Malaysia plays a pivotal role in the global halal ecosystem, having developed a comprehensive regulatory
framework for halal certification under the supervision of the Department of Islamic Development Malaysia
(JAKIM). Since its formalization in 1974, JAKIM’s procedures have become internationally recognized,
providing a benchmark for halal compliance and enforcement. These procedures are grounded in the Manual
Procedure for Malaysia Halal Certification (MPPHM) and include document evaluations, physical inspections,
and compliance monitoring (Kamaruddin et al., 2012; JAKIM, 2020).
Three main types of audits are employed in the halal certification process which are; adequacy audits, which
assess documentation and preparedness; field audits, which evaluate on-site processes and facilities; and
follow-up audits, which verify corrective actions after non-compliance issues are identified. These audit stages
ensure a holistic review of compliance with halal standards, covering aspects such as ingredient sourcing,
equipment usage, storage, and hygiene (Jais, 2016; Mohamad & Othman, 2009).
Despite the robustness of the certification framework, the traditional approach is often resource-intensive,
involving manual data entry, physical documentation, and fragmented evidence collection. This has led to
increasing interest in the application of digital tools to modernize and streamline the process. One such
innovation is QuikHalal, a mobile auditing application developed to digitize halal audits. It enables auditors to
create checklists tailored to business types, capture photographic evidence, tag GPS locations, and generate
audit reports automatically—all of which significantly reduce administrative burden, errors, and audit cycle
times (Irfan & Iskandar, 2017; Ahmad et al., 2019; Holistics Lab, 2017).
Technology integration in halal auditing not only improves efficiency and transparency but also supports
Malaysia’s broader digital transformation agenda under Industry 4.0, which emphasizes automation, real-time
data, and mobile solutions in compliance systems. However, the adoption of these technologies remains
uneven, particularly among auditors and businesses that lack digital proficiency or face infrastructural
constraints (Widiani & Abdullah, 2018; Amin, 2021). As such, understanding the behavioral intentions behind
technology adoption—through models like UTAUT—becomes essential in designing strategies to encourage
broader uptake within the halal auditing ecosystem.
Theoretical Foundation
UTAUT consolidates eight existing theories into a unified model to explain user acceptance (Venkatesh et al.,
2003). These include: 1.Theory of Reasoned Action (TRA), 2.Technology Acceptance Model (TAM), 3.
Motivational Model (MM), 4.Theory of Planned Behavior (TPB), 5. Combined TAM and TPB (C-TAM-TPB),
6. Model of PC Utilization (MPCU), 7. Diffusion of Innovations Theory (DOI), and 8.Social Cognitive Theory
(SCT).
By integrating these diverse perspectives, UTAUT provides a consolidated framework for understanding users’
intentions to adopt technology, highlighting four key factors that directly affect both intention and usage
behavior:
1. Performance Expectancy (PE): the degree to which individuals believe that using a system will help
them attain gains in job performance.
2. Effort Expectancy (EE): the perceived ease of use associated with the system.
3. Social Influence (SI): the extent to which users perceive that important others believe they should use
the technology.
4. Facilitating Conditions (FC): the perception of the availability of organizational and technical
infrastructure to support system use.
In addition to these direct predictors, UTAUT also considers moderating variables such as gender, age,
experience, and voluntariness of use, which influence the strength of relationships between constructs. Later
studies have expanded UTAUT to include other psychological factors like self-efficacy (SE), attitude toward
using technology (AT), and technology-related anxiety (ANXI), especially when investigating contexts
involving voluntary or user-driven adoption (Šumak & Šorgo, 2016; Hoque & Sorwar, 2017).