Digital platform capabilities and agility: The role of technological uncertainty in SMEs

Abstract

The research aims to explore the impact of digital platform integration and reconstruction capabilities on organizational agility (mediator) and ESG performance of small and medium size enterprises of Pakistan. The study also examines the moderating role of technology uncertainty between digital platform capabilities and organizational agility that further leads to ESG performance. Data is collected from 300 firms while industries include; IT, Manufacturing, Textiles, Electronics, and Automobile in eight big cities of Pakistan and the respondents are middle and senior managers. Data was analyzed using the software; SPSS (reliability analysis) and R Studio (version 4.5.2), Structural Equation modeling (SEM) and regression Analysis. The study used Resource-Based View and Dynamic Capability Theory to see the role of digital platform capabilities, organization agility and uncertain technology conditions’ on ESG. The findings of this research are that such organizations with agility perform better on the ESG dimensions. First, this study takes a capability perspective to explore the impact of digital platform integration and reconstruction capability on Organizational agility, which extends the related studies taking digital perspective. Second, the inner mechanism between organizational agility and firm ESG performance is revealed where organizational agility works as a bridge for ESG performance. Third, the relationship between digital platform capabilities and organizational agility is exposed by the moderation of technology uncertainty. Where technology uncertainty positively moderates the relationship between digital platform capabilities and organizational agility.

Keywords: {'name': 'Digital Platform Integration Capabilities (DPIC), Digital Platform Reconstruction Capabilities (DPRC), Technological Uncertainty (TU), Organizational Agility (OA), Environmental, Social, and Governance performance (ESG Performance)'}

Authors & affiliations

Saba Hafeez
Huazhong University of Science and Technology

Ding Xiu-Hao
Huazhong University of Science and Technology

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.1007/s10668-023-02921-x

DOI: 10.1016/j.techfore.2022.121513

DOI: 10.1016/j.joitmc.2024.100266

DOI: 10.1108/CR-05-2021-0081

DOI: 10.1016/j.techfore.2021.121423

DOI: 10.1002/mde.4337

DOI: 10.1016/j.jclepro.2021.125791

DOI: 10.1016/j.techfore.2023.122588

DOI: 10.1080/23311975.2025.2564919

DOI: 10.1016/j.frl.2020.101716

DOI: 10.1016/j.jclepro.2023.137493

DOI: 10.1016/j.jbusres.2019.03.035

DOI: 10.1016/j.techfore.2021.121383

DOI: 10.1016/j.ijpe.2019.107599

DOI: 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E

DOI: 10.1007/s10668-025-06097-4

DOI: 10.1016/j.gfj.2017.03.001

DOI: 10.1016/j.indmarman.2025.11.004

DOI: 10.1002/bsd2.70239

DOI: 10.1108/SEF-09-2024-0571

DOI: 10.1016/j.jclepro.2017.02.180

DOI: 10.1016/j.jbusres.2023.113868

Hair, J. F. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.

Hamdi, S., Silong, A. D., Rasdi, R. M., & Omar, Z. B. (2016). Moderating effect of technology uncertainty on the relationship between innovation speed and product success: A survey in Malaysian biotechnology industry. International Journal of Business and Management, 11(6), 134–143. https://doi.org/10.5539/ijbm.v11n6p134

DOI: 10.1002/bse.2943

DOI: 10.1002/sd.70048

DOI: 10.1016/j.frl.2023.104653

DOI: 10.1016/j.daach.2021.e00182

DOI: 10.1016/j.jbusres.2018.01.058

DOI: 10.1016/j.ijpe.2024.109434

DOI: 10.1371/journal.pone.0301864

DOI: 10.1108/EJIM-10-2020-0422

DOI: 10.1007/s11747-007-0032-6

DOI: 10.1108/IJOEM-01-2025-0200

DOI: 10.1016/j.ijpe.2023.109124

DOI: 10.1108/JSTP-04-2022-0092

DOI: 10.1080/00036846.2024.2385755

DOI: 10.9770/jesi.2021.9.1(18)

DOI: 10.1016/j.im.2021.103434

Guizani, M., Al-Fuqaha, A., Mohammadi, M., Aledhari, M., & Ayyash, M. (2020). The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions. IEEE Internet of Things Journal, 7(12), 11963–11978. https://doi.org/10.1109/JIOT.2020.3017237

DOI: 10.1108/JABS-09-2022-0309

DOI: 10.1007/s11846-024-00768-8

DOI: 10.1037/0021-9010.88.5.879

DOI: 10.1108/IJOPM-11-2021-0708

DOI: 10.1108/JKM-06-2021-0453

DOI: 10.1016/j.jik.2022.100199

DOI: 10.1002/sej.1493

DOI: 10.1016/j.techfore.2022.121562

DOI: 10.1111/isj.12450

DOI: 10.1016/j.techfore.2023.122989

DOI: 10.3390/su17188517

DOI: 10.2307/23044052

DOI: 10.1525/cmr.2016.58.4.13

DOI: 10.17705/1CAIS.03913

DOI: 10.1016/j.jsis.2019.01.003

DOI: 10.1016/j.techfore.2022.121960

DOI: 10.1108/08858621211221689

DOI: 10.1016/j.jclepro.2023.137980

DOI: 10.1007/s41471-023-00163-1

DOI: 10.1016/j.ijinfomgt.2022.102513

DOI: 10.1007/s11356-022-20789-8

DOI: 10.1109/ACCESS.2025.3542575

DOI: 10.3390/su151411304

DOI: 10.4018/JGIM.335905