The effect of organizational inertia on the adoption of big data analytics
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Abstract
Purpose – This research aims to examine the impact of organizational inertia (OI) on the adoption of big data analytics (BDA), considering OI as a second-order formative construct composed of insight inertia, action inertia and psychological inertia. The study also explores the moderating effect of department leader power on the relationship between OI and BDA adoption.
Design/methodology/approach – The study uses a mixed-methods approach, combining quantitative partial least squares structural equation modelling (PLS-SEM) analysis with qualitative interviews, highlighting BDA’s greater susceptibility to internal inertial forces than other IT technologies, due to its emphasis on decision-making.
Findings – Hypothesis testing using PLS-SEM demonstrates significant contributions from these OI dimensions and confirms a negative relationship between OI and BDA adoption, moderated positively by department leader power.
Social implications – Overcoming OI constrains BDA adoption in Latin America. Strengthening datadriven capabilities improves service efficiency, innovation and competitiveness, which enhances employment quality and access to better services. These advances contribute to inclusive development and support SDG 9 and SDG 8.
Originality/value – This study contributes to understanding and informing strategies to overcome organizational resistance, promote BDA adoption and advance digital transformation to improve productivity and societal outcomes in Latin America.
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References
Aboelmaged, M. and Mouakket, S. (2020), “Influencing models and determinants in big data analytics research: a bibliometric analysis”, Information Processing and Management, Vol. 57 No. 4, p. 102234.
Alkayid, K., Selem, K.M., Shehata, A.E. and Tan, C.C. (2022), “Leader vision, organisational inertia and service hotel employee creativity: role of knowledge-donating”, Current Psychology, Vol. 42 No. 4, pp. 3382-3394.
Anning-Dorson, T., Baba, F., Zulu, M. and Acheampong, G. (2025), “Data-driven dynamic capabilities in emerging markets: a grounded theory approach to digital transformation in African retail banking”, International Journal of Information Management, Vol. 84, p. 102914.
Armstrong, J.S. and Overton, T.S. (1977), “Estimating nonresponse bias in mail surveys”, Journal of Marketing Research, Vol. 14 No. 3, pp. 396-402.
Babalghaith, R. and Aljarallah, A. (2024), “Factors affecting big data analytics adoption in small and medium enterprises”, Information Systems Frontiers, Vol. 26 No. 6, pp. 1-23.
Babu, M.P. and Sastry, S.H. (2014), “Big data and predictive analytics in ERP systems for automating decision making process”, in 2014 IEEE 5th International Conference on Software Engineering and Service Science, IEEE, pp. 259-262.
Barlette, Y. and Baillette, P. (2022), “BDA in turbulent contexts: towards organisational change for enhanced agility”, Production Planning and Control, Vol. 33 Nos 2-3, pp. 105-122.
Brown, S. (2023), “Survey details data officers’ priorities, challenges for 2023”, available at: https:// mitsloan.mit.edu/ideas-made-to-matter/survey-details-data-officers-priorities-challenges-2023
Chen, C.T., Chen, S.C., Khan, A., Lim, M.K. and Tseng, M.L. (2024), “Antecedents of big data analytics and artificial intelligence adoption on operational performance: the ChatGPT platform”, Industrial Management and Data Systems, Vol. 124 No. 7.
Damanpour, F. and Schneider, M. (2006), “Phases of the adoption of innovation in organisations: effects of environment, organisation and top managers 1”, British Journal of Management, Vol. 17 No. 3, pp. 215-236.
Davenport, T. and Bean, R. (2023), “Action and inaction on data, analytics, and AI”, available at: https://sloanreview.mit.edu/article/action-and-inaction-on-data-analytics-and-ai/
Economic Commission for Latin America and the Caribbean (ECLAC) (2022), A Digital Path for Sustainable Development in Latin America and the Caribbean, ECLAC.
El-Haddadeh, R., Fadlalla, A. and Hindi, N.M. (2025), “Big data analytics adoption success: value chain process-level perspective”, Business Process Management Journal, Vol. 31 No. 2, pp. 686-707.
Gal, D. (2006), “A psychological law of inertia and the illusion of loss aversion”, Judgment and Decision Making, Vol. 1 No. 1, pp. 23-32.
Godkin, L. and Allcorn, S. (2008), “Overcoming organisational inertia: a tripartite model for achieving strategic organisational change”, The Journal of Applied Business and Economics, Vol. 8 No. 1, pp. 82-96.
Gopal, P.R.C., Rana, N.P., Krishna, T.V. and Ramkumar, M. (2022), “Impact of big data analytics on supply chain performance: an analysis of influencing factors”, Annals of Operations Research, Vol. 333 Nos 2-3, pp. 769-797.
Grover, V., Chiang, R.H.L., Liang, T.-P. and Zhang, D. (2018), “Creating strategic business value from big data analytics: a research framework”, Journal of Management Information Systems, Vol. 35 No. 2, pp. 388-423, doi: 10.1080/07421222.2018.1451951.
Haag, S. (2014), “Organisational inertia as barrier to firms’ IT adoption–multidimensional scale development and validation”, Smart Sustainability: The Information Systems Opportunity: 20th Americas Conference on Information Systems (AMICS 2014), Curran Associates, pp. 2252-2262.
Hair, J.F., Hult, G.T.M., Ringle, C. and Sarstedt, M. (2017), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage Publications, p. 384.
Hannan, M.T. and Freeman, J. (1984), “Structural inertia and organisational change”, American Sociological Review, Vol. 49 No. 2, pp. 149-164.
Hedberg, B. and Ericson, A. (1997), Insiktstroghet Och Manovertroghet Organisationers Omorientering [Insight Inertia and Action Inertia in Organisational Reorientation]. Blt Hedberg and Se Sjostrand, Från Foretagskriser till Industripolitik (from Organisational Crisis to Industrial Politics), Liber, Malmo, pp. 54-66.
Huang, H.C., Lai, M.C., Lin, L.H. and Chen, C.T. (2013), “Overcoming organisational inertia to strengthen business model innovation: an open innovation perspective”, Journal of Organisational Change Management, Vol. 26 No. 6, pp. 977-1002.
Huang, J.C., Lu, C.W., Wen, H.M., Hsieh, C.T. and Wang, H.W. (2020), “The study of organisational inertia, business model innovation and organisational performance in Taiwan financial institutions: organisational learning perspective”, Revista Argentina de Clínica Psicológica, Vol. 29 No. 5, pp. 104-115.
Hung, J.L., He, W. and Shen, J. (2020), “Big data analytics for supply chain relationship in banking”, Industrial Marketing Management, Vol. 86, pp. 144-153.
Jahanmir, S.F. and Cavadas, J. (2018), “Factors affecting late adoption of digital innovations”, Journal of Business Research, Vol. 88 No. C, pp. 337-343.
Kock, N. and Lynn, G.S. (2012), “Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations”, Journal of the Association for Information Systems, Vol. 13 No. 7, pp. 546-580.
Li, W., Chen, W., Pang, Q. and Song, J. (2023), “How to mitigate the inhibitory effect of organisational inertia on corporate digital entrepreneurship? ”, Frontiers in Psychology, Vol. 14, p. 1130801.
Li, Y., Wang, J., Li, X. and Zhao, W. (2007), “Design creativity in product innovation”, The International Journal of Advanced Manufacturing Technology, Vol. 33 Nos 3-4, pp. 213-222.
Liao, S.H., Fei, W.C. and Liu, C.T. (2008), “Relationship between knowledge inertia, organisational learning and organisational innovation”, Technovation, Vol. 28 No. 4, pp. 183-195.
Lindell, M.K. and Whitney, D.J. (2001), “Accounting for common method variance in cross-sectional research designs”, Journal of Applied Psychology, Vol. 86 No. 1, p. 114.
Lutfi, A., Alrawad, M., Alsyouf, A., Almaiah, M.A., Al-Khasawneh, A., Al-Khasawneh, A.L., Alshira’h, A.F., Alshirah, M.H., Saad, M. and Ibrahim, N. (2023), “Drivers and impact of big data analytic adoption in the retail industry: a quantitative investigation applying structural equation modeling”, Journal of Retailing and Consumer Services, Vol. 70 No. C, p. 103129.
Manpowergroup (2023), “Expectativas de Empleo Q2”, available at: https://manpowergroup.pe/wps/ wcm/connect/manpowergroup/9afc77e9-61a1-4d70-b235-a2d5c78cbc91/PERU_MEOS_Rep orte_Q2_2023_final+281%29.pdf?MOD=JPERES
Mikalef, P., van de Wetering, R. and Krogstie, J. (2021), “Building dynamic capabilities by leveraging big data analytics: the role of organisational inertia”, Information and Management, Vol. 58 No. 6, pp. 1-17.
Moradi, E., Jafari, S.M., Doorbash, Z.M. and Mirzaei, A. (2021), “Impact of organisational inertia on business model innovation, open innovation and corporate performance”, Asia Pacific Management Review, Vol. 26 No. 4, pp. 171-179.
Nedzinskas, Š., Pundzienė, A., Buožiūtė-Rafanavičienė, S. and Pilkienė, M. (2013), “The impact of dynamic capabilities on SME performance in a volatile environment as moderated by organisational inertia”, Baltic Journal of Management, Vol. 8 No. 4, pp. 376-396.
Orero-Blat, M., Rodríguez, A.L.L. and Palacios-Marqués, D. (2025), “The strategic advantage of innovative organizational culture: an exploratory analysis in digital transformation and big data analytics capabilities”, Journal of Management and Organization, Vol. 31 No. 2, pp. 443-462.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.
Ringle, C.M., Wende, S. and Becker, J.-M. (2022), “SmartPLS 4”, SmartPLS GmbH, Oststeinbek, available at: www.smartpls.com
Shahbaz, M., Gao, C., Zhai, L., Shahzad, F. and Hu, Y. (2019), “Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change”, Journal of Big Data, Vol. 6 No. 1, pp. 1-20.
Siggelkow, N. and Rivkin, J.W. (2005), “Speed and search: designing organisations for turbulence and complexity”, Organization Science, Vol. 16 No. 2, pp. 101-122.
Simmering, M.J., Fuller, C.M., Richardson, H.A., Ocal, Y. and Atinc, G.M. (2015), “Marker variable choice, reporting, and interpretation in the detection of common method variance: a review and demonstration”, Organizational Research Methods, Vol. 18 No. 3, pp. 473-511.
Sull, D.N. (1999), “Why good companies go bad?”, Harvard Business Review, Vol. 77 No. 4, pp. 42-52.
Teofilus, T., Ardyan, E., Sutrisno, T.F., Sabar, S. and Sutanto, V. (2022), “Managing organisational inertia: Indonesian family business perspective”, Frontiers in Psychology, Vol. 13, p. 839266.
Tu, M. (2018), “An exploratory study of internet of things (IoT) adoption intention in logistics and supply chain management”, The International Journal of Logistics Management, Vol. 29 No. 1, pp. 131-151.
United Nations Conference on Trade and Development (UNCTAD) (2021), “Informe sobre la economía digital 2021”, available at: https://unctad.org/system/files/official-document/der2021_es_0.pdf
Verma, S., Bhattacharyya, S.S. and Kumar, S. (2018), “An extension of the technology acceptance model in the big data analytics system implementation environment”, Information Processing and Management, Vol. 54 No. 5, pp. 791-806.
Vilgis, V., Jordán, V. and Patiño, A. (2023), “Medición de la economía de Internet en América Latina: los casos del Brasil, Chile, Colombia y México”, ECLAC.
Youssef, M.A.E.A., Eid, R. and Agag, G. (2022), “Cross-national differences in big data analytics adoption in the retail industry”, Journal of Retailing and Consumer Services, Vol. 64 No. C, p. 102827.
Yu, J., Taskin, N., Nguyen, C.P., Li, J. and Pauleen, D. (2022), “Investigating the determinants of big data analytics adoption in decision making: an empirical study in New Zealand, China, and Vietnam”, Pacific Asia Journal of the Association for Information Systems, Vol. 14 No. 4, p. 3.
Zeithaml, V.A., Jaworski, B.J., Kohli, A.K., Tuli, K.R., Ulaga, W. and Zaltman, G. (2020), “A theories- in-use approach to building marketing theory”, Journal of Marketing, Vol. 84 No. 1, pp. 32-51.
Zhen, J., Cao, C., Qiu, H. and Xie, Z. (2021), “Impact of organisational inertia on organisational agility: the role of IT ambidexterity”, Information Technology and Management, Vol. 22 No. 1, pp. 53-65.
Further reading
Hedberg, B. and Wolff, R. (2003), “Organizing, learning, and strategizing: from construction to discovery”, in Dierkes, M., Antal, A.B., Child, J. and Nonaka, I. (Eds), Handbook of Organisational Learning and Knowledge, Oxford University Press, Oxford, pp. 535-556.
Palomino-Tamayo, W. and Timaná, J.S. (2022), “Creating firm value, overcoming organisational inertia through the marketing value chain”, Academia Revista Latinoamericana de Administración, Vol. 35 No. 1, pp. 20-36.
Shao, Z., Wang, T. and Feng, Y. (2016), “Impact of chief information officer’s strategic knowledge and structural power on enterprise systems success”, Industrial Management and Data Systems, Vol. 116 No. 1, pp. 43-64.