This study investigates the impact of business intelligence (BI) and integrated reporting (IR) on human resource (HR) analytics and organizational performance (OP). Grounded in the resource-based view (RBV) theory, this study explores the mediating role of HR analytics in the relationship between BI, IR and OP. Data were collected from 353 IT managers, and the proposed model was analysed using partial least squares structural equation modelling via SmartPLS 4.0. The results reveal that both BI and IR have a positive and significant influence on HR analytics and OP. Furthermore, HR analytics partially mediates these relationships, strengthening their effects on organizational outcomes. This study contributes to the RBV literature by highlighting HR analytics as a strategic resource that enhances OP. The findings offer valuable insights into business leaders, underscoring the critical role of HR analytics in data-driven decision-making, optimizing HR practices and gaining a competitive advantage.
AgarwalR., & AtifM. (2024). Early adopters of institutional creativity in integrated reporting. Review of Accounting and Finance, 23(2), 205–221. https://doi.org/10.1108/RAF-07-2023-0209
2.
Al-DwairiR. M., Al-KhataybehM., NajadatD., & RawashdehA. (2024). User self-efficacy enhances business intelligence tools for organizational agility. Indonesian Journal of Electrical Engineering and Computer Science, 36(1), 592–602. https://doi.org/10.11591/ijeecs.v36.i1.pp592-602
3.
Al-MaaitahT. A., KhalidA. S., Al-JunaidiA. M. F., DaabsehT. K. I.Al, AlnawaflehA., QatawnehN. A., & Al-MaaitahD. A. (2024). Strategies for success: A theoretical model for implementing business intelligence systems to enhance organizational performance. International Journal of Advanced and Applied Sciences, 11(5), 55–61. https://doi.org/10.21833/ijaas.2024.05.006
4.
AroraM., PrakashA., MittalA., & SinghS. (2022). Moderating role of resistance to change in the actual adoption of HR analytics in the Indian banking and financial services industry. Evidence-Based HRM, 11(3), 253–270. https://doi.org/10.1108/EBHRM-12-2021-0249
5.
AroraM., PrakashA., MittalA., & SinghS. (2022a). Examining the slow acceptance of HR analytics in the Indian engineering and construction industry: a SEM-ANN-based approach. Engineering, Construction and Architectural Management, 31(5), 1973–1993. https://doi.org/10.1108/ECAM-09-2021-0795
BahugunaP. C., SrivastavaR., & TiwariS. (2023). Human resources analytics: Where do we go from here?Benchmarking. https://doi.org/10.1108/bij-06-2022-0401
8.
Bar-GilO., RonT., & CzerniakO. (2023). AI for the people? Embedding AI ethics in HR and people analytics projects. Technology in Society, 77(May2023), 102527. https://doi.org/10.1016/j.techsoc.2024.102527
9.
BaronR. M., & KennyD. A. (1986). The moderator-mediator variable distinction in social psychological research. Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
10.
BechterB., BrandlB., & LehrA. (2022). The role of the capability, opportunity, and motivation of firms for using human resource analytics to monitor employee performance: A multi-level analysis of the organisational, market, and country context. New Technology, Work and Employment, 37(3), 398–424. https://doi.org/10.1111/ntwe.12239
11.
BelizónM. J., & KieranS. (2022). Human resources analytics: A legitimacy process. Human Resource Management Journal, 32(3), 603–630. https://doi.org/10.1111/1748-8583.12417
12.
BuallayA., Al HawajA. A., & HamdanA. (2021). Integrated reporting and performance: a cross-country comparison of GCC Islamic and conventional banks. Journal of Islamic Marketing, 12(8), 1619–1636. https://doi.org/10.1108/JIMA-08-2017-0084
13.
CavanaghJ., WalkerM., Pariona-CabreraP., BartramT., & HalvorsenB. (2022). Health services in Australia and the impact of antiquated rostering practices on medical scientists: A case for HR analytics and evidenced-based human resource management. Personnel Review. https://doi.org/10.1108/PR-09-2021-0690
14.
CayratC., & BoxallP. (2022). Exploring the phenomenon of HR analytics: A study of challenges, risks and impacts in 40 large companies. Journal of Organizational Effectiveness: People and Performance, 9(4), 572–590. https://doi.org/10.1108/JOEPP-08-2021-0238
15.
ChatterjeeS., ChaudhuriR., VrontisD., & SiachouE. (2022). Examining the dark side of human resource analytics: An empirical investigation using the privacy calculus approach. International Journal of Manpower, 43(1), 52–74. https://doi.org/10.1108/IJM-02-2021-0087
16.
CohenJ. (1988). Statistical power analysis for the behavioral sciences (2nd ed). Routledge. https://doi.org/10.4324/9780203771587
17.
DhankharK., & SinghA. (2022). Employees’ adoption of HR analytics: A theoretical framework based on career construction theory. Evidence-Based HRM, 11(3), 395–411. https://doi.org/10.1108/EBHRM-02-2022-0053
18.
DhimanN., KumarS., & NagpalT. (2023). Employee’s intentions to use HR analytics: Technology acceptance model with job relevance and self-efficacy. Vision, 1–15. https://doi.org/10.1177/09722629231183540
19.
ElbendaryI., & Mohamed ShehataG. (2024). Towards a more flexible SMEs: Can HR flexibility spur the nexus between capacity-enhancing HR practices and job performance?Management & Sustainability: An Arab Review, 3(4), 421–445. https://doi.org/10.1108/msar-10-2023-0053
20.
EllmerM., ReichelA., MuhammadG., SiddiquiM. S., RasheedR., ShabbirH., SherR. F., GurusingheR. N., ArachchigeB. J. H., DayarathnaD., VargasR., YurovaY. V., RuppelC. P., TworogerL. C., GreenwoodR., JanaB., KaushikT., RayatR., MarlerJ. H., … JarvenpääM. (2022). The ball of wax we call HR analytics. Journal of Organizational Effectiveness, 43(2), 145–156. https://doi.org/10.1108/MRR-04-2016-0084
FallettaS. V., & CombsW. L. (2021). The HR analytics cycle: A seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work-Applied Management, 13(1), 51–68. https://doi.org/10.1108/jwam-03-2020-0020
23.
FornellC., & LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
24.
Fosso WambaS. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67(July), 102544. https://doi.org/10.1016/j.ijinfomgt.2022.102544
25.
FreemanR. E., DmytriyevS. D., & PhillipsR. A. (2021). Stakeholder theory and the resource-based view of the firm. Journal of Management, 47(7), 1757–1770. https://doi.org/10.1177/0149206321993576
26.
GargS., SinhaS., KarA. K., ManiM., VermaS., RanaN., MeherJ. R., VottoA. M., ValechaR., NajafiradP., RaoH. R., RodgersW., MurrayJ. M., StefanidisA., DegbeyW. Y., TarbaS. Y., TambeP., CappelliP., YakubovichV., … Martínez CaroE. (2021). Recruiting digital talent: The strategic role of recruitment in organisations’ digital transformation. International Journal of Organizational Analysis, 35(1), 100047. https://doi.org/10.1177/2397002220952734
27.
HairJ. F., RisherJ. J., SarstedtM., & RingleC. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
28.
HenselerJ., DijkstraT. K., SarstedtM., RingleC. M., DiamantopoulosA., StraubD. W., KetchenD. J., HairJ. F., HultG. T. M., & CalantoneR. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928
29.
HenselerJ., RingleC. M., & SarstedtM. (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. https://doi.org/10.1007/s11747-014-0403-8
30.
HermantoI. R., WidyariniL. A., & DarmaD. C. (2024). Digitalization impact on sustainable firm performance of small, medium, and large businesses. Virtual Economics, 7(1), 7–24. https://doi.org/10.34021/ve.2024.07.01(1)
31.
HuangX., YangF., ZhengJ., FengC., ZhangL., CavanaghJ., Pariona-CabreraP., HalvorsenB., HastutiR., ChoW., ChoiS., ChoiH., SinghS., MuduliA., DhimanN., KumarS., NagpalT., KurikalaV., ParvathiV., … AfnanR. T. (2023). Identifying the characteristics that lead to effective data analytics deployment in human resource management—A paradigm for consequences. International Journal of Knowledge Management Studies, 14(4), 114312. https://doi.org/10.1504/IJKMS.2023.133866
32.
IoakeimidouD., ChatzoudesD., SymeonidisS., & ChatzoglouP. (2023). HRA adoption via organizational analytics maturity: Examining the role of institutional theory, resource-based view and diffusion of innovation. International Journal of Manpower. https://doi.org/10.1108/IJM-10-2022-0496
33.
KashiveN., & KhannaV. T. (2023). Emerging HR analytics role in a crisis: An analysis of LinkedIn data. Competitiveness Review, 33(6), 1179–1204. https://doi.org/10.1108/CR-03-2022-0029
34.
KezaabuS., NkundabanyangaS. K., BananukaJ., & KabuyeF. (2024). Managerial competences and integrated reporting practices: Perception-based evidence from Uganda. Journal of Accounting in Emerging Economies, 14(1), 157–186. https://doi.org/10.1108/JAEE-07-2021-0219
35.
Kim VuA. T., BuiN. T., & TranD. T. (2024). Factors that affect the application of integrated report in listed companies in Vietnam. Asian Review of Accounting, 32(2), 349–369. https://doi.org/10.1108/ARA-03-2023-0073
36.
LathabhavanR. (2024). Mental well-being through HR analytics: Investigating an employee supportive framework. Personnel Review, 53(5), 1110–1128. https://doi.org/10.1108/PR-11-2022-0836
37.
LeeJ. Y., & LeeY. (2024). Integrative literature review on people analytics and implications from the perspective of human resource development. Human Resource Development Review, 23(1), 58–87. https://doi.org/10.1177/15344843231217181
38.
MaD., & WuW. (2024). Does artificial intelligence drive technology convergence? Evidence from Chinese manufacturing companies. Technology in Society, 79(March), 102715. https://doi.org/10.1016/j.techsoc.2024.102715
39.
MakriM., MakanL. T., & KabraK. C. (2024). Board characteristics and integrated reporting in an emerging market: Evidence from India. Asian Journal of Accounting Research, 9(1), 2–12. https://doi.org/10.1108/AJAR-02-2022-0050
40.
MargheritaA. (2022). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2), 100795. https://doi.org/10.1016/j.hrmr.2020.100795
41.
McCartneyS., & FuN. (2022a). Bridging the gap: Why, how and when HR analytics can impact organizational performance. Management Decision, 60(13), 25–47. https://doi.org/10.1108/MD-12-2020-1581
42.
McCartneyS., & FuN. (2022b). Promise versus reality: A systematic review of the ongoing debates in people analytics. Journal of Organizational Effectiveness, 9(2), 281–311. https://doi.org/10.1108/JOEPP-01-2021-0013
43.
McCartneyS., MurphyC., & MccarthyJ. (2020). 21st century HR: A competency model for the emerging role of HR Analysts. Personnel Review, 50(6), 1495–1513. https://doi.org/10.1108/PR-12-2019-0670
44.
NitzlC. (2016). The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: Directions for future theory development. Journal of Accounting Literature, 37, 19–35. https://doi.org/10.1016/j.acclit.2016.09.003
45.
OlszakC. M. (2022). Business intelligence systems for innovative development of organizations. Procedia Computer Science, 207, 1754–1762. https://doi.org/10.1016/j.procs.2022.09.233
46.
PrzegalinskaA., TriantoroT., KovbasiukA., CiechanowskiL., FreemanR. B., & SowaK. (2025). Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives. International Journal of Information Management, 81 (November2024). https://doi.org/10.1016/j.ijinfomgt.2024.102853
47.
QamarY., & SamadT. A. (2022). Human resource analytics: A review and bibliometric analysis. Personnel Review, 51(1), 251–283. https://doi.org/10.1108/PR-04-2020-0247
48.
QatawnehN. (2024). Empirical insights into business intelligence adoption and decision-making performance during the digital transformation era: Extending the TOE model in the Jordanian banking sector. Journal of Open Innovation: Technology, Market, and Complexity, 10(4), 100401. https://doi.org/10.1016/j.joitmc.2024.100401
49.
QuH., DingZ., AhlstromD., WuC., & CaiH. H. (2024). When I have to versus when I am able to: Behavioral and resource explanations for firms’ international expansion via exporting. International Business Review, 34(1), 102358. https://doi.org/10.1016/j.ibusrev.2024.102358
50.
RahmanM. M., & Abdul Kader JilaniM. M. (2024). Unveiling synergies: Business intelligence nexus with environmental and financial performance in Bangladeshi manufacturing firms. Business Strategy and Development, 7(4). https://doi.org/10.1002/bsd2.70013
51.
RamachandranR., BabuV., & MurugesanV. P. (2023). Human resource analytics revisited: A systematic literature review of its adoption, global acceptance and implementation. Benchmarking. https://doi.org/10.1108/BIJ-04-2022-0272
52.
RatnamD. S., & DeviV. R. (2024). Addressing impediments to HR analytics adoption: Guide to HRD professionals. Human Resource Development International, 27(1), 142–151. https://doi.org/10.1080/13678868.2023.2195986
53.
RingleC. M., SarstedtM., MitchellR., & GuderganS. P. (2020). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2017.1416655
54.
RiveraY. A. (2024). Analysing the lobbying behaviour of experts during the due process of the International Integrated Reporting Framework. Sustainability Accounting, Management and Policy Journal, 15(1), 23–47. https://doi.org/10.1108/SAMPJ-07-2021-0310
55.
RoulJ., MohapatraL. M., & KameshA. V. S. (2024). Exploring the landscape of human resource analytics: A systematic literature review and future agenda. Human Resource Development International, 1–14. https://doi.org/10.1080/13678868.2024.2334982
56.
ShetS. V., PoddarT., Wamba SamuelF., & DwivediY. K. (2021). Examining the determinants of successful adoption of data analytics in human resource management: A framework for implications. Journal of Business Research, 131(March), 311–326. https://doi.org/10.1016/j.jbusres.2021.03.054
57.
SinghA., & JhaS. (2024). Navigating strategic leadership: Unraveling the dynamics of technological innovation in organizations. In KauravR. P. Singh & MishraV. (Eds.), Review of technologies and disruptive business strategies: Vol. 3. Review of management literature (pp. 205–227). Emerald Publishing. https://doi.org/10.1108/S2754-586520240000003011
58.
SinghS., & MuduliA. (2023). Examining the role of organisational trust on information sharing intention and human resource analytics outcomes: An empirical study. International Journal of Knowledge Management Studies, 14(4), 435–456. https://doi.org/10.1504/IJKMS.2023.133866
59.
SinghR., SharmaP., ForoponC., & BelalH. M. (2022). The role of big data and predictive analytics in the employee retention: A resource-based view. International Journal of Manpower, 43(2). https://doi.org/10.1108/IJM-03-2021-0197
60.
SobhanR., & MiaM. R. (2024). Impact of board characteristics on integrated reporting: Evidence from South Asian countries. Journal of Financial Reporting and Accounting. https://doi.org/10.1108/JFRA-07-2023-0363
61.
SousaM. J., & DiasI. (2020). Business intelligence for human capital management. International Journal of Business Intelligence Research, 11(1), 38–49. https://doi.org/10.4018/IJBIR.2020010103
62.
StoneM. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
63.
StrohmeierS., ColletJ., & KabstR. (2022). (How) do advanced data and analyses enable HR analytics success? A neo-configurational analysis. Baltic Journal of Management, 17(3), 285–303. https://doi.org/10.1108/BJM-05-2021-0188
64.
van den HeuvelS., BondaroukT., CavanaghJ., WalkerM., Pariona-CabreraP., BartramT., HalvorsenB., QamarY., SamadT. A., AroraM., PrakashA., MittalA., SinghS., Álvarez-GutiérrezF. J., StoneD. L., CastañoA. M., García-IzquierdoA. L., SummaryE., JiangY., … JarvenpääM. (2022). The ball of wax we call HR analytics. Journal of Organizational Effectiveness, 20(2), 145–156. https://doi.org/10.1108/MRR-04-2016-0084
65.
VermaS., RanaN., & MeherJ. R. (2024). Identifying the enablers of HR digitalization and HR analytics using ISM and MICMAC analysis. International Journal of Organizational Analysis, 32(3), 504–521. https://doi.org/10.1108/IJOA-01-2023-3611
66.
WangL., ZhouY., SandersK., MarlerJ. H., & ZouY. (2024). Determinants of effective HR analytics implementation: An in-depth review and a dynamic framework for future research. Journal of Business Research, 170(October2023), 114312. https://doi.org/10.1016/j.jbusres.2023.114312
67.
YooJ. W., ParkJ., & ParkH. (2024). The impact of AI-enabled CRM systems on organizational competitive advantage: A mixed-method approach using BERTopic and PLS-SEM. Heliyon, 10(16), e36392. https://doi.org/10.1016/j.heliyon.2024.e36392
68.
YoonS. W., HanS. H., & ChaeC. (2024). People analytics and human resource development: Research landscape and future needs based on bibliometrics and scoping review. Human Resource Development Review, 23(1), 30–57. https://doi.org/10.1177/15344843231209362
69.
YusufM., DasawatyE. S., EsraM. A., ApriwenniP., MeidenC., & FahleviM. (2024). Integrated reporting, corporate governance, and financial sustainability in Islamic banking. Uncertain Supply Chain Management, 12(1), 273–290. https://doi.org/10.5267/j.uscm.2023.9.022
70.
ZhaoX., LiX., LiY., & WangZ. (2024). The impact of digital transformation on firm performance. Industrial Management and Data Systems, 124(8), 2567–2587. https://doi.org/10.1108/IMDS-09-2023-0661
71.
ZhouY., WangL., SandersK., MarlerJ., & ZouY. (2022). Determinants of effective HR analytics implementation: An in-depth review and a dynamic model for future research. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4034254
