the Factors Affecting SME Performance: The Mediating Role of Social Media Adoption. Sustainability, 13(1), 75. https://doi.org/10.3390/su13010075

Alsheibani, S., Cheung, Y., & Messom, C. (2018). AI-readiness at Firm-Level. PACIS, 4, 231–245. https://aisel.aisnet.org/pacis2018/37

Baccarella, C. V., Maier, L., Meinel, M., Wagner, T. F., & Voigt, K.-I. (2022). The effect of organizational support for creativity on innovation and market performance: the moderating role of market dynamism. Journal of Manufacturing Technology Management, 33(4), 827–849. https://doi.org/10.1108/JMTM-10-2020-0423

Behl, A. (2022). Antecedents to firm performance and competitiveness using the lens of big data analytics: a cross-cultural study. Management Decision, 60(2), 368–398. https://doi.org/10.1108/MD-01-2020-0121

Benoit, D. F., Lessmann, S., & Verbeke, W. (2020). On realising the utopian potential of big data analytics for maximising return on marketing investments. Journal of Marketing Management, 36(3–4), 233–247. https://doi.org/10.1080/0267257X.2020.1739446

Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170–180. https://doi.org/10.1016/j.ijforecast.2018.09.003

Caballero-Morales, S. O. (2021). Innovation as recovery strategy for SMEs in emerging economies during the COVID-19 pandemic. Research in International Business and Finance, 57(May 2020), 101396. https://doi.org/10.1016/j.ribaf.2021.101396

Chang, V. (2021). An ethical framework for big data and smart cities. Technological Forecasting and Social Change, 165, 120559. https://doi.org/10.1016/j.techfore.2020.120559

Chang, Y.-W. (2020). What drives organizations to switch to cloud ERP systems? The impacts of enablers and inhibitors. Journal of Enterprise Information Management, 33(3), 600–626. https://doi.org/10.1108/JEIM-06-2019-0148

Chen, C.-C., Chen, C.-W., & Tung, Y.-C. (2018). Exploring the Consumer Behavior of Intention to Purchase Green Products in Belt and Road Countries: An Empirical Analysis. Sustainability, 10(3), 854. https://doi.org/10.3390/su10030854

Chen, Y., Yin, Y., Browne, G. J., & Li, D. (2019). Adoption of building information modeling in Chinese construction industry. Engineering, Construction and Architectural Management, 26(9), 1878–1898. https://doi.org/10.1108/ECAM-11-2017-0246

Clohessy, T., Acton, T., & Rogers, N. (2019). Blockchain Adoption: Technological, Organisational and Environmental Considerations. In Business Transformation through Blockchain (pp. 47–76). Springer International Publishing. https://doi.org/10.1007/978-3-319-98911-2_2

Crick, J. M. (2019). Moderators affecting the relationship between coopetition and company performance. Journal of Business & Industrial Marketing, 34(2), 518–531. https://doi.org/10.1108/JBIM-03-2018-0102

Cruz-Jesus, F., Pinheiro, A., & Oliveira, T. (2019). Understanding CRM adoption stages: empirical analysis building on the TOE framework. Computers in Industry, 109, 1–13. https://doi.org/10.1016/j.compind.2019.03.007

Evans, J. R., & Mathur, A. (2018). The value of online surveys: a look back and a look ahead. Internet Research, 28(4), 854–887. https://doi.org/10.1108/IntR-03-2018-0089

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

Fosso Wamba, S., Gunasekaran, A., Papadopoulos, T., & Ngai, E. (2018). Big data analytics in logistics and supply chain management. The International Journal of Logistics Management,

Frizzo-Barker, J., Chow-White, P. A., Mozafari, M., & Ha, D. (2016). An empirical study of the rise of big data in business scholarship. International Journal of Information Management, 36(3), 403–413. https://doi.org/10.1016/j.ijinfomgt.2016.01.006

Ghaleb, E. A. A., Dominic, P. D. D., Fati, S. M., Muneer, A., & Ali, R. F. (2021). The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees. Sustainability, 13(15), 8379. https://doi.org/10.3390/su13158379

Govindan, K., Cheng, T. C. E., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. Transportation Research Part E: Logistics and Transportation Review, 114, 343–349. https://doi.org/10.1016/j.tre.2018.03.011

Gu, V. C., Zhou, B., Cao, Q., & Adams, J. (2021). Exploring the relationship between supplier development, big data analytics capability, and firm performance. Annals of Operations Research, 302(1), 151–172. https://doi.org/10.1007/s10479-021-03976-7

Gupta, D., Bhatt, S., Gupta, M., & Tosun, A. S. (2021). Future Smart Connected Communities to Fight COVID-19 Outbreak. Internet of Things, 13, 100342. https://doi.org/10.1016/j.iot.2020.100342

Gupta, S., Qian, X., Bhushan, B., & Luo, Z. (2019). Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective. Management Decision, 57(8), 1857–1882. https://doi.org/10.1108/MD-06-2018-0633

Hair Jr., J. F., M. Hult, G. T., M. Ringle, C., Sarstedt, M., Castillo Apraiz, J., Cepeda Carrión, G. A., & Roldán, J. L. (2019). Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM) (Segunda Edición). In Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM) (Segunda Edición). OmniaScience. https://doi.org/10.3926/oss.37

Hashimy, L., Jain, G., & Grifell-Tatjé, E. (2023). Determinants of blockchain adoption as decentralized business model by Spanish firms – an innovation theory perspective. Industrial Management & Data Systems, 123(1), 204–228. https://doi.org/10.1108/IMDS-01-2022-0030

Hassan, M. S., Islam, M. A., Sobhani, F. A., Nasir, H., Mahmud, I., & Zahra, F. T. (2022). Drivers Influencing the Adoption Intention towards Mobile Fintech Services: A Study on the Emerging Bangladesh Market. Information, 13(7), 349. https://doi.org/10.3390/info13070349

Ho, J. C., Wu, C.-G., Lee, C.-S., & Pham, T.-T. T. (2020). Factors affecting the behavioral intention to adopt mobile banking: An international comparison. Technology in Society, 63, 101360. https://doi.org/10.1016/j.techsoc.2020.101360

Hue, T. T. (2019). The determinants of innovation in Vietnamese manufacturing firms: an empirical analysis using a technology–organization–environment framework. Eurasian Business Review, 9(3), 247–267. https://doi.org/10.1007/s40821-019-00125-w

INEGI. (2023). Directorio de empresas y establecimientos. Economía y Sectores Productivos. https://www.inegi.org.mx/temas/directorio/

Jebarajakirthy, C., & Shankar, A. (2021). Impact of online convenience on mobile banking adoption intention: A moderated mediation approach. Journal of Retailing and Consumer Services, 58, 102323. https://doi.org/10.1016/j.jretconser.2020.102323

Johnson, D. S., Muzellec, L., Sihi, D., & Zahay, D. (2019). The marketing organization’s journey to become data-driven. Journal of Research in Interactive Marketing, 13(2), 162–178. https://doi.org/10.1108/JRIM-12-2018-0157

Kim, S., Lee, J., & Gweon, G. (2019). Comparing Data from Chatbot and Web Surveys. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3290605.3300316

Kim, Y., & Kim, B. (2021). The Effective Factors on Continuity of Corporate Information Security Management: Based on TOE Framework. Information, 12(11), 446. https://doi.org/10.3390/info12110446

Lehrer, C., Wieneke, A., vom Brocke, J., Jung, R., & Seidel, S. (2018). How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service. Journal of Management Information Systems, 35(2), 424–460. https://doi.org/10.1080/07421222.2018.1451953

LG Tornatzky, M Fleischer, A. C. (1990). The processes of technological innovation.

Lichy, J., & Kachour, M. (2019). Big Data Perception & Usage. Proceedings of the 2019 3rd International Conference on E-Commerce, E-Business and E-Government - ICEEG 2019, 89–94. https://doi.org/10.1145/3340017.3340032

Machado, C. G., Winroth, M., Almström, P., Ericson Öberg, A., Kurdve, M., & AlMashalah, S. (2021). Digital organisational readiness: experiences from manufacturing companies. Journal of Manufacturing Technology Management, 32(9), 167–182. https://doi.org/10.1108/JMTM-05-2019-0188

Mahakittikun, T., Suntrayuth, S., & Bhatiasevi, V. (2021). The impact of technological-organizational-environmental (TOE) factors on firm performance: merchant’s perspective of mobile payment from Thailand’s retail and service firms. Journal of Asia Business Studies, 15(2), 359–383. https://doi.org/10.1108/JABS-01-2020-0012

Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875–1900. https://doi.org/10.1080/00207543.2020.1793011

Malik, S., Chadhar, M., Vatanasakdakul, S., & Chetty, M. (2021). Factors Affecting the Organizational Adoption of Blockchain Technology: Extending the Technology–Organization–Environment (TOE) Framework in the Australian Context. Sustainability, 13(16), 9404. https://doi.org/10.3390/su13169404

Maroufkhani, P., Wan Ismail, W. K., & Ghobakhloo, M. (2020). Big data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management, 11(4), 483–513. https://doi.org/10.1108/JSTPM-02-2020-0018

Masood, T., & Egger, J. (2019). Augmented reality in support of Industry 4.0—Implementation challenges and success factors. Robotics and Computer-Integrated Manufacturing, 58, 181–195. https://doi.org/10.1016/j.rcim.2019.02.003

Mat Roni, S., & Djajadikerta, H. G. (2021). SPSS Basics. In Data Analysis with SPSS for Survey-based Research (pp. 9–14). Springer Singapore. https://doi.org/10.1007/978-981-16-0193-4_2

Mathrani, S., & Lai, X. (2021). Big Data Analytic Framework for Organizational Leverage. Applied Sciences, 11(5), 2340. https://doi.org/10.3390/app11052340

Mikalef, P., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2020). Big data and business analytics: A research agenda for realizing business value. Information & Management, 57(1), 103237. https://doi.org/10.1016/j.im.2019.103237

Mohtaramzadeh, M., Ramayah, T., & Jun-Hwa, C. (2018). B2B E-Commerce Adoption in Iranian Manufacturing Companies: Analyzing the Moderating Role of Organizational Culture. International Journal of Human–Computer Interaction, 34(7), 621–639. https://doi.org/10.1080/10447318.2017.1385212

Morimura, F., & Sakagawa, Y. (2023). The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry. Journal of Retailing and Consumer Services, 71, 103193. https://doi.org/10.1016/j.jretconser.2022.103193

Müller, O., Fay, M., & vom Brocke, J. (2018). The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics. Journal of Management Information Systems, 35(2), 488–509. https://doi.org/10.1080/07421222.2018.1451955

Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework. Buildings, 12(2), 90. https://doi.org/10.3390/buildings12020090

Ngo, H. T., & Nguyen, L. T. H. (2024). Consumer adoption intention toward FinTech services in a bank-based financial system in Vietnam. Journal of Financial Regulation and Compliance, 32(2), 153–167. https://doi.org/10.1108/JFRC-08-2021-0061

Ocloo, C. E., Xuhua, H., Akaba, S., Shi, J., & Worwui-Brown, D. K. (2020). The Determinant Factors of Business to Business (B2B) E-Commerce Adoption in Small- and Medium-Sized Manufacturing Enterprises. Journal of Global Information Technology Management, 23(3), 191–216. https://doi.org/10.1080/1097198X.2020.1792229

Olabode, O. E., Boso, N., Hultman, M., & Leonidou, C. N. (2022). Big data analytics capability and market performance: The roles of disruptive business models and competitive intensity. Journal of Business Research, 139, 1218–1230. https://doi.org/10.1016/j.jbusres.2021.10.042

Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Information Systems and E-Business Management, 16(3), 479–491. https://doi.org/10.1007/s10257-018-0377-z

Park, J.-H., & Kim, Y. B. (2021). Factors Activating Big Data Adoption by Korean Firms. Journal of Computer Information Systems, 61(3), 285–293. https://doi.org/10.1080/08874417.2019.1631133

Pateli, A., Mylonas, N., & Spyrou, A. (2020). Organizational Adoption of Social Media in the Hospitality Industry: An Integrated Approach Based on DIT and TOE Frameworks. Sustainability, 12(17), 7132. https://doi.org/10.3390/su12177132

Pelayo, C. A. D., & Arroyo, J. C. (2016). Investigación de mercados para pequeñas y medianas empresas. Editorial Universitaria| Libros UDG.

Peñaloza, G. A., Saurin, T. A., & Formoso, C. T. (2020). Monitoring complexity and resilience in construction projects: The contribution of safety performance measurement systems. Applied Ergonomics, 82, 102978. https://doi.org/10.1016/j.apergo.2019.102978

Pescaroli, G., Velazquez, O., Alcántara-Ayala, I., Galasso, C., Kostkova, P., & Alexander, D. (2020). A Likert Scale-Based Model for Benchmarking Operational Capacity, Organizational Resilience, and Disaster Risk Reduction. International Journal of Disaster Risk Science, 11(3), 404–409. https://doi.org/10.1007/s13753-020-00276-9

Pillai, R., Sivathanu, B., Mariani, M., Rana, N. P., Yang, B., & Dwivedi, Y. K. (2022). Adoption of AI-empowered industrial robots in auto component manufacturing companies. Production Planning & Control, 33(16), 1517–1533. https://doi.org/10.1080/09537287.2021.1882689

Pizam, A., Ozturk, A. B., Balderas-Cejudo, A., Buhalis, D., Fuchs, G., Hara, T., Meira, J., Revilla, M. R. G., Sethi, D., Shen, Y., State, O., Hacikara, A., & Chaulagain, S. (2022). Factors affecting hotel managers’ intentions to adopt robotic technologies: A global study. International Journal of Hospitality Management, 102, 103139. https://doi.org/10.1016/j.ijhm.2022.103139

Raguseo, E., & Vitari, C. (2018). Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects. International Journal of Production Research, 56(15), 5206–5221. https://doi.org/10.1080/00207543.2018.1427900

Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10–24. https://doi.org/10.1016/j.jclepro.2019.03.181

Rivas-Tovar L. A. (2024). Normas Apa 7a Edición: Estructura,Citas y Referencias. Instituto Politécnico Nacional. https://www.researchgate.net/publication/357046089_NORMAS_APA_7_EDICION_ESTRUCTURA_CITAS_Y_REFERENCIAS

Saidali, J., Rahich, H., Tabaa, Y., & Medouri, A. (2019). The combination between Big Data and Marketing Strategies to gain valuable Business Insights for better Production Success. Procedia Manufacturing, 32, 1017–1023. https://doi.org/10.1016/j.promfg.2019.02.316

Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 20(January), 277–320. https://doi.org/10.1002/mar.21640

Sen, S., & Yildirim, I. (2022). A Tutorial on How to Conduct Meta-Analysis with IBM SPSS Statistics. Psych, 4(4), 640–667. https://doi.org/10.3390/psych4040049

Shahzad, F., Xiu, G., Khan, I., Shahbaz, M., Riaz, M. U., & Abbas, A. (2020). The moderating role of intrinsic motivation in cloud computing adoption in online education in a developing country: a structural equation model. Asia Pacific Education Review, 21(1), 121–141. https://doi.org/10.1007/s12564-019-09611-2

Shankar, A., & Rishi, B. (2020). Convenience Matter in Mobile Banking Adoption Intention? Australasian Marketing Journal, 28(4), 273–285. https://doi.org/10.1016/j.ausmj.2020.06.008

Siddiqui, S. H., & Khan, M. S. (2019). SMEs Intention towards Use and Adoption of Digital Financial Services. Sustainable Business and Society in Emerging Economies, 1(2), 65–80. https://doi.org/10.26710/sbsee.v1i1.1007

Storkholm, M. H., Mazzocato, P., Tessma, M. K., & Savage, C. (2018). Assessing the reliability and validity of the Danish version of Organizational Readiness for Implementing Change (ORIC). Implementation Science, 13(1), 78. https://doi.org/10.1186/s13012-018-0769-y

Sun, B., & Liu, Y. (2021). Business model designs, big data analytics capabilities and new product development performance: evidence from China. European Journal of Innovation Management, 24(4), 1162–1183. https://doi.org/10.1108/EJIM-01-2020-0004

Sun, J., & Chi, T. (2018). Key factors influencing the adoption of apparel mobile commerce: An empirical study of Chinese consumers. Journal of the Textile Institute, 109(6), 785–797. https://doi.org/10.1080/00405000.2017.1371828

Sundarakani, B., Ajaykumar, A., & Gunasekaran, A. (2021). Big data driven supply chain design and applications for blockchain: An action research using case study approach. Omega, 102, 102452. https://doi.org/10.1016/j.omega.2021.102452

Taouab, O., & Issor, Z. (2019). Firm Performance: Definition and Measurement Models. European Scientific Journal ESJ, 15(1). https://doi.org/10.19044/esj.2019.v15n1p93

Teng, S., & Khong, K. W. (2021). Examining actual consumer usage of E-wallet: A case study of big data analytics. Computers in Human Behavior, 121, 106778. https://doi.org/10.1016/j.chb.2021.106778

Vitari, C., & Raguseo, E. (2020). Big data analytics business value and firm performance: linking with environmental context. International Journal of Production Research, 58(18), 5456–5476. https://doi.org/10.1080/00207543.2019.1660822

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13. https://doi.org/10.1016/j.techfore.2015.12.019

Wulandari, A., Suryawardani, B., & Marcelino, D. (2020). Social Media Technology Adoption for Improving MSMEs Performance in Bandung: a Technology-Organization-Environment (TOE) Framework. 2020 8th International Conference on Cyber and IT Service Management (CITSM), 1–7. https://doi.org/10.1109/CITSM50537.2020.9268803

Yadegaridehkordi, E., Nilashi, M., Shuib, L., Hairul Nizam Bin Md Nasir, M., Asadi, S., Samad, S., & Fatimah Awang, N. (2020). The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications, 40, 100921. https://doi.org/10.1016/j.elerap.2019.100921

Yan, C., Siddik, A. B., Akter, N., & Dong, Q. (2021). Factors influencing the adoption intention of using mobile financial service during the COVID-19 pandemic: the role of FinTech. Environmental Science and Pollution Research, 30(22), 61271–61289. https://doi.org/10.1007/s11356-021-17437-y

Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1–15. https://doi.org/10.1016/j.jbusres.2020.03.028

Yeh, C.-C., & Chen, Y.-F. (2018). Critical success factors for adoption of 3D printing. Technological Forecasting and Social Change, 132, 209–216. https://doi.org/10.1016/j.techfore.2018.02.003

Yoo, S.-K., & Kim, B.-Y. (2018). A Decision-Making Model for Adopting a Cloud Computing System. Sustainability, 10(8), 2952. https://doi.org/10.3390/su10082952

Zaman, U., Zahid, H., Habibullah, M. S., & Din, B. H. (2021). Adoption of Big Data Analytics (BDA) Technologies in Disaster Management: A Decomposed Theory of Planned Behavior (DTPB) Approach. Cogent Business & Management, 8(1). https://doi.org/10.1080/23311975.2021.1880253

Zhao, J., Xue, F., Khan, S., & Khatib, S. F. A. (2021). WITHDRAWN: Consumer behaviour analysis for business development. Aggression and Violent Behavior, 101591. https://doi.org/10.1016/j.avb.2021.101591