AI and Big Data in Project Risk and Quality Management: Opportunities and Challenges in Pakistan Article Swipe
Muhammad Zubair Mumtaz
,
Samina Gul
,
A ARSHAD
,
Muhammad Saeed
,
Shahid Farooq
,
Muhammad Shahid
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.63075/ha3sns55
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.63075/ha3sns55
Project Risk and Quality Management (PRQM) has become more complicated with the changing technological environment and the dynamic character of contemporary industries. In Pakistan's industrial economy, organizations encounter more challenges in managing project-related risks and quality outcomes. This research explores the impact of technological readiness on the adoption of advanced data-driven tools into PRQM practices and the influence of organizational support levels on this association. Quantitative research design was employed with data gathered among project managers, IT experts, and quality management staff in major industry sectors including manufacturing, construction, energy, and textiles. Two central variables—technological readiness and the use of data-intensive technologies for PRQM—were investigated in conjunction with the moderating influence of organizational support. Regression analysis findings identify a strong positive impact of technological readiness on the use of such tools. In addition, the role of solid organizational support was discovered to strengthen this association, indicating that infrastructure is not enough without managerial and strategic support. The findings highlight the need for the fusion of digital capability and institutional commitment to reinforce risk management and quality assurance processes. The research concludes with workable recommendations for policy, investment, and leadership involvement in enhancing technological uptake in industrial projects in Pakistan. Key Words: Quality Management, Project Risk, Technological environmental
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- Language
- en
- Landing Page
- https://doi.org/10.63075/ha3sns55
- OA Status
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- Cited By
- 1
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4410600595Canonical identifier for this work in OpenAlex
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https://doi.org/10.63075/ha3sns55Digital Object Identifier
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AI and Big Data in Project Risk and Quality Management: Opportunities and Challenges in PakistanWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-05-21Full publication date if available
- Authors
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Muhammad Zubair Mumtaz, Samina Gul, A ARSHAD, Muhammad Saeed, Shahid Farooq, Muhammad ShahidList of authors in order
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https://doi.org/10.63075/ha3sns55Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.63075/ha3sns55Direct OA link when available
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Big data, Quality (philosophy), Data quality, Risk management, Business, Quality management, Data science, Computer science, Engineering, Operations management, Marketing, Data mining, Management system, Finance, Epistemology, Metric (unit), PhilosophyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.complicated | 9 |
| abstract_inverted_index.conjunction | 106 |
| abstract_inverted_index.data-driven | 50 |
| abstract_inverted_index.environment | 14 |
| abstract_inverted_index.industries. | 21 |
| abstract_inverted_index.investment, | 186 |
| abstract_inverted_index.involvement | 189 |
| abstract_inverted_index.Quantitative | 65 |
| abstract_inverted_index.association, | 144 |
| abstract_inverted_index.association. | 64 |
| abstract_inverted_index.contemporary | 20 |
| abstract_inverted_index.investigated | 104 |
| abstract_inverted_index.technologies | 101 |
| abstract_inverted_index.Technological | 205 |
| abstract_inverted_index.construction, | 88 |
| abstract_inverted_index.environmental | 206 |
| abstract_inverted_index.institutional | 168 |
| abstract_inverted_index.organizations | 26 |
| abstract_inverted_index.technological | 13, 43, 123, 192 |
| abstract_inverted_index.data-intensive | 100 |
| abstract_inverted_index.infrastructure | 147 |
| abstract_inverted_index.manufacturing, | 87 |
| abstract_inverted_index.organizational | 59, 112, 137 |
| abstract_inverted_index.project-related | 32 |
| abstract_inverted_index.recommendations | 183 |
| abstract_inverted_index.variables—technological | 94 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.92591653 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |