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Global Research journal of Natural Science  
& Technology (GRJNST)  
Volume: 04 - Issue 4 (2026), 2110  
ISSN P: 2790-7643 ISSN E: 2790-7651  
www.grjnst.net  
https://doi.org/10.53762/grjnst.04.04.01  
AI Capability and Performance: The Strategic Roles of Cyber Risk  
Management Firm and Knowledge Management Systems in Pakistan Halal  
Foods  
Received: 14 April 2026. Accepted: 16 May 2026. Published: 19 June 2026  
Ghulam Zara  
Abasyn University Islamabad Campus  
zara.zafar152@gmail.com  
https://orcid.org/0009-0009-3801-8461  
Hassan Raza Ahsan  
Federal Urdu University of Arts,  
Science & Technology, Islamabad, Pakistan  
hassan_raza325@yahoo.com  
https://orcid.org/0009-0006-1259-0660  
GRJNST, Volume: 04 - Issue 4 (2026) / ISSN P: 2790-7643  
Article ID: 2110  
https://doi.org/10.53762/grjnst.04.04.01  
Copyright © 2026 GRJNST. This article is published under an Open Access model. It is made available to the public under the terms of the Creative  
Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use and distribution  
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Abstract  
The fast development of digital technology has changed the way organizations  
operate and increased the importance of artificial intelligence capabilities, cyber  
risk management and knowledge management systems, in enhancing business  
performance. The present study examined the impact of artificial intelligence  
capabilities on organizational performance, and the mediating role of cyber risk  
management and knowledge management systems in halal food manufacturing  
enterprises in Pakistan. The study is based on the Resource-Based View, the  
Theory of Dynamic Capabilities and the Knowledge-Based View. The study has  
adopted a quantitative research methodology with a cross-sectional survey  
design. Data was collected from 300 management workers working in halal  
food production companies in Pakistan using a standardised questionnaire. The  
collected data were analyzed using SPSS and SmartPLS. Results show that  
artificial intelligence capabilities have a significant positive effect on corporate  
performance, cyber risk management and knowledge management systems. In  
addition, the cyber risk management and knowledge management systems  
contribute greatly to the performance of the organization. The results also  
reveal that the proposed model possesses adequate explanatory power, predictive  
relevance, reliability and validity. The study contributes to the existing literature  
on digital transformation and organizational performance by providing real  
findings from the halal food manufacturing industry of Pakistan. The results  
suggest that organizations should spend money on AI technology, cybersecurity  
measures and knowledge management systems to enhance operational efficiency,  
organizational resilience and sustained competitive advantage.  
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Keywords: AI Capability, Cyber Risk Management, Knowledge Management  
System, Firm Performance  
Introduction  
The fast development of digital technologies has changed the operational environment  
of today’s organizations, especially of the manufacturing industry. In recent years,  
organizations have increasingly adopted artificial intelligence (AI), cybersecurity  
measures, and knowledge management systems to enhance operational efficiency and  
organizational competitiveness (Olan et al., 2022; Leoni et al., 2022). The development  
of industry 4.0 has made easier the application of intelligent technology in the business  
processes. This gives organizations a chance to automate operations, improve resource  
allocation and increase strategic performance. Among these technical breakthroughs, AI  
competence has become a critical organizational resource to improve productivity and  
competitiveness (Shao et al. 2026). Neiroukh et al., (2025) also showed that  
organizations with high AI capabilities have better speed of decision making, operational  
efficiency and organizational performance.  
Furthermore, artificial intelligence competence refers to an organization's ability to  
successfully implement, integrate, and use artificial intelligence technology,  
infrastructure, technical knowledge, and analytical abilities in order to achieve strategic  
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goals (Mikalef et al., 2023). In addition, solutions that are powered by artificial  
intelligence improve predictive analytics, automate processes, provide intelligent  
forecasting, and monitor customer behavior, all of which contribute to an increase in  
organizational efficiency. According to Islam et al. (2024), artificial intelligence  
technologies are increasingly used in the manufacturing sector to improve production  
quality, inventory management, supply chain operations, and product innovation. The  
growing use of artificial intelligence has had a significant influence on businesses  
operating in specialized environments, such as the manufacture of halal products, where  
compliance with laws, quality control, operational transparency, and consumer trust are  
of the utmost importance.  
Although there are strategic benefits to using AI, companies are also facing increasing  
security dangers and digital vulnerabilities. The digital revolution in industrial processes  
has heightened the organizational dependence on interrelated technologies, cloud  
infrastructures, and data-driven operations, which, in turn, increases the susceptibility to  
cyber-attacks (Areghan, 2025). Vidović et al. (2025) mention that cybersecurity  
incidents, data breaches, ransomware attacks, and operational disruptions may have a  
considerable influence on firm reputation, financial stability and customer confidence.  
As a result, managing cyber risk has emerged as a vital organizational capability to  
protect digital assets, ensure business continuity, and enhance organizational resilience.  
Research like Kure et al. (2018) and Fernando et al. (2023) suggests that effective  
management of cyber risks provides a substantial contribution to improve company  
performance via the reduction of operational uncertainty and the improvement of  
information system reliability.  
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Cyber risk management is the process of identifying, assessing, monitoring, and  
managing cybersecurity threats that might influence the organization’s operations  
(Parsola, 2022). Organizations with excellent cybersecurity management systems are  
more likely to preserve intellectual property, ensure customer data safety, and maintain  
stakeholder confidence in digital, linked business environments. Cyber risk management  
is equally important in halal food production as integrated supply chain systems, digital  
certification procedures and online transaction platforms depend on secure information  
management and operational integrity. Poor management of cyber risks leads to poor  
corporate performance (Okoye, 2017), disruption of business continuity and loss of  
client trust in halal products.  
Besides AI capabilities and cyber risk management, knowledge management systems  
(KMS) have become an important organizational resource that significantly affects  
corporate performance. Knowledge management systems are the technological and  
organizational structures used to create, store, distribute, transmit and apply knowledge  
within enterprises (Mohammad et al., 2024). Organizational knowledge is recognised as  
a significant intangible asset in information-dense, technology-driven organizational  
settings, enhancing inventive ability, operational efficiency and strategic flexibility.  
Knowledge management systems (KMS) are able to support organizational learning,  
assist in decision-making, and develop employee collaboration, and finally affect  
organizational performance (Jia et al., 2024). Recent empirical studies have shown that  
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knowledge management systems are important for enhancing organizational  
productivity, innovativeness, and performance (Lai et al., 2022). Knowledge  
management systems help manufacturing organisations effectively gather and use critical  
company knowledge to optimise processes, improve quality, foster product innovation,  
and make strategic decisions. Halal manufacturing companies utilize knowledge  
management systems to improve compliance with halal principles, product consistency  
and best practices in operations.  
Prior research has studied the effects of AI capability, cyber risk management and  
knowledge management systems on organizational outcomes separately, but there is a  
paucity of research on these factors altogether in a comprehensive framework, especially  
in the context of halal product manufacturing companies. The present study is based on  
general manufacturing industries, service organizations and technical companies,  
although factual data from halal manufacturing sectors are scarce. Additionally, previous  
studies have mostly concentrated on the direct relationships between technology  
capabilities and business performance, overlooking the interdependent roles of AI  
capability, cybersecurity management, and knowledge management systems in boosting  
organizational performance.  
Literature review  
Theoretical Foundation  
The study is mainly based on the Resource-Based View (RBV) which argues that  
companies may achieve sustained competitive advantage and superior performance via  
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the efficient use of valuable, rare, distinctive and non-substitutable organizational  
resources (Barney, 1991). AI capabilities, cyber risk management and knowledge  
management systems are seen as important organizational assets for the contemporary  
digital corporation to boost operational efficiency, innovation potential, organizational  
resilience and decision making quality. The RBV has clearly defined the importance of  
technical and administrative capabilities in boosting the performance of halal product  
production firms.  
In addition, the study is supported by the Dynamic Capabilities Theory which defines  
the ability of the organization to integrate, reconfigure and transform internal  
competences to meet the changing technology and market circumstances (Teece et al.,  
1997). AI capabilities may increase digital transformation and operational intelligence.  
Cyber risk management can improve the organization's resistance to cybersecurity threats  
and knowledge management systems can support organizational learning and knowledge  
utilization. Recent studies like Zheng (2024) reveal that organizations with  
sophisticated digital capabilities and competent knowledge-based resources display more  
innovation, strategic agility, and economic success. Thus, the integration of Resource-  
Based View and Dynamic Capabilities Theory provides a strong theoretical foundation  
to understand the combined effect of AI capability, cyber risk management and  
knowledge management systems on organizational performance in the halal  
manufacturing industry.  
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Hypothesis development  
The effectiveness of artificial intelligence has turned into kind of a vital organizational  
asset that helps companies boost operational efficiency, sharpen strategic choices ,  
encourage innovation and get a clearer edge over competitors. AI competency is basically  
how capable an organization is at rolling out, embedding, and putting AI technologies  
into practice, including the infrastructure, technical know-how, and analytical skills  
needed to push corporate objectives forward and create measurable value (Mikalef et al.,  
2023). As AI technology keeps spreading through manufacturing, it has shifted  
ordinary, routine operating procedures into more intelligent, data oriented systems ,  
which in turn can lift organizational efficiency and overall performance. If you look at it  
through the Resource-Based View (RBV), AI capacity is treated as a strategic  
organizational asset that supports long lasting competitive advantage and better business  
outcomes. The Resource-Based View suggests that firms with valuable resources and  
hard-to-duplicate technical skills are more likely to achieve stronger results (Barney,  
1991).  
Recent empirical research does, in a general sense, strongly back up the idea that higher  
AI capacity links with stronger organizational performance. Neiroukh et al. (2025)  
showed that AI capabilities actually can improve organizational performance not just “a  
little” but noticeably, mainly because decision making gets more efficient and the  
organization becomes more strategically responsive, in practice. Wahyudi et al. (2026)  
noted the same pattern too, companies that use AI technology tend to see higher  
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operational efficiency , stronger innovation performance, and better organizational  
competitiveness. In industrial contexts, AI capabilities support process automation ,  
predictive maintenance, quality control, and even supply chain optimisation, so overall  
organizational performance tends to rise. And for specialized sectors, like halal product  
manufacturing, the potential advantage can be even more meaningful since intelligent  
systems can help with halal compliance monitoring, raise operational transparency, and  
boost production efficiency. So in the end, firms that build up stronger AI capabilities  
are expected to reach improved organizational performance, relatively faster perhaps,  
depending on how they implement it.  
H1: Artificial intelligence capability has a positive and significant relationship with firm  
performance.  
The swift digital shift within firms has, sort of, increased their exposure to cybersecurity  
attacks, so cyber risk management has become a serious corporate matter. Cyber risk  
management, in general, is about identifying, assessing, monitoring, and mitigating  
cybersecurity threats that could disrupt organizational systems, digital infrastructure, and  
day-to-day operational continuity (Parsola, 2022). At the same time, artificial  
intelligence has become a key technical enabler, strengthening corporate cybersecurity  
readiness and the overall performance of cyber risk management. Dynamic Capabilities  
Theory suggests that companies with stronger technical expertise can deal more  
effectively with environmental uncertainty and digital hazards, because they stay engaged  
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in ongoing adaptation and innovation (Teece et al., 2016). In practice, AI capabilities let  
organizations employ machine learning methods, predictive analytics, vigilant  
monitoring tools, and automated threat detection approaches, which all together  
improve cybersecurity management and organizational resilience.  
Recent studies show that AI technologies can significantly improve cybersecurity  
operations, in that anomaly detection improves, cyber threat identification becomes  
more precise, and incident response speeds up. Ravikumar (2025) argued that AI-  
enhanced cybersecurity approaches make it easier for an organisation to spot and  
respond to complex cyber threats in real time. Likewise, Mızrak (2023) found that  
companies relying on intelligent digital systems tend to improve their cybersecurity  
readiness and overall ability to mitigate risk. In practice, AI-driven cybersecurity systems  
can sift through vast amounts of organizational data, detect anomalous or out-of-pattern  
behaviour, and even surface likely vulnerabilities before real operational disruption  
occurs. In industrial settings, digitalisation, along with integrated operational  
technologies, can exacerbate cybersecurity vulnerabilities, especially those related to  
cloud services, IoT devices, and supply chain networks. Halal product manufacturing  
firms are increasingly relying on digital certification systems, production monitoring  
platforms, and interconnected information systems, making the need for AI-enhanced  
cybersecurity measures more urgent. So overall, organizations that have stronger AI  
capabilities tend to build more solid cyber risk management frameworks and, in turn,  
improve cybersecurity resilience.  
H2: Artificial intelligence capability has a positive and significant relationship with cyber  
risk management.  
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Knowledge management systems (KMS) are basically organizational frameworks meant  
to improve the generation, storage, exchange, transfer, and practical use of organizational  
information (Kumar & Gupta, 2012). In this digital age, AI capabilities have become a  
key technical asset, as they strengthen corporate knowledge management by accelerating  
information processing, enabling advanced analytics, and supporting learning  
mechanisms. How exactly AI capabilities tie in with knowledge management systems can  
be understood through the Knowledge-Based View (KBV), because it argues that  
information is a critically crucial organizational resource for achieving ongoing  
competitive advantage. With AI capabilities in place, an organization can better collect,  
evaluate, structure and distribute useful information among its divisions, which then  
really helps knowledge generation and knowledge application (Olan et al., 2023).  
Recent research suggests that AI technologies substantially improve corporate learning  
and knowledge-sharing activities. Gao et al. (2025) found that AI capabilities help  
strengthen knowledge integration, boost innovation ability, and support strategic  
decision making, mainly because they make intelligent information handling easier while  
also encouraging organizational cooperation. In a similar way, Yu et al. (2017) reported  
that technical capabilities have a positive effect on knowledge management effectiveness  
and organizational learning results. AI-driven solutions offer smart data mining,  
automatic knowledge categorisation, and near-instant information distribution, so,  
overall, they improve how organisations apply knowledge and how smoothly operations  
run. In industrial settings, AI-enhanced knowledge systems can improve product  
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development, reinforce operational standardisation, strengthen quality management, and  
optimise processes. Halal product manufacturing companies may benefit in particular, as  
these tools make it easier to share halal compliance knowledge, increase operational  
visibility, and help organisations understand the requirements of halal manufacturing.  
So, companies that already have strong AI capabilities are expected to build more  
efficient knowledge management systems.  
H3: Artificial intelligence capability has a positive and significant relationship with  
knowledge management systems.  
Cyber risk management has become paramount for firms operating in technology-led,  
digitally interconnected business spaces. Ahmad et al. (2021) argue that as enterprises  
continue to adopt digital technologies, cloud computing systems, and web-based  
operational platforms, their exposure to cyberattacks and information security  
vulnerabilities has increased noticeably. Also, having solid cyber risk management helps a  
firm not only spot likely cyber threats but also guard digital assets, sustain day-to-day  
operations, and strengthen overall organizational resilience. When you look at it through  
the Resource-Based View, cyber risk management is treated as an essential internal  
capability, as it protects strategic resources and supports long-term company  
performance. Firms with strong cybersecurity management systems are typically better at  
reducing the likelihood of operational stoppages, mitigating financial harm, and  
minimising reputational damage from cyberattacks or data breaches.  
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Recent empirical studies back up, not just hint at, a positive link between cyber risk  
management and overall organizational results. Sukachova et al. (2025) found that firms  
with strong cybersecurity capabilities, kind of like a resilient shield, tend to show  
stronger organizational resilience and also better commercial outcomes. Althonayan and  
Andronache (2019) suggested that when enterprise risk management and cybersecurity  
measures work together, the organisation’s sustainability improves significantly, as well  
as operational continuity and even strategic effectiveness. Effective cyber risk  
management builds stakeholder trust, strengthens consumer confidence, and supports  
long-term organizational stability. Cybersecurity management is increasingly necessary  
for manufacturing firms because production systems, supply chains, and operational  
technologies increasingly rely on interconnected digital infrastructure. For halal product  
manufacturing organizations, the stakes are even higher: cyber risk management is crucial  
so companies can protect halal certification documents, customer information, and  
supply chain traceability platforms. If there’s a cybersecurity incident, operational  
dependability can be harmed, and customer confidence in halal goods may drop. So, in  
the end, companies that implement efficient cyber risk management techniques are more  
likely to reach improved performance outcomes.  
H4: Cyber risk management has a positive and significant relationship with firm  
performance.  
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Knowledge management systems are widely regarded as strategic organizational tools  
that help organisations increase innovation capacity, enhance organizational learning,  
and support corporate success (Migdadi, 2022). In a similar vein, knowledge  
management systems (KMS) help with the collection, storage, circulation, and  
application of organizational information. Because of that organizations can improve the  
quality of decisions, raise operational efficiency, and gain a bit more strategic flexibility.  
The knowledge-based view argues that organizational knowledge is a major intangible  
asset that can strengthen competitive advantage and produce better organizational  
outcomes (Osobajo & Bjeirmi, 2021). When knowledge management systems are run  
well, they can also expand organizational learning, strengthen employee collaboration,  
and encourage innovative activities that, in practice, benefit firm performance. More  
recent empirical work also points to a clear and positive effect of KMS on  
organizational performance. Alrubaiee et al. (2015) found that knowledge management  
practices significantly improve operational efficiency, innovative capacity, and overall  
organizational productivity. Likewise, Rialti et al. (2020) showed that firms employing  
effective knowledge management systems tend to display stronger innovation  
performance, higher strategic adaptability, and better organizational competitiveness.  
Also, knowledge-sharing approaches let firms leverage human know-how in a more  
efficient way, reduce operational inefficiencies, and improve how quickly the  
organization responds to shifts in the environment.  
In manufacturing contexts, Knowledge Management Systems help with process  
improvements, better quality outcomes, and product novelty, through more efficient  
knowledge sharing and a kind of collective organizational learning. In halal product  
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manufacturing companies, it is especially important to have knowledge management  
systems that work well, so they can preserve halal compliance, maintain strong quality  
assurance protocols, and ensure operational consistency across departments. When  
knowledge management is done efficiently, it also supports staff coaching, compliance  
awareness, and ongoing refinement tasks in halal industrial settings. So, organizations  
that have effective knowledge management systems are more apt to reach stronger  
operational results and better financial performance. Based on that, researchers put  
forward the following hypothesis.  
H5: Knowledge management systems have a positive and significant relationship with  
firm performance.  
Figure 1: Research model  
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Research Methodology  
Research Design  
This research employs a quantitative methodology to investigate the influence of  
artificial intelligence capabilities, cyber risk management, and knowledge management  
systems on the performance of halal food manufacturing firms in Pakistan. Quantitative  
research is considered suitable because it enables researchers to objectively investigate  
correlations among variables through statistical analysis and hypothesis testing (Lim,  
2025). Furthermore, the research employed a cross-sectional survey design, collecting  
data from participants at a single point in time. Moreover, the cross-sectional method is  
extensively used in organizational and management research since it allows for the  
efficient and systematic analysis of causal linkages among variables (Hunziker &  
Blankenagel, 2024).  
Target population  
The study's population of interest comprises halal food manufacturing companies  
operating in Pakistan. Similarly, the unit of study comprises managers in halal food  
manufacturing firms, since they hold substantial knowledge about organizational  
technical capabilities, cybersecurity procedures, knowledge management systems, and  
business performance. The participants include operations managers, production  
managers, IT managers, supply chain managers, quality assurance managers, and senior  
executives engaged in organizational decision-making processes. In addition, Pakistan is  
a significant setting for this research, as the halal food production sector has expanded  
considerably due to rising local and global demand for halal-certified goods. The  
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escalating digital transformation of industrial processes in Pakistan has heightened the  
significance of AI capabilities, cybersecurity management, and knowledge management  
systems in enhancing organizational performance.  
Sampling Technique and Sample Size  
The research employed a purposive sampling method to select participants from halal  
food production companies in Pakistan. A purposive sample is deemed suitable, as the  
research focuses on administrative personnel with relevant organizational expertise and  
experience in artificial intelligence, cybersecurity procedures, and knowledge  
management systems. The recommended sample size for structural equation modeling  
(SEM) investigations typically varies between 200 and 400 participants. This research  
aims to gather data from approximately 300 management respondents, following the  
guidelines of Hair et al. (2022), to ensure statistical reliability and sufficient  
representation of the target population.  
Data Collection Method  
Primary data were obtained using a standardised questionnaire sent to the management  
of halal food production companies in Pakistan. The questionnaire was constructed  
using validated measurement scales derived from earlier research. Data collection was  
executed using both online and physical survey methodologies to enhance response rates  
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and accessibility. A pilot study was performed with a small sample of respondents prior  
to releasing the questionnaire to assess the clarity, reliability, and validity of the survey  
instrument. Essential improvements were made in response to participant comments to  
enhance the clarity and pertinence of the questionnaire questions.  
Constructs Measurement  
The measuring scales employed in this study were derived from previously validated  
research to confirm the reliability and validity of the variables. The research assessed  
four primary variables: artificial intelligence capabilities, cyber risk management,  
knowledge management systems, and organizational performance. Each construct was  
assessed using five items adapted from existing literature to align with the setting of halal  
food manufacturing firms in Pakistan. All items were evaluated on a five-point Likert  
scale, with 1 denoting strong disagreement and 5 indicating strong agreement. The  
competency of artificial intelligence was assessed using five elements derived from  
Obenza et al. (2024). Cyber risk management was assessed by five items derived from  
Siyaya et al. (2025), focusing on cyber threat detection. Furthermore, knowledge  
management systems were assessed using five items derived from Ma et al. (2025).  
Moreover, firm performance was evaluated using five factors derived from Atobishi et al.  
(2024). The modification of existing scales from previous studies improves the content  
validity and reliability of the measuring instrument employed in the present research.  
Data analysis and results  
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The gathered data were examined using SPSS and SmartPLS. SPSS facilitated  
preliminary data screening, descriptive statistics, demographic analysis, evaluation of  
missing values, and reliability assessment. SmartPLS was used to do Partial Least  
Squares Structural Equation Modeling (PLS-SEM) for hypothesis testing and structural  
model assessment. PLS-SEM was chosen because it is well-suited to predictive and  
exploratory research models involving numerous latent dimensions and intricate  
interactions. Moreover, PLS-SEM is suitable for research incorporating managerial and  
behavioral dimensions and does not need stringent assumptions about data normality.  
Sample Characteristics  
The demographic details of the participants in this research are shown in Table 1. The  
study was completed by 300 management staff members from Pakistani halal food  
production companies. According to the demographic analysis, male respondents  
comprised 72.7% (n = 218) of the sample, while female respondents comprised 27.3%  
(n = 82). The management makeup often seen in Pakistan's manufacturing industry is  
reflected in this distribution. In terms of age distribution, the majority of respondents  
were between 31 and 35, comprising 31.3% (n = 94) of the sample. Respondents  
between the ages of 36 and 40 made up 29.3% (n = 88). 18.7% (n = 56) of  
respondents were between the ages of 25 and 30, while 20.7% (n = 62) were above the  
age of 41. The findings show that the respondents had sufficient administrative  
experience and professional maturity regarding the variables under study.  
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Regarding educational background, 54.0% (n = 162) of respondents held a master's  
degree, while 30.3% (n = 91) held a bachelor's degree. Just 4.0% (n = 12) of  
respondents had PhD degrees, while 11.7% (n = 35) had MPhil/MS degrees. The  
interviewees' educational backgrounds indicate that they were professionally competent  
and had sufficient academic understanding of technical and management  
procedures. Production managers made up the largest category of management positions  
(24.7%, n = 74), followed by operations managers (22.7%, n = 68). IT managers made  
up 16.3% (n = 49), supply chain managers 18.7% (n = 56), and quality assurance  
managers 17.6% (n = 53). Respondents from several management departments were  
included, which improved the data's dependability and comprehensiveness.  
According to the work experience analysis, 38.7% (n = 116) of the respondents had 6  
to 10 years of professional experience, while 24.3% (n = 73) had 11 to 15 years of  
experience. 23.7% (n = 71) of participants had one to five years of experience, while  
13.3% (n = 40) had more than fifteen years of experience. This distribution shows that  
most respondents had significant industrial experience related to company performance  
and organizational technical capabilities. Lastly, in terms of company size, the largest  
percentage of participating companies was medium-sized businesses (45.7%; n = 137),  
followed by big businesses (28.3%; n = 85) and small businesses (26.0%; n = 78). The  
study's results are more broadly applicable to Pakistan's halal food production industry  
thanks to the involvement of companies with varying organizational sizes.  
Table 1: Respondents’ data  
Demographic  
Category  
Frequency  
Percentage  
Total  
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Variable  
Gender  
(n)  
218  
82  
56  
94  
88  
62  
91  
162  
35  
12  
68  
74  
49  
56  
(%)  
72.7  
27.3  
18.7  
31.3  
29.3  
20.7  
30.3  
54.0  
11.7  
4.0  
Male  
100  
100  
Female  
Age  
25–30 Years  
31–35 Years  
36–40 Years  
41 Years and Above  
Bachelor’s Degree  
Master’s Degree  
MPhil/MS  
PhD  
Educational  
Qualification  
100  
100  
Managerial Position Operations Manager  
Production Manager  
22.7  
24.7  
16.3  
18.7  
IT Manager  
Supply Chain  
Manager  
Quality Assurance  
Manager  
53  
17.6  
Work Experience  
1–5 Years  
71  
23.7  
38.7  
24.3  
100  
6–10 Years  
11–15 Years  
116  
73  
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Above 15 Years  
Small Enterprise  
Medium Enterprise  
Large Enterprise  
40  
13.3  
26.0  
45.7  
28.3  
Type of Firm  
78  
100  
137  
85  
Measurement model  
The measurement model was tested to check the reliability and validity of the constructs  
employed in this research, i.e. artificial intelligence capabilities, cyber risk management,  
knowledge management systems, and business performance. The reflective measurement  
model was assessed using SmartPLS, following the suggestions of Hair et al. (2022).  
The indicator's dependability was assessed using factor loadings, and all item loadings  
exceeded the suggested cut-off of 0.70, indicating adequate reliability. Cronbach’s alpha  
(CA) and composite reliability (CR) were used to assess internal consistency. The  
results indicated that all constructs were above the acceptable threshold of 0.70,  
indicating high internal consistency. Convergent validity was tested using average  
variance extracted (AVE). AVE values for all constructs were over 0.50, indicating that  
the constructs satisfactorily explained the variance in their indicators. Additionally, the  
Fornell–Larcker criteria and the Heterotrait–Monotrait ratio (HTMT) were used to  
test discriminant validity. The findings showed that the square root of each construct's  
AVE exceeded its correlations with other constructs, and HTMT values were below the  
required threshold of 0.85, indicating appropriate discriminant validity for the  
constructs. The results indicate that the measurement model has sufficient reliability and  
validity and is adequate for future examination of the structural model and hypothesis  
testing (Henseler et al., 2016).  
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Construct the reliability and validity of the study  
We checked construct reliability and validity to verify the consistency, accuracy, and  
sufficiency of the measuring scales employed in the present study. The reliability of the  
constructs was examined using Cronbach’s alpha (CA) and composite reliability (CR).  
All constructs’ values were above the required threshold value of 0.70, suggesting  
excellent internal consistency reliability. CR values ranging between 0.70 and 0.95  
indicate adequate construct reliability in PLS-SEM (Hair et al., 2022). Convergent  
validity was tested using factor loadings and average variance extracted (AVE). All  
indicator loadings exceeded 0.70, and all AVE values exceeded 0.50, indicating that the  
constructs explained more than 50% of the variance in their respective indicators.  
Table 2 shows good construct reliability and validity for all the variables in the research.  
All measurement items had factor loadings above the suggested level of 0.70, indicating  
high internal reliability. All constructs had Average Variance Extracted (AVE) values  
over 0.50, thereby demonstrating sufficient convergent validity. Also, the values of  
Composite dependability (CR) and Cronbach’s Alpha were above the acceptable value  
of 0.70, which indicates good internal consistency dependability for all constructions.  
Therefore, the measurement model has adequate reliability and validity to proceed with  
structural model analysis and hypothesis testing, as recommended by Hair et al. (2022).  
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Table 2: Constructs reliability and validity  
Constructs  
Item  
Loadings AVE  
CR  
Cronbach’s  
Codes  
Alpha  
AI Capability  
AIC1  
0.812  
0.845  
0.836  
0.801  
0.824  
0.821  
0.843  
0.798  
0.815  
0.822  
0.832  
0.851  
0.814  
0.681 0.914  
0.667 0.909  
0.689 0.917  
0.883  
AIC2  
AIC3  
AIC4  
AIC5  
Cyber Risk Management  
CRM1  
CRM2  
CRM3  
CRM4  
CRM5  
KMS1  
KMS2  
KMS3  
0.875  
Knowledge Management  
System  
0.889  
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KMS4  
KMS5  
FP1  
0.826  
0.839  
0.841  
0.857  
0.824  
0.836  
0.845  
Firm Performance  
0.702 0.922  
0.894  
FP2  
FP3  
FP4  
FP5  
Discriminant Validity  
Discriminant validity was tested to evaluate the extent to which a variable in the research  
was statistically unique from other variables. Furthermore, discriminant validity in PLS-  
SEM refers to the degree to which a construct measures phenomena not measured by  
other constructs in the model. Discriminant validity was assessed using the Fornell–  
Larcker criteria and Heterotrait–Monotrait ratio (HTMT) as recommended by Hair et  
al. (2022). The Fornell–Larcker criteria showed that the square root of the Average  
Variance Extracted (AVE) of each construct was larger than the construct’s correlations  
with other constructs, which indicates sufficient discriminant validity. Furthermore, the  
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HTMT values for all construct pairings were below the required threshold value of  
0.85, which again supports the uniqueness of the constructs. Henseler et al. (2023) have  
written that HTMT is among the most reliable methods for testing discriminant  
validity in variance-based SEM research. The results consequently demonstrate that  
artificial intelligence capabilities, cyber risk management, knowledge management  
systems and company performance are statistically different entities. The result is that  
the measurement model has good discriminant validity.  
Table 3: Discriminant Validity (Fornell–Larcker Criteria)  
Constructs  
AI  
Cyber Risk  
Knowledge  
Management  
System  
Firm  
Capability  
Management  
Performance  
AI Capability  
0.825  
Cyber Risk  
0.612  
0.817  
Management  
Knowledge  
Management  
System  
0.648  
0.701  
0.635  
0.830  
Firm Performance  
0.664  
0.678  
0.838  
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The discriminant validity of the measurement model was assessed using the Fornell–  
Larcker criteria Table 3 and the Heterotrait–Monotrait ratio (HTMT) in Table 4, as  
suggested by Hair et al. (2022) and Henseler et al. (2023). The Fornell–Larcker test  
results reveal that the square root of each construct's AVE (bolded numbers on the  
diagonal) exceeds the correlations among the constructs, supporting discriminant  
validity for artificial intelligence capability, cyber risk management, knowledge  
management systems, and firm performance. Similarly, the HTMT values for all  
pairwise construct combinations are below the threshold of 0.85, indicating that the  
constructs are empirically distinct and assess separate theoretical notions. The latest  
methodological development in PLS-SEM uses HTMT as a more conservative criterion  
for the establishment of discriminant validity, and the findings of this research clearly  
support the uniqueness of all constructs in the model.  
Table 4: Discriminant Validity (HTMT Criteria)  
Constructs  
AI  
Cyber Risk  
Knowledge  
Management  
System  
Firm  
Capability  
Management  
Performance  
AI Capability  
Cyber Risk  
0.831  
0.731  
0.778  
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Management  
Knowledge  
Management  
System  
0.754  
0.801  
0.742  
0.768  
0.790  
0.783  
Firm Performance  
—
Structural model  
The structural model was evaluated to investigate the proposed links among artificial  
intelligence capabilities, cyber risk management, knowledge management systems, and  
firm performance using SmartPLS. In accordance with the recommendations of Hair et  
al. (2021), the assessment of the structural model included analysing path coefficients  
(β), t-values, p-values, coefficient of determination (R²), and predictive relevance (Q²).  
The importance of the proposed correlations was evaluated using the bootstrapping  
method with 5,000 resamples. In PLS-SEM, path coefficients (β) signify the magnitude  
and orientation of links between constructs, with elevated positive β values indicating  
greater positive associations. Hair et al. (2021) assert that a link is deemed statistically  
significant when the t-value exceeds 1.96 at the 95% confidence level, and the p-value is  
below 0.05. Additionally, p-values < 0.01 indicate very significant correlations between  
constructs, and path coefficients (β) are -+1. The structural model findings revealed  
positive and significant correlations among the study variables, indicating that artificial  
intelligence capability positively affects cyber risk management, knowledge management  
systems, and firm performance, and that cyber risk management and knowledge  
management systems also positively affect firm performance. The coefficient of  
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determination (R²) values further demonstrated the model's efficacy in elucidating the  
variation in endogenous constructs. The results of the structural model substantiate the  
provided hypotheses and validate the appropriateness of the theoretical framework for  
elucidating company performance in halal food manufacturing enterprises in Pakistan  
(Sarstedt et al., 2014).  
The hypothesis results of this study, as shown in Table 5, demonstrate that all presented  
hypotheses are statistically significant and strongly positively confirmed. The correlation  
between artificial intelligence capabilities and firm performance (β = 0.412, t = 7.103,  
p < 0.001) indicates that AI capability substantially improves organizational outcomes.  
Likewise, AI capability demonstrates a robust and significant influence on cyber risk  
management (β = 0.468, t = 8.667, p < 0.001) and knowledge management systems (β  
= 0.501, t = 9.635, p < 0.001), suggesting that organizations with enhanced AI  
capability are inclined to exhibit superior cybersecurity and knowledge management  
practices.  
Moreover, cyber risk management has a substantial and significant influence on business  
performance (β = 0.297, t = 4.869, p < 0.001), indicating that proficient cybersecurity  
measures enhance organizational efficiency, resilience, and overall performance.  
Similarly, knowledge management systems significantly affect organizational  
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performance (β = 0.354, t = 6.211, p < 0.001), underscoring the significance of  
information exchange, storage, and application in enhancing organizational results. All t-  
values are above the essential threshold of 1.96, and all p-values are below 0.05, so  
affirming that all hypotheses are statistically significant at the 95% confidence level, in  
accordance with structural equation modelling standards (Hair et al., 2022).  
Table 5: Hypotheses results  
Hypothesis  
H1  
Paths  
STD t-  
p-  
Result  
β-  
values values  
value  
0.412 0.058 7.103 0.000 Supported  
0.468 0.054 8.667 0.000 Supported  
0.501 0.052 9.635 0.000 Supported  
AI Capability → Firm  
Performance  
H2  
AI Capability → Cyber  
Risk Management  
H3  
AI Capability →  
Knowledge Management  
System  
H4  
H5  
Cyber Risk Management  
0.297 0.061 4.869 0.000 Supported  
0.354 0.057 6.211 0.000 Supported  
→ Firm Performance  
Knowledge Management  
System → Firm  
Performance  
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Table 6 shows the coefficient of determination (R 2 ) and the predictive relevance (Q 2  
) for the endogenous constructs of the research, namely cyber risk management,  
knowledge management system, and business performance. The R2 values demonstrate  
the model’s explanatory power and the variance of the dependent constructs explained  
by the independent variable (artificial intelligence capabilities). The findings  
demonstrate that the R2 value of cyber risk management is 0.438, indicating that the  
artificial intelligence capabilities can account for 43.8% of the variation, which is a  
reasonable level of explanatory power. The knowledge management system has the same  
R2 value of 0.501, which means 50.1% of the variation is explained. This shows a good  
level of explanatory power. An R2 value of 0.627 for firm performance indicates that  
62.7% of the variation in firm performance is explained collectively by artificial  
intelligence capabilities, cyber risk management, and the knowledge management system,  
indicating the good explanatory power of the structural model.  
Table 6: R² and Q²  
Construct  
R²  
Q²  
Cyber Risk Management  
0.438  
0.501  
0.627  
0.312  
0.346  
0.401  
Knowledge Management System  
Firm Performance  
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Table 6 shows the effect size (f²) values, which assess the individual contribution of each  
exogenous construct to the endogenous variables in the structural model. The findings  
reveal that the artificial intelligence capability has a significant effect on cyber risk  
management (f2 = 0.421) and knowledge management systems (f2 = 0.503), suggesting  
that the AI capability plays a robust role in improving cybersecurity practices and  
knowledge management processes in halal food manufacturing firms. The medium effect  
size (f² = 0.318) of cyber risk management on firm performance and the large effect (f²  
= 0.376) of knowledge management system indicate that both constructs are important  
for the organizational performance improvement, with a greater impact of knowledge  
management. Finally, the capacity for artificial intelligence has a medium direct influence  
on firm performance (f² = 0.289), indicating that AI enhances performance both  
directly and indirectly through other organizational capacities. Finally, the findings  
support the argument that AI competence is a critical driver of organizational  
capabilities and that knowledge management systems and cyber risk management are  
essential drivers of firm success.  
Table 6: Effect size (f²)  
f² Effect Size  
Interpretation  
Large  
0.421 (AI Capability → CRM)  
0.503 (AI Capability → KMS)  
0.318 (CRM → FP)  
Large  
Medium  
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Large  
0.376 (KMS → FP)  
Medium  
0.289 (AI → FP)  
Conclusions and Discussions  
This research investigated the influence of artificial intelligence capabilities, cyber risk  
management, and knowledge management systems on the performance of halal food  
manufacturing enterprises in Pakistan. The results indicate that all provided predictions  
are validated, demonstrating that AI capability is crucial in enhancing organizational  
systems and performance outcomes. AI capabilities significantly enhance cyber risk  
management and knowledge management systems, thereby enhancing overall business  
performance. The findings align with the theoretical frameworks of the Resource-Based  
View, which assert that organizations gain a competitive advantage by developing and  
integrating key technologies and organizational skills. The research further substantiates  
that cyber risk management and knowledge management systems have substantial  
beneficial impacts on organizational performance. This indicates that enterprises in  
digitally connected settings, especially within the halal food production industry, must  
emphasize cybersecurity resilience and efficient information exchange to guarantee  
operational efficiency, compliance, and innovation. The findings demonstrate that AI  
capacity directly enhances business performance and indirectly contributes via improved  
cyber risk management and knowledge management systems, underscoring the need for  
integrated digital capability development.  
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Implication of the study  
This research offers several theoretical, practical, and managerial insights for halal food  
production companies in Pakistan. This study theoretically enhances the literature on  
digital transformation by including artificial intelligence capabilities, cyber risk  
management, and knowledge management systems into a unified research framework to  
elucidate business performance. The results substantiate the Resource-Based View,  
Dynamic Capabilities Theory, and Knowledge-Based View by illustrating that technical  
and knowledge-based capabilities constitute significant strategic resources that augment  
organizational competitiveness and performance. The report underscores the need for  
managers and policymakers to invest in AI technology, enhance cybersecurity measures,  
and establish robust knowledge management systems to augment operational efficiency,  
foster innovation, and bolster organizational resilience. The results indicate that halal  
food production companies must use intelligent systems and digital security measures to  
ensure halal compliance, optimize decision-making processes, and augment supply chain  
transparency. The report provides essential insights for government bodies and industry  
regulators to formulate digital transformation strategies and cybersecurity frameworks  
that facilitate the sustainable development of Pakistan’s halal manufacturing sector. The  
research enhances academic understanding and management practice by highlighting the  
strategic significance of AI-driven organizational capabilities in attaining better  
organizational performance.  
Limitations and future recommendations  
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Although this study is a significant contributions, this research has numerous  
shortcomings that need acknowledgment. The study used a cross-sectional research  
design, collecting data at a single time point, which limited the ability to demonstrate  
long-term causal linkages among artificial intelligence capabilities, cyber risk  
management, knowledge management systems, and company performance. Future  
studies may use longitudinal designs to more effectively investigate temporal changes and  
causal relationships. The research examined only halal food manufacturing enterprises in  
Pakistan, which may limit the generalizability of the results to other sectors or nations.  
Future researchers are urged to repeat the study across other economic sectors and  
foreign settings to improve external validity. Third, the research depended on self-  
reported data from management respondents, potentially introducing common method  
bias and subjective interpretation. Subsequent research may use diverse data sources or  
mixed-method strategies to enhance data precision and reliability. The present research  
only investigated direct correlations among the variables. Future studies may investigate  
mediating and moderating factors, including organizational culture, digital innovation,  
technology preparedness, and environmental unpredictability, to enhance understanding  
of organizational performance. Future research may explore upcoming technologies, like  
blockchain, big data analytics, and Industry 4.0 techniques, in conjunction with AI  
capabilities to enhance the literature on digital transformation and organizational  
performance within the halal manufacturing industry.  
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