Purpose: Drawing on the Resource Based View (RBV) and Dynamic Capabilities Theory (DCT), the study seeks to investigate the impact of Big Data Analytics (BDA) on Project Success (PS) through Knowledge Sharing (KS) and Innovation Performance (IPF). Design/Methodology: Survey data were collected from 422 senior-level employees in IT companies, and the proposed relationships were assessed using the SMART-PLS 4 Structural Equation Modeling tool. Findings: The results show a positive and significant indirect effect of big data analytics on project success through knowledge sharing. IPF significantly mediated the relationship between BDA and PS in IT companies. Originality/Value: This study is one of the first to consider big data analytics as an essential antecedent of project success. With little or no research on the interrelationship of big data analytics, knowledge sharing, innovation performance, and organizational performance, the study investigates the mediating role of knowledge sharing and innovation performance on the relationship between BDA and PS. Implications: This study, grounded in RBV and DCT, investigates BDA’s influence on PS through KS and IPF. Implications encompass BDA’s strategic role, KS and IPF mediation, and practical and research-based insights. Findings guide BDA integration, collaborative cultures, and sustained success.

1. Aga DA, Noorderhaven N, Vallejo B (2016). Transformational leadership and project success: The mediating role of team-building. International Journal of Project Management 34(5): 806–818. doi: 10.1016/j.ijproman.2016.02.012
2. Ahmed R, Shaheen S, Philbin SP (2022). The role of big data analytics and decision-making in achieving project success. Journal of Engineering and Technology Management 65: 101697. doi: 10.1016/j.jengtecman.2022.101697
3. Alavi M, Leidner DE (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly 25(1): 107–136. doi: 10.2307/3250961
4. Alavi M, Kayworth TR, Leidner DE (2006). An empirical examination of the influence of organizational culture on knowledge management practices. Journal of Management Information Systems 22(3): 191–224. doi: 10.2753/MIS0742-1222220307
5. Azeem M, Ahmed M, Haider S, Sajjad M (2021). Expanding competitive advantage through organizational culture, knowledge sharing, and organizational innovation. Technology in Society 66: 101635. doi: 10.1016/j.techsoc.2021.101635
6. Baden-Fuller C, Teece DJ (2020). Market sensing, dynamic capability, and competitive dynamics. Industrial Marketing Management 89: 105–106. doi: 10.1016/j.indmarman.2019.11.008
7. Bag S, Wood LC, Xu L, et al. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling 153: 104559. doi: 10.1016/j.resconrec.2019.104559
8. Barlette Y, Baillette P (2022). Big data analytics in turbulent contexts: Towards organizational change for enhanced agility. Production Planning and Control 33(2–3): 105–122. doi: 10.1080/09537287.2020.1810755
9. Barney JB (1986). Strategic factor markets: Expectations, luck, and business strategy. Management Science 32(10): 1231–1241. doi: 10.1287/mnsc.32.10.1231
10. Barney J (1991). Firm resources and sustained competitive advantage. Journal of Management 17(1): 99–120. doi: 10.1177/014920639101700108
11. Barney J, Wright M, Ketchen Jr DJ (2001). The resource-based view of the firm: Ten years after 1991. Journal of Management 27(6): 625–641. doi: 10.1177/014920630102700601
12. Beer M (2023). Developing a sustainable high-commitment, high-performance system of organizing, managing, and leading: An actionable systems theory of change and development. Research in Organizational Change and Development 30: 95–128. doi: 10.1108/S0897-301620220000030006
13. Behl A, Jayawardena N, Nigam A, et al. (2023). Investigating the revised international marketing strategies during COVID-19 based on resources and capabilities of the firms: A mixed method approach. Journal of Business Research 158: 113662. doi: 10.1016/j.jbusres.2023.113662
14. Boer D, Hanke K, He J (2018). On detecting systematic measurement error in cross-cultural research: A review and critical reflection on equivalence and invariance tests. Journal of Cross-Cultural Psychology 49(5): 713–734. doi: 10.1177/0022022117749042
15. Chang YC, Chang HT, Chi H, et al. (2012). How do established firms improve radical innovation performance? The organizational capabilities view. Technovation 32(7–8): 441–451. doi: 10.1016/j.technovation.2012.03.001
16. Chen X, Vo H, Aji A, Wang F (2014). High performance integrated spatial big data analytics. In: BigSpatial '14: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, Proceedings of SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems; 4 November 2014; Dallas Texas. Association for Computing Machinery. pp. 11–14. doi: 10.1145/2676536.2676538
17. Chong D, Shi H (2015). Big data analytics: A literature review. Journal of Management Analytics 2(3): 175–201. doi: 10.1080/23270012.2015.1082449
18. Christensen PH (2007). Knowledge sharing: Moving away from the obsession with best practices. Journal of Knowledge Management 11(1): 36–47. doi: 10.1108/13673270710728222
19. Cohen J (2009). Graph twiddling in a MapReduce world. Computing in Science and Engineering 11: 29–41. doi: 10.1109/MCSE.2009.120
20. Constantiou ID, Kallinikos J (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology 30(1): 44–57. doi: 10.1057/jit.2014.17
21. Cooke-Davies T (2002). The “real” success factors on projects. International Journal of Project Management 20(3): 185–190. doi: 10.1016/S0263-7863(01)00067-9
22. Cuzzocrea A, Song IY, Davis KC (2011). Analytics over large-scale multidimensional data: The big data revolution! In: Proceedings of the ACM 14th International Workshop on Data Warehousing and OLAP, Proceedings of International Conference on Information and Knowledge Management; 28 October 2011; Glasgow Scotland, UK. Association for Computing Machinery. pp. 101–104. doi: 10.1145/2064676.2064695
23. Dean J, Ghemawat S (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM 51: 107–113. doi: 10.1145/1327452.1327492
24. Donate MJ, de Pablo JDS (2015). The role of knowledge-oriented leadership in knowledge management practices and innovation. Journal of Business Research 68(2): 360–370. doi: 10.1016/j.jbusres.2014.06.022
25. Drejer I (2004). Identifying innovation in surveys of services: A Schumpeterian perspective. Research Policy 33(3): 551–562. doi: 10.1016/j.respol.2003.07.004
26. Dwivedi YK, Kshetri N, Hughes L, et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management 71: 102642. doi: 10.1016/j.ijinfomgt.2023.102642
27. Edu AS (2022). Positioning big data analytics capabilities towards financial service agility. Aslib Journal of Information Management 74(4): 569–588. 10.1108/AJIM-08-2021-0240
28. Eisenhardt KM, Martin JA (2000). Dynamic capabilities: What are they? Strategic Management Journal 21(10–11): 1105–1121. doi: 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
29. Elia G, Raguseo E, Solazzo G, Pigni F (2022). Strategic business value from big data analytics: An empirical analysis of the mediating effects of value creation mechanisms. Information and Management 59(8): 103701. doi: 10.1016/j.im.2022.103701
30. Favaretto M, De Clercq E, Schneble CO, Elger BS (2020). What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade. PloS One 15(2): e0228987. doi: 10.1371/journal.pone.0228987
31. Gasik S (2011). A model of project knowledge management. Project Management Journal 42(3): 23–44. doi: 10.1002/pmj.20239
32. Gold AH, Malhotra A, Segars AH (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems 18(1): 185–214. doi: 10.1080/07421222.2001.11045669
33. Grover V, Chiang RHL, Liang T, Zhang D (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems 35(2): 388–423. doi: 10.1080/07421222.2018.1451951
34. Hair Jr JF, Sarstedt M, Hopkins L, Kuppelwieser VG (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review 26(2): 106–121. doi: 10.1108/EBR-10-2013-0128
35. Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019). When to use and how to report the results of PLS-SEM. European Business Review 31(1): 2–24. doi: 10.1108/EBR-11-2018-0203
36. Hasan R, Kamal MM, Daowd A, et al. (2022). Critical analysis of the impact of big data analytics on supply chain operations. Production Planning and Control. doi: 10.1080/09537287.2022.2047237
37. Hayaeian S, Hesarzadeh R, Abbaszadeh MR (2022). The impact of knowledge management strategies on the relationship between intellectual capital and innovation: Evidence from SMEs. Journal of Intellectual Capital 23(4): 765–798. doi: 10.1108/JIC-07-2020-0240
38. Henseler J, Ringle CM, Sarstedt M (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43: 115–135. doi: 10.1007/s11747-014-0403-8
39. Jahani H, Jain R, Ivanov D (2023). Data science and big data analytics: A systematic review of methodologies used in the supply chain and logistics research. Annals of Operations Research. doi: 10.1007/s10479-023-05390-7
40. Jeble S, Dubey R, Childe SJ, et al. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. The International Journal of Logistics Management 29(2): 513–538. doi: 10.1108/IJLM-05-2017-0134
41. Jugdev K, Perkins D, Fortune J, et al. (2013). An exploratory study of project success with tools, software and methods. International Journal of Managing Projects in Business 6(3): 534–551. doi: 10.1108/IJMPB-08-2012-0051
42. Karaboga T, Zehir C, Tatoglu E, et al. (2022). Big data analytics management capability and firm performance: The mediating role of a data-driven culture. Review of Managerial Science. doi: 10.1007/s11846-022-00596-8
43. Kastouni MZ, Lahcen AA (2020). Big data analytics in telecommunications: Governance, architecture and use cases. Journal of King Saud University-Computer and Information Sciences 34(6): 2758–2770. doi: 10.1016/j.jksuci.2020.11.024
44. Garmaki M, Gharib RK, Boughzala I (2023). Big data analytics capability and contribution to firm performance: The mediating effect of organizational learning on firm performance. Journal of Enterprise Information Management 35(5): 1161–1184. doi: 10.1108/JEIM-06-2021-0247
45. Khan A, Tao M (2022). Knowledge absorption capacity’s efficacy to enhance innovation performance through big data analytics and digital platform capability. Journal of Innovation and Knowledge 7(3): 100201. doi: 10.1016/j.jik.2022.100201
46. Kharub M, Gupta H, Rana S, McDermott O (2023). Employee’s performance and Kaizen events’ success: Does supervisor behaviour play a moderating role? The TQM Journal. doi: 10.1108/TQM-06-2022-0203
47. Lehrer C, Wieneke A, Brocke JV, et al. (2018). How big data analytics enables service innovation: Materiality, affordance, and the individualization of service. Journal of Management Information Systems 35(2): 424–460. doi: 10.1080/07421222.2018.1451953
48. Lin S, Lin J, Han F, Luo XR (2022). How big data analytics enables the alliance relationship stability of contract farming in the age of digital transformation. Information and Management 59(6): 103680. doi: 10.1016/j.im.2022.103680
49. Lu Y, Ram K (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Quarterly 35(4): 931–954. doi: 10.2307/41409967
50. Lutfi A, Alrawad M, Alsyouf A, et al. (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 70: 103129. doi: 10.1016/j.jretconser.2022.103129
51. Maheshwari P, Kamble S (2023). A comparative approach for sustainable supply chain finance to implement industry 4.0 in micro-, small-, and medium-sized enterprises. In: Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance. Springer. pp. 207–230.
52. Mangla SK, Raut R, Narwane VS, et al. (2021). Mediating effect of big data analytics on project performance of small and medium enterprises. Journal of Enterprise Information Management 34(1): 168–198. doi: 10.1108/JEIM-12-2019-0394
53. Mariani M, Baggio R (2022). Big data and analytics in hospitality and tourism: A systematic literature review. International Journal of Contemporary Hospitality Management 34(1): 231–278. doi: 10.1108/IJCHM-03-2021-0301
54. Mariani M, Baggio R, Fuchs M, Höepken W (2018). Business intelligence and big data in hospitality and tourism: A systematic literature review. International Journal of Contemporary Hospitality Management 30(12): 3514–3554. doi: 10.1108/IJCHM-07-2017-0461
55. Marshall A, Mueck S, Shockley R (2015). How leading organizations use big data and analytics to innovate. Strategy and Leadership 43(5): 32–39. doi: 10.1108/SL-06-2015-0054
56. Mikalef P, Boura M, Lekakos G, Krogstie J (2019). Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment. British Journal of Management 30(2): 272–298. doi: 10.1111/1467-8551.12343
57. Möller K, Rajala A (2007). Rise of strategic nets—New modes of value creation. Industrial Marketing Management 36(7): 895–908. doi: 10.1016/j.indmarman.2007.05.016
58. Müller O, Junglas I, Brocke JV, Debortoli S (2016). Utilizing big data analytics for information systems research: Challenges, promises and guidelines. European Journal of Information Systems 25(4): 289–302. doi: 10.1057/ejis.2016.2
59. Nonaka I (1994). A dynamic theory of organizational knowledge creation. Organization Science 5(1): 14–37. doi: 10.1287/orsc.5.1.14
60. Norena-Chavez D, Thalassinos E (2023). Fueling innovation performance through entrepreneurial leadership: Assessing the neglected mediating role of intellectual capital. Journal of Infrastructure, Policy and Development 7(1): 2020. doi: 10.24294/jipd.v7i1.2020
61. Obitade OP (2021). The mediating role of knowledge management and information systems selection management capability on big data analytics quality and firm performance. Journal of Decision Systems 32(1): 201–241. doi: 10.1080/12460125.2021.1966162
62. O’Driscoll T (2014). Can big data deliver added value. Training 51(2): 51.
63. Patrucco AS, Marzi G, Trabucchi D (2023). The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions. Technovation 126: 102814. doi: 10.1016/j.technovation.2023.102814
64. Pearce CL (2004). The future of leadership: Combining vertical and shared leadership to transform knowledge work. Academy of Management Perspectives 18(1): 47–57. doi: 10.5465/ame.2004.12690298
65. Preacher KJ, Hayes AF (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods 40(3): 879–891. doi: 10.3758/BRM.40.3.879
66. Rahimi M, Kumar P, Moazzamigodarzi M, Mishra AR (2022). Digital transformation challenges in sustainable financial service systems using novel interval-valued Pythagorean fuzzy double normalization-based multiple aggregation approach. Environment, Development and Sustainability. doi: 10.1007/s10668-022-02719-3
67. Ramakrishnan T, Jones MC, Sidorova A (2012). Factors influencing business intelligence (BI) data collection strategies: An empirical investigation. Decision Support Systems 52(20): 486–496. doi: 10.1016/j.dss.2011.10.009
68. Ringle CM, Sarstedt M, Mitchell R, Gudergan SP (2018). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management 1(1): 1–27. doi: 10.1080/09585192.2017.1416655
69. Rivera DS, Shanks G (2015). A dashboard to support management of business analytics capabilities. Journal of Decision Systems 24(1): 73–86. doi: 0.1080/12460125.2015.994335
70. Robertson J, Caruana A, Ferreira C (2021). Innovation performance: The effect of knowledge-based dynamic capabilities in cross-country innovation ecosystems. International Business Review 32(2): 101866. doi: 10.1016/j.ibusrev.2021.101866
71. Ruoslahti H (2020). Complexity in project co-creation of knowledge for innovation. Journal of Innovation and Knowledge 5(4): 228–235. doi: 10.1016/j.jik.2019.12.004
72. Saeed M, Adiguzel Z, Shafique I, et al. (2023). Big data analytics-enabled dynamic capabilities and firm performance: Examining the roles of marketing ambidexterity and environmental dynamism. Business Process Management Journal 29(4): 1204–1226. doi: 10.1108/BPMJ-01-2023-0015
73. Saggi MK, Jain S (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing and Management 54(5): 758–790. doi: 10.1016/j.ipm.2018.01.010
74. Sakr S (2013). Processing large-scale graph data: A guide to current technology. Available online: https://gchockler.com/wp-content/uploads/2017/06/os-giraph-pdf.pdf (accessed on 21 August 2023).
75. Sarker IH (2022). Ai-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science 3(2): 158. doi: 10.1007/s42979-022-01043-x
76. Serrador P, Pinto JK (2015). Does Agile work? A quantitative analysis of agile project success. International Journal of Project Management 33(5): 1040–1051. doi: 10.1016/j.ijproman.2015.01.006
77. Shenhar AJ, Dvir D (2007). Reinventing Project Management: The Diamond Approach to Successful Growth and Innovation. Harvard Business Review Press.
78. Shirazi F, Tseng HT, Adegbite O, et al. (2022). New product success through big data analytics: An empirical evidence from Iran. Information Technology and People 35(5): 1513–1539. doi: 10.1108/ITP-03-2020-0105
79. Su X, Zeng W, Zheng M, et al. (2022). Big data analytics capabilities and organizational performance: The mediating effect of dual innovations. European Journal of Innovation Management 25(4): 1142–1160. doi: 10.1108/EJIM-10-2020-0431
80. Teece DJ (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal 28(13): 1319–1350. doi: 10.1002/smj.640
81. Wamba-Taguimdje SL, Wamba SF, Kamdjoug JRK, et al. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal 26(7): 1893–1924. doi: 10.1108/BPMJ-10-2019-0411
82. Wang H, Qi S, Zhou C, et al. (2022). Green credit policy, government behavior and green innovation quality of enterprises. Journal of Cleaner Production 331: 129834. doi: 10.1016/j.jclepro.2021.129834
83. Wang Y, Hajli N (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research 70: 287–299. doi: 10.1016/j.jbusres.2016.08.002
84. Williams P (2017). Assessing collaborative learning: Big data, analytics and university futures. Assessment and Evaluation in Higher Education 42(6): 978–989. doi: 10.1080/02602938.2016.1216084
85. Wu Q, Yan D, Umair M (2023). Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs. Economic Analysis and Policy 77: 1103–1114. doi: 10.1016/j.eap.2022.11.024
86. Yang J, Li J, Delios A (2015). Will a second mouse get the cheese? Learning from early entrants’ failures in a foreign market. Organization Science 26(3): 908–922. doi: 10.1287/orsc.2015.0967
87. Young R, Chen W, Quazi A, et al. (2020). The relationship between project governance mechanisms and project success: An international data set. International Journal of Managing Projects in Business 13(7): 1496–1521. doi: 10.1108/IJMPB-10-2018-0212