Advancing Sustainability in Meat Cold Chains: Adoption Determinants of Real-Time Visibility Technologies in Australia

Abstract

This study examines the adoption of real-time visibility (RTV) technologies in the Australian meat cold supply chain, a sector where sustainability challenges such as food spoilage, energy inefficiency, and waste are acute. RTV technologies offer promising solutions by enhancing traceability, operational efficiency, and decision-making across supply chain stages. However, adoption remains uneven due to a range of contextual, organisational, and perceptual factors. Through a nationally distributed quantitative survey targeting stakeholders across inventory, logistics, and retail operations, we identify key drivers and barriers influencing RTV adoption. We explore how demographic factors (e.g., age, role), perceived usefulness and ease of use, and supply chain characteristics interact to shape adoption outcomes. Importantly, the study investigates how horizontal collaboration and data-sharing practices moderate these relationships, especially within the transport and logistics stages where cold chain vulnerabilities are highest. Spearman and partial correlation analyses, alongside binary logistic regression, reveal that perceived ease of use and usefulness are significant predictors of adoption, while barriers such as cost and technical complexity impede it. However, strong collaboration and data-sharing networks can mitigate these barriers and enhance adoption likelihood. Our findings suggest that targeted digital infrastructure investment, workforce training, and policy support for cross-organisational collaboration are essential for advancing sustainability in meat cold chains. This research contributes to a growing body of knowledge that connects technological innovation with food system resilience and waste minimisation.

1. Introduction

The increasing complexity of global food supply chains has intensified the need for efficiency, transparency, and traceability—particularly in cold supply chain logistics (CSCL), where temperature-sensitive products are highly vulnerable to quality degradation and regulatory non-compliance [1,2]. Cold chains lie at the centre of food supply chains and are defined as the temperature-controlled infrastructure that provides safe storage, transportation, and distribution of perishable goods [3]. In the meat sector, cold chains are subject to strict requirements for temperature control and continuous environmental monitoring to ensure food safety, quality, and regulatory compliance [4,5,6,7], which necessitates a tailored logistics system. CSCL refers more specifically to the integration of supply chain logistics operations with refrigeration technologies to maintain specified temperature and humidity conditions for temperature-sensitive products (e.g., foods, pharmaceuticals) throughout production, distribution, and consumption [8,9]. When applied specifically to meat and meat products, this constitutes meat CSCL. Given the perishability of meat, precise condition management across processing, storage, transportation, and retail is essential [10,11].

Real-time visibility (RTV) technologies, including internet of things (IoT)-enabled sensors, Radio Frequency Identification (RFID), and cloud-based platforms, have emerged as key enablers of these goals by providing end-to-end monitoring, location tracking, and condition reporting across supply chain nodes [12,13]. The importance of RTV adoption is especially pronounced in CSCL for perishable products such as meat, which demand continuous temperature control and timely interventions to prevent spoilage and ensure food safety [14].

Horizontal collaboration (HC) in logistics systems refers to a collaborative arrangement in which two or more independent companies work together at the same level of the supply chain to plan, coordinate, and execute activities, aiming to improve efficiency, reduce costs, and achieve mutual benefits [15]. This type of collaboration among firms within CSCL has been proposed as a strategy to significantly enhance sustainability performance and reduce food loss and waste (FLW) across the entire chain. This is primarily due to the ability to coordinate firm-specific management activities, such as transport planning and inventory control. When combined with RTV technologies, this coordination improves integration levels, as well as transparency, tracking, and tracing of products and services, enhancing utilisation of resources [16,17]. Based on these points and previous research [13], Figure 1 illustrates the definition of cold supply chain logistics (top of the figure), key supply chain stages (shown at the middle of the figure) (RTV leverage points), and main components’ dynamic interactions for CSCL (bottom of the figure).

Despite notable technological advancements, the adoption of RTV technologies remains uneven across the meat industry, due in part to operational, infrastructural, financial, and organisational challenges [18,19]. These barriers undermine visibility, hinder digital transformation, and ultimately impact supply chain resilience and sustainability.

While prior research has examined broader aspects of digital transformation in food and logistics systems [20], limited empirical evidence exists on the sector-specific drivers and challenges associated with RTV adoption in the meat cold supply chain. In particular, little is known about how firm-level characteristics—such as infrastructure, demographics, and collaborative practices—influence RTV adoption decisions within this context. Moreover, existing frameworks rarely integrate relational enablers like horizontal collaboration and data-sharing, which could play critical roles in facilitating technology uptake in fragmented, high-risk supply chains such as meat logistics.

To address these gaps, this study aims to systematically explore the factors influencing RTV adoption within the meat industry’s CSCL, with a focus on the Australian context. Specifically, the research explores the roles of perceived usefulness, ease of use, attitudes toward technology, horizontal collaboration, and data-sharing mechanisms, along with demographic and operational factors. It also examines how these constructs interact to influence the actual use of RTV technologies.

This research makes three primary contributions. First, it contextualises RTV adoption within the underexplored yet critical meat CSCL sector, addressing a significant gap in the supply chain analytics literature—particularly within the Australian context [2,13,14]—and expands current knowledge in food logistics and supply chain visibility. Second, it develops and empirically tests an integrated conceptual framework that incorporates both technological and relational dimensions, including the moderating roles of collaboration and data-sharing—factors rarely examined jointly in relation to RTV adoption. Third, it provides practitioner-oriented insights based on original data and empirical evidence from industry stakeholders, supporting the development of more effective strategies and policies for digital transformation in perishable supply chains.

The remainder of this paper is structured as follows: Section 2 reviews the relevant literature, especially focusing on RTV adoption, enablers, and barriers. Section 3 details the methodology, while Section 4 and Section 5 present the results and discussion, respectively. Finally, Section 6 concludes the paper with implications, limitations, and future directions.

Table 1. The table of acronyms used in this study.

Acronym	Full Description
RTV	Real-time visibility
CSCL	Cold Supply Chain Logistics
FLW	Food Loss and Waste
RFID	Radio Frequency Identification
IoT	Internet of Things
FSMA	Food Safety Modernization Act (United States)
SFCR	Safe Food for Canadians Regulations (Canada)
SMEs	Small and Medium-sized Enterprises
TAM	Technology Acceptance Model
UTAUT	Unified Theory of Acceptance and Use of Technology
MLA	Meat & Livestock Australia
SPSS	Statistical Package for the Social Sciences
EU	Perceived Ease of Use
USF	Perceived Usefulness
ATT	Attitude Towards Using RTV technologies (Adoption)
F	Factor (Driver/Barrier)
HC	Horizontal collaboration
DSH	Data-sharing
HDC	Hybrid Distribution Centre
p	p-value (statistical significance level)
Exp(B)	Odds Ratio in Binary Logistic Regression
χ2	Chi-Square statistic
R2	Coefficient of Determination (model fit statistic)
WMS	Warehouse Management System
TMS	Transport Management System
EDI	Electronic Data Interchange
API	Application Programming Interface
SOP	Standard Operating Procedures
MVDS	Minimal Viable Data Set
KPIs	Key Performance Indicators
RACI	Responsible, Accountable, Consulted, and Informed
SLA	Service-Level Agreement
ROI	Return-On-Investment
SKU	Stock Keeping Unit
ID	Identifier
AATP	Australian Agricultural Traceability Protocol
NSW	New South Wales
QLD	Queensland
VIC	Victoria
TAS	Tasmania
ACT	Australian Capital Territory
WA	Western Australia
NT	Northern Territory

shows the acronyms used in this study.

2. Literature Review

2.1. CSCL

A cold chain is a temperature-controlled supply chain involving the storage, transportation, and distribution of perishable goods [3]. In the meat sector, cold chains are subject to strict requirements for temperature control and continuous environmental monitoring to ensure food safety, quality, and regulatory compliance [4,5,6,7], which necessitates a tailored logistics system. Efficient CSCL in the meat sector requires coordination across multiple nodes, including processing and packing facilities (slaughterhouses), warehousing, refrigerated transport, and last mile delivery (retail outlets), to minimise temperature deviations, reduce spoilage, and comply with stringent food safety standards [8,9]. The adoption of advanced monitoring systems, RTV technologies, and predictive maintenance of refrigeration equipment further enhances operational efficiency and mitigates risks associated with cold chain disruptions [10,11].

The literature suggests that six intrinsic dynamics—load integrity, product characteristics, conditional demand, origin/destination, distribution, and transport integrity—shape the performance of meat CSCL [2,5,8,9,11]. When clustered around a core of real-time visibility (RTV) technologies and embedded within horizontal collaboration, these dynamics form an integrated arrangement that can establish effective nexuses for cold chain success.

2.2. RTV Technologies

RTV technologies—such as IoT sensors, RFID, and cloud-based platforms—have emerged as critical enablers of these stages and processes, facilitating continuous tracking and responsive intervention [13]. RTV technologies enhance operational efficiency, reduce spoilage, and support compliance with food safety regulations and sustainability goals [20,21,22]. Regulatory frameworks such as the United States Food Safety Modernization Act (FSMA) and the European Union’s cold chain directives increasingly mandate such tools [23]. Nevertheless, RTV adoption in meat supply chains remains fragmented due to barriers including data security concerns, regulatory complexity, infrastructure limitations, and organisational and demographic factors [24,25,26]. This review synthesises the major themes influencing RTV adoption in CSCL to inform a framework that considers barriers, demographic influences, and enabling mechanisms within the Australian meat industry.

2.3. Drivers and Barriers to Technology Adoption

RTV technologies contribute to enhanced quality control by enabling accurate monitoring across inventory, transport, and retail stages [27,28,29]. These technologies facilitate improved information flow, align supply with demand, and reduce food loss and waste [13,30,31]. In transit, RTV enables spoilage mitigation through real-time tracking and dynamic rerouting [32]. Intelligent packaging further supports traceability and freshness assurance [33]. Integration with Industry 4.0 technologies also strengthens transparency and risk analytics [20,34]. Hence, enhanced quality control and inventory efficiency may serve as potential drivers for RTV adoption.

Food safety regulations, such as the FSMA and Safe Food for Canadians Regulations (SFCR) promote traceability and the adoption of RTV technologies [23,35]. However, enforcement inconsistencies and gaps in stakeholder awareness—especially in developing economies—limit their effectiveness [36,37]. Regulatory fragmentation and the burden of compliance can further impede digital transformation in cold chains [19,38]. Firms operating across borders or in developing markets face particular challenges in navigating diverse regulatory landscapes [39]. Thus, while regulatory mandates can be adoption drivers, compliance challenges may act as counterweights [40,41].

Data integrity concerns may also hinder RTV deployment, with firms hesitant to adopt due to risks associated with cyber threats and unauthorised data access in IoT-integrated systems [21]. Technologies such as blockchain offer decentralised, tamper-resistant platforms for secure data exchange [42], while addressing security concerns may help build stakeholder trust and accelerate adoption [43,44].

Previous research indicates that data inaccuracy—stemming from sensor malfunctions, transmission errors, and heterogeneous IT systems—may contribute to resistance or delays in RTV adoption [45,46]. Such limitations disrupt inventory management and compromise food safety, especially in perishable logistics [47]. In contrast, developing robust data validation mechanisms and interoperability standards can lead to fostering confidence in RTV systems [48].

RTV adoption also depends on adequate digital and physical infrastructure, which is often lacking—particularly among Small and Medium-sized Enterprises (SMEs) operating in resource-constrained regions [3,49]. Limited internet connectivity and insufficient Information Technology (IT) capabilities restrict real-time data utilisation [50], while investments in infrastructure and equitable access to digital tools may help facilitate more inclusive adoption [51].

2.4. The Role of Collaboration and Data-Sharing

Horizontal collaboration across supply chain actors facilitates shared investment, risk mitigation, and collective implementation of RTV technologies [52]. Data-sharing practices enhance coordination, traceability, and responsiveness to disruptions [2,53]. Other related factors and practices—such as trust and formal data governance frameworks, often supported by technologies like blockchain—are considered essential for effective collaboration and may influence RTV adoption [54].

2.5. Organisational, Demographic, and Product-Level Factors

The literature also suggests that demographic and organisational characteristics may influence RTV adoption. For instance, younger employees tend to adopt digital technologies more readily than their older counterparts [25,55]. Strategic decision-makers often prioritise long-term value, whereas frontline workers emphasise ease of use and operational integration [56,57]. Inclusive engagement strategies and training programs may help bridge these divides.

Product-specific requirements also shape RTV adoption. Different meat types—such as beef, lamb, and poultry—have varying spoilage rates and storage needs. For instance, lamb requires stricter temperature controls than beef [58]. Furthermore, differences in biological variability affect the feasibility of automation and digital monitoring, highlighting the need for tailored RTV technologies [59] as well as the potential influence of the type of meat handled.

Overall, while RTV technologies offer significant benefits for meat cold chains, adoption is shaped by a complex interplay of technical, regulatory, infrastructural, and organisational factors. A nuanced understanding of these dynamics is essential for designing effective strategies that support digital transformation and strengthen the resilience and sustainability of the meat CSCL sector.

3. Methodology

3.1. Conceptual Framework

To investigate the factors influencing the adoption of RTV technologies in meat CSCL, this study draws upon established theories in technology adoption and barrier analysis. Specifically, the Technology Acceptance Model (TAM) developed by Davis [60,61,62], forms the core theoretical foundation. TAM posits that individuals’ behavioural intentions toward technology use are primarily shaped by perceived usefulness and perceived ease of use, which in turn influence their attitudes toward the technology. In the context of RTV technologies, stakeholders are more likely to adopt these technologies when they perceive them as useful for improving operational efficiency and they are sufficiently easy to operate [61,62,63,64].

While TAM is widely used to explain user acceptance of new technologies and innovations through perceptual factors and constructs such as perceived usefulness (USF) and perceived ease of use (EU), and user attitudes (ATT), it has several limitations when applied to complex, multi-actor environments such as meat CSCL. First, TAM provides limited consideration of external factors (potential barriers and drivers) such as infrastructure readiness, regulatory compliance requirements, perishability risks, complex multi-stakeholder coordination, data security concerns, technical interoperability issues, or high implementation costs, all of which are highly prominent in CSCL contexts. Second, the model lacks integration of inter-organisational dynamics. While in fragmented cold chains, adoption decisions are often interdependent, horizontal collaboration (HC) and data-sharing (DSH) amongst different actors are expected to have a determining effect on promoting adoption decisions. These determining factors and their effects are absent from the original TAM structure. Finally, TAM neglects sector-specific operational nuances. Cold chains for meat have stringent real-time monitoring requirements and product-specific spoilage profiles, such as differences between lamb and beef characteristics, and HC and DSH at different stages of meat CSCL, which influence both the perceived need for technology and its configuration. Such sectoral nuances fall outside TAM’s original scope.

To address these limitations, our study integrates TAM with additional constructs to capture the practical realities of meat CSCL. External factors (barriers and drivers, F1–F6)—including infrastructure readiness, regulatory complexity, quality control needs, and data security concerns—were incorporated to reflect adoption constraints and enablers. Contextual variables, specifically stage-specific levels of horizontal collaboration and data-sharing, were considered in the correlation analysis phase and then extended into the meat type handled (beef/lamb) in the logistic regression phase to account for inter-organisational coordination and product-specific effects. Furthermore, demographic variables such as age and employee job positions were incorporated to reflect the heterogeneous nature of supply chain actors. This integrated approach allows us to retain TAM’s strength in capturing perceptual drivers, while extending it to incorporate structural, relational, and contextual realities of meat CSCL, thus offering a more complete explanatory framework for RTV adoption.

Building on the discussed limitations of TAM in capturing the full complexity of technology adoption in supply chain environments, ranging from external factors such as infrastructure limitations, regulatory complexity, concerns over data security, and the need for enhanced quality control [18,65] to the specific meat CSCL contextual variables, we now present the conceptual framework of our study. Figure 2 illustrates the proposed relationships between the key constructs: perceptual factors derived from TAM (perceived usefulness, ease of use, and attitudes towards use), external factors (drivers and barriers), and contextual factors (horizontal collaboration and data-sharing). Each construct is operationalised into measurable variables and grouped into six analytical categories. In the framework, independent variables (barriers, horizontal collaboration, data-sharing) are represented as rectangles, while dependent variables (attitude, usefulness, and ease of use) are shown as circles. This conceptual structure highlights how these variables are expected to interact and influence the adoption of RTV technologies within meat CSCL, thereby providing a comprehensive foundation for the subsequent empirical analysis.

The study employs a dual-method approach—correlation analysis and logistic regression—chosen for its suitability in examining determinants of RTV technology adoption within complex meat industry CSCL systems. The survey-based quantitative design, grounded in the TAM framework, enables the collection of structured data from a broad range of respondents, including frontline staff, technicians, engineers, experts, and managers across various sectors and departments. This diversity strengthens the validity of the analysis and enables robust statistical assessment of adoption constructs and factors.

Correlation analysis is applied to identify and compare both item-level and construct-level relationships, and to capture direct effects as well as nuanced suppression or reduction effects, while controlling for confounding variables. Logistic regression complements this by providing a robust method for modelling the likelihood of RTV adoption as a function of multiple predictors, including their simultaneous effects and interactions, to assess their relative importance and statistical significance. These complementary methods provide a rigorous framework to reveal how perceptual, external, and contextual factors—such as perceived ease of use, perceived usefulness, attitudes, drivers, barriers, horizontal collaboration, and data-sharing—influence adoption within meat industry CSCL systems.

The survey items for the TAM constructs were primarily adapted from previous peer-reviewed studies and validated instruments to ensure content validity, with additional questions independently developed to capture context-specific factors relevant to RTV technology adoption. A 5-point Likert scale (ranging from 1 = strongly disagree to 5 = strongly agree) was used to measure respondents’ perceptions of the TAM, HC, and DSH construct items. For selected potential external factors (drivers and barriers—F1 to F6), a 7-point Likert scale was employed, allowing respondents to rate the significance of each item from 1 = most significant to 7 = least significant. All constructs, including F1–F6, HC, and DSH, were developed and operationalised through a three-stage process.

First, a comprehensive literature review synthesised findings from peer-reviewed studies on technology adoption in supply chains, cold chain logistics, and food systems (e.g., [12,14,19,20,22,25,42,53,56,66], consistently identifying operational, regulatory, and security-related issues—such as infrastructure readiness, data accuracy, and compliance complexity—as significant determinants of adoption, alongside the pivotal roles of HC and DSH in the context of meat CSCL. Second, preliminary consultations with two industry professionals and two academic experts active in the Australian meat supply chain validated the relevance of these factors and refined wording to align with industry terminology. Third, empirical adaptation to the study context ensured that the final set of factors reflected both universally recognised determinants from the literature and sector-specific challenges identified by experts in the Australian meat supply chain (e.g., [67]), making them theoretically grounded, practically relevant, and directly measurable through the survey instrument.

A pilot test with a small group of 45 respondents representative of the target population was conducted between 8 October 2024 and 14 October 2024 to identify ambiguities, assess question clarity, and ensure appropriate response scaling. Structured data were then collected through a full-scale, nationwide survey administered from 20 October 2024 to 5 December 2024, followed by a screening process as described in Section 3.2.

Collectively, these methodological approaches ensure data quality and validity while providing a robust basis for testing hypotheses and drawing inferences on the relative importance of adoption drivers and barriers; their individual, collective, and interactive effects; and the unique roles of DSH and HC in shaping adoption determinants. In doing so, they align with the study’s objectives and offer both explanatory depth and predictive power.

3.2. Survey and Data Collection

To test the validity of the proposed conceptual framework, a structured questionnaire was used as the primary data collection instrument. It comprised four sections. The first section collected demographic information, including gender, age, organisational role, and years of work experience. The second section addressed the barriers and drivers influencing the adoption of RTV technologies. The third section examined perceptual factors, such as perceived ease of use, perceived usefulness, and overall attitudes toward RTV technologies, and their influence on adoption and usage. The final section explored the roles of HC and DSH in facilitating the adoption of RTV technologies across different stages of CSCL operations, namely inventory, logistics, and retailer operations. QuestionPro, a private research company based in the USA, and specialising in conducting surveys, was engaged to distribute online questionnaires to Australian respondents across CSCL for the meat industry. A brief introductory statement (outlined below) was presented for RTV technologies to help participants better understand the survey context. The survey process ensured full anonymity of respondents, and the questionnaire was reviewed and approved by the Human Research Ethics Committee of the University. The introductory statement was as follows:

“This project aims to explore how users accept and use Real-time Visibility Technologies (RTV) and data-sharing practices across meat cold supply chain logistics systems. These technologies (of various types) enable users to easily track product data such as position, estimated time of arrival, temperature, humidity, and food quality. Tracking is in real time, with some technology compiling data into historical logs. User interfaces with the data include online dashboards and email/SMS alerts. The project also aims to determine whether the application of these technologies can reduce the level of food loss and waste across these systems. Furthermore, it also aims to investigate whether the application of new information technologies can improve sustainability performance”.

The target population included professionals and decision-makers directly involved in supply chain operations, ensuring industry-relevant and experience-based insights into RTV adoption challenges. The survey was conducted from 20 October 2024 to 5 December 2024. Following a screening process, a total of 178 valid responses were collected, exceeding the minimum sample size (167) required to achieve statistical validity at a 99% confidence level with a 10% margin of error. The sample of the data met the following two requirements to ensure the study is nationally representative: (a) at least 18 years of age, and (b) at least 12 months of work experience in one of the Logistics, Retail, Abattoir, Recycling or Disposal centres in the meat industry. The sample data were collected by aligning with the census data from the Australian State of the Industry Report—2023, by Meat & Livestock Australia (MLA, 2023) [67], which allows for broader generalisations at a national level. The demographic data are presented in

Table 2. Demographic characteristics of survey respondents.

Characteristic	Distribution of Responses (%)
Gender	Males: 44.95%	Females: 54.49%	Preferred not to say: 0%	Other: 0.56%
Age	18–30: 28.09%	31–40: 42.70%	41–50: 13.48%	50+: 15.37%
Resident state	NSW: 37.08%	VIC: 24.16%	QLD: 19.66%	Other: 19.10%
Work industry	Retail: 45.05%	Logistics: 25.02%	Abattoir: 10.32%	Other: 16.61%
Position	Senior managers: 47.19%	Supervisors: 17:42%	Operators: 12.92%	Other: 22.47%

, along with the regional concentration of survey participants in Figure 3. However, as this study focuses on selected drivers and barriers, perceptual factors, horizontal collaboration, and data-sharing in adoption of RTV technologies, only age and employee position were considered for further analysis. It is also worth noting that only the three dominant employee position categories from the full survey are presented in Table 2. The “Other” category in the Position row (22.47% of respondents) comprises Senior Executive/Executive (8.87%), Senior Director/Director (6.18%), and the “Others” subclass (8.43%). including procurement officers, advisors, and information technology specialists.

3.3. Data Analysis Techniques

Alongside descriptive statistics, this study employed a dual data analysis approach: correlation analysis and binary logistic regression, conducted using SPSS version 29.0.2.0. Descriptive statistics (means, standard deviations, and response percentages) were used to summarise survey items. Correlation analysis examined baseline associations, including the effects of controlled variables, while regression modelling identified the determinants of RTV technology adoption.

Perceptual constructs in this study (EU, USF, ATT) served as the primary predictors. External factors (F1–F6) captured operational and regulatory barriers and drivers. Contextual variables comprised (HC1–HC3), (DSH1–DSH3), and meat type (beef/lamb), reflecting stage-specific collaboration, data-sharing, and product-specific characteristics. It should be noted that meat type was examined only in the predictive logistic regression stage along with the demographic variables (age and employees’ job positions), whereas the other variables were included in both analyses as detailed below.

3.3.1. Correlation Analysis

Informed by the TAM framework, Spearman’s Rho correlation analysis was first employed to examine baseline associations between perceptual constructs (EU, USF, ATT) and RTV technology usage in the complex context of meat CSCL. This non-parametric technique is well suited for ordinal data and robust in handling non-normally distributed variables, such as in our current case study.

Partial correlation analyses were then conducted to capture the same relationships while controlling for confounding influences of external factors (barriers and drivers, F1–F6), including lack of infrastructure (F1), concerns about data accuracy and reliability (F2), complexity of regulatory compliance (F3), enhanced quality control (F4), compliance with regulatory requirements (F5), and concerns about data security and privacy (F6). Controlling for these factors, the partial correlations enable a nuanced association analysis between perceptual variables and RTV technology use. This disentanglement clarifies the item-level effects of each driver or barrier and provides the foundation for examining reduction and suppression effects.

Reduction (also called attenuation) refers to a situation in which the strength of a relationship becomes weaker once another variable is controlled for. For example, one might expect that training on new RTV technology strongly increases willingness to adopt it, but after taking into account differences in warehouse technology, this effect may appear smaller. In contrast, suppression represents the opposite effect, where a relationship that was previously weak or hidden becomes stronger—or even statistically significant—after accounting for the influence of another variable. For instance, initial observations might suggest that communication between supply chain partners has little impact on technology adoption, but after accounting for standard operating procedures, the effect may become apparent.

In the next phase, the adoption factors that were applied as control variables in the previous step were entered alongside the perceptual constructs’ items in the Variables box, and full correlations were conducted to examine the direct associations between all these factors and RTV technology usage. In parallel, partial correlations, controlling for contextual variables—(HC1–HC3) and (DSH1–DSH3)—enabled further exploration of these relationships. It is worth noting that in the correlation analysis phase, the scope of controlled contextual variables was intentionally limited to HC and DSH. Meat type (beef/lamb), by contrast, represents a categorical contextual characteristic more appropriately examined within the predictive framework of logistic regression, alongside demographic factors such as age and employees’ job positions.

The rationale for the selection of the discussed factors is three-fold. In terms of theoretical relevance, external factors (F1–F6) represent core operational, regulatory, and security considerations that, based on prior literature and our conceptual framework, are likely to influence both user perceptions (EU, USF, ATT) and actual RTV adoption. Without controlling for them, observed correlations might conflate the effect of perceptions with the indirect influence of these external factors. In terms of empirical significance, descriptive statistics and regression results confirmed that these factors were among the most frequently cited determinants—either as enablers or inhibitors—of RTV adoption in meat CSCL. In terms of alignment with study objectives, our aim was to isolate the pure relationships between perceptual constructs (from TAM) and adoption outcomes. Controlling for these barriers and drivers allows us to account for external influences, thereby reducing bias in estimating perceptual effects.

Similarly, the rationale for selecting contextual variables (HC, DSH, and meat type) rests on three principal grounds. In terms of theoretical relevance, they represent core structural and sectoral aspects of the meat CSCL system. Based on prior literature and our conceptual framework, these factors are likely to influence user perceptions (ease of use, usefulness, attitude), RTV adoption, and the corresponding impact level of barriers and drivers. Without controlling for them, observed correlations might conflate perceptual effects with the indirect influence of collaboration, data-sharing practices, or product-specific requirements. In terms of empirical significance, descriptive statistics and preliminary analyses indicated that levels of HC and DSH vary across stages of the supply chain, and meat type is a key differentiator of spoilage risks and monitoring requirements. In terms of alignment with study objectives, this study aimed to isolate the direct relationships between perceptual constructs, selected barriers and drivers, and adoption outcomes.

The approach discussed allows for a more precise estimation of the relationships between perceptual constructs, external factors, and contextual variables by isolating the examined variables and their effects. Notably, correlations were assessed at the individual item level rather than at the aggregated scale level, enabling the identification of more nuanced patterns and associations [68,69]. Overall, this sequence of analyses provides a valid pathway to empirically examine (i) the associations between item-level perceptual factors, informed by the TAM framework, and RTV technology usage; (ii) how external factors (barriers and drivers) influence these relationships; and (iii) how contextual variables (HC, DSH, and meat type) not only affect perceptual–RTV usage associations but also moderate the impacts of the identified barriers and drivers within the meat CSCL context.

3.3.2. Binary Logistic Regression

Following the correlation analysis, binary logistic regression modelling was used to identify significant predictors of RTV adoption and positive user attitudes. This approach is particularly well-suited for analysing dichotomous dependent variables, such as the decision to adopt or not adopt a technology. Binary logistic regression has been widely applied in studies grounded in the TAM framework, as seen in previous works (e.g., [55,70,71]).

In the context of this study, a tailored binary logistic regression model was developed to reflect the unique characteristics of RTV adoption in meat CSCL. While the overarching methodological framework is informed by TAM, our model incorporates interaction terms to examine whether horizontal collaboration and data-sharing practices moderate the impact of adoption barriers on RTV technology uptake. To our knowledge, this specific formulation—featuring interaction terms between collaborative practices and adoption constraints—has not been previously applied within the meat CSCL context, thereby contributing a novel analytical perspective to the literature.

The initial focus of this study was to identify the determinants of RTV adoption (drivers/barriers) in Australia’s meat CSCL, and then to examine how these factors are affected by the moderating effects of HC and DSH. Beyond significance testing, our rationale in specifying HC and DSH as moderators was explanatory: these mechanisms can reduce or suppress the salience of barriers (e.g., infrastructure gaps, data reliability concerns) by improving transparency, standardising processes, and distributing costs/risks. Thus, conditional patterns and stage-specific attenuation provide not just statistical outcomes but evidence about why adoption occurs in some contexts and not others, and how collaborative practices can enable firms to tap the full potential of RTV technologies.

The dependent variable—attitude towards RTV technology adoption—was binary-coded, with a value of “1” indicating positive perception (responses of “Strongly Agree” or “Agree”) and a value of “0” denoting neutral or negative views (responses of “Neutral”, “Disagree”, or “Strongly Disagree”). This approach aligns with established practices in technology adoption research (e.g., [70,72]). Independent variables were selected based on insights from the correlation analysis to ensure meaningful associations with the dependent variable. Variables with statistically insignificant associations were excluded stepwise to produce a parsimonious model [73], except for items theoretically integral to the TAM framework. The final set of independent variables included demographic, perceptual, external, and contextual factors such as age, perceived usefulness, barriers and drivers (e.g., data privacy concerns, regulatory complexity, infrastructure limitations, and quality [14,19,20,74]), meat type, HC, and DSH.

Model parameters were estimated by maximising the log-likelihood function. In addition to coefficient estimates, marginal effects were calculated to interpret the change in the probability of RTV adoption when a binary variable switches from 0 to 1, or when a continuous variable increases by one unit.

To assess the overall performance and fit of the binary logistic regression model, several statistical tests were employed: The Omnibus Test of Model Coefficients evaluates whether the set of predictors, taken together, significantly improves the model compared to a baseline (null) model with no predictors. A statistically significant result (p < 0.05) indicates that the model explains a significant portion of the variation in the dependent variable [75]. The Cox and Snell R² is a pseudo-R² measure that approximates the proportion of variance explained, but its maximum value is less than 1. To provide a more interpretable measure, the Nagelkerke R² adjusts the Cox and Snell statistic to range between 0 and 1, making it comparable in scale to the R² used in linear regression [76]. Finally, the Hosmer and Lemeshow Goodness-of-Fit Test assesses how closely the predicted probabilities match the observed outcomes. A non-significant result (p > 0.05) suggests that the model fits the data adequately, indicating no statistically significant discrepancy between observed and predicted classifications [77]. Overall, the regression model was designed to evaluate the likelihood of RTV technology adoption as a function of key perceptual, contextual, and organisational factors. In particular, this study considered user attitudes as a proxy for adoption decisions in the meat CSCL context, consistent with prior findings that identify attitudes as strong predictors of technology acceptance and adoption [60,66,78].

4. Results

4.1. Descriptive Analysis

This section presents the overall findings of the six groups of parameters discussed earlier in Section 3.1. As the differences between each survey item were statistically significant (p < 0.01), individual p-values are not reported, as all variables in these tables differed significantly from one another. Furthermore, the Kolmogorov–Smirnov test indicated that the survey items were not normally distributed, justifying the use of non-parametric tests (Spearman’s Rho correlations) in this study. Labels and statistics for each survey item and construct are provided in

Table 3. Descriptive statistics of survey items for EU construct.

Survey Item	Variable	Distribution of Responses (%)					Mean (Standard Deviation)
		1	2	3	4	5
Using an RTV tool often involves a lot of hassle (EU1).	Perceived Ease of Use (EU), [60,61,62].	5.62	16.85	32.58	33.15	11.80	3.29 (1.06)
I find it easy to get the RTV tools to do what I want to do (EU2).		1.12	3.37	26.40	38.89	29.21	3.93 (0.89)
It would be easy for me to become skilful at using an RTV tool (EU3).		0.56	3.93	23.03	46.63	25.84	3.93 (0.83)
Overall, I find the RTV tools easy to use (EU4).		0.56	3.37	24.72	47.19	24.16	3.91 (0.82)

1—Strongly disagree (%), 2—Disagree (%), 3—Neutral (%), 4—Agree (%), 5—Strongly agree (%).

,

Table 4. Descriptive statistics of survey items for USF construct.

Survey Item	Variable	Distribution of Responses (%)					Mean (Standard Deviation)
		1	2	3	4	5
An RTV tool is useful for getting information at the vehicle level about the cargo in transit and shipping processes (USF1).	Perceived Usefulness (USF), [60,61,62].	0.56	4.49	28.09	47.19	19.66	3.81 (0.82)
An RTV tool is useful for getting information at the pallet level about cargo in transit and shipping processes (USF2).		1.12	3.37	29.78	48.31	17.42	3.78 (0.81)
An RTV tool is useful for getting information at the item level about the cargo in transit and shipping processes (USF3).		1.69	2.25	29.78	44.94	21.35	3.82 (0.85)
An RTV tool increases productivity (USF4).		1.12	2.81	21.35	58.43	16.29	3.86 (0.76)
Using RTV tools in my job would enable me to accomplish tasks more quickly (USF5).		1.12	4.49	28.65	42.13	23.60	3.83 (0.88)
I would find RTV useful in my job (USF6).		2.81	4.49	28.65	42.13	21.19	3.76 (0.94)

1—Strongly disagree (%), 2—Disagree (%), 3—Neutral (%), 4—Agree (%), 5—Strongly agree (%).

,

Table 5. Descriptive statistics of survey items for ATT construct.

Survey Item	Variable	Distribution of Responses (%)					Mean (Standard Deviation)
		1	2	3	4	5
Using an RTV tool within the next 12 months for my firm is a smart idea (ATT1).	Attitude Towards Using/Adoption (ATT), [63,79,80].	1.69	3.93	28.65	47.19	18.54	3.77 (0.85)
Using an RTV tool within the next 12 months would be good for my firm (ATT2).		0.56	1.69	24.72	48.31	24.72	3.95 (0.78)
Overall, I have a positive impression of this technology (ATT3).		2.25	3.37	29.21	45.51	19.66	3.77 (0.88)
If my firm adopts an RTV tool within the next 12 months, I would be delighted (ATT4).		1.12	2.81	30.90	43.26	21.91	3.82 (0.84)

1—Strongly disagree (%), 2—Disagree (%), 3—Neutral (%), 4—Agree (%), 5—Strongly agree (%).

,

Table 6. Descriptive statistics of survey items for HC construct.

Survey Item	Variable	Distribution of Responses (%)					Mean (Standard Deviation)
		1	2	3	4	5
RTV requires horizontal collaboration among supply chain parties at the inventory level (HC1).	Horizontal collaboration (HC) [65,81].	0.56	1.69	25.84	50.00	21.91	3.91 (0.77)
RTV requires horizontal collaboration among supply chain parties at the transport and logistics stage (HC2).		2.81	2.81	30.34	44.38	19.66	3.75 (0.90)
RTV requires horizontal collaboration among supply chain parties at vendors’ and retailers’ operations (HC3).		2.25	2.25	30.90	45.51	19.10	3.77 (0.86)

1—Strongly disagree (%), 2—Disagree (%), 3—Neutral (%), 4—Agree (%), 5—Strongly agree (%).

,

Table 7. Descriptive statistics of survey items for DSH construct.

Survey Item	Variable	Distribution of Responses (%)					Mean (Standard Deviation)
		1	2	3	4	5
RTV requires data-sharing among supply chain parties at the inventory level (DSH1)	Data-sharing (DSH) [82,83].	0.56	5.06	29.21	44.38	20.79	3.80 (0.85)
RTV requires data-sharing among supply chain parties at the transport and logistics stage (DSH2).		1.69	3.93	28.65	47.19	18.54	3.77 (0.86)
RTV requires data-sharing among supply chain parties at vendors’ and retailers’ operations (DSH3).		1.12	3.93	34.83	43.82	16.29	3.70 (0.83)

1—Strongly disagree (%), 2—Disagree (%), 3—Neutral (%), 4—Agree (%), 5—Strongly agree (%).

and

Table 8. Survey responses on the importance of six factors (F1–F6; barriers and drivers) influencing RTV technology adoption.

Survey Item	Distribution of Responses (%)							Mean (Standard Deviation)
	1	2	3	4	5	6	7
Lack Of Infrastructure (F1).	9.55	18.54	20.22	26.97	10.67	10.67	3.37	3.65 (1.57)
Concerns about data accuracy or reliability (F2).	14.00	18.00	18.50	25.80	10.70	10.70	2.20	3.42 (1.61)
Complexity of regulatory compliance (F3).	10.70	18.00	16.90	30.30	10.70	0.70	2.80	3.65 (1.57)
Enhanced quality control (F4).	18.00	20.20	16.90	21.90	11.80	7.90	3.40	3.26 (1.68)
Regulatory compliance (F5).	19.10	19.70	14.60	21.90	14.60	8.40	1.70	3.25 (1.66)
Concerns about data security and privacy (F6).	14.61	17.42	19.66	26.97	8.99	9.55	2.81	3.38 (1.60)

1—Most Significant (%)|2—Very Significant (%)|3—Significant (%)|4—Moderately Significant (%)|5—Somewhat Significant (%)|6—Slightly Significant (%)|7—Least Significant (%).

. Table 3 presents the descriptive statistics of the survey items measuring the EU construct, while Table 4 provides the corresponding results for the USF construct. Table 5 reports the descriptive statistics for the ATT construct, and Table 6 summarizes those for the HC construct. Similarly, Table 7 presents the descriptive statistics for the DSH construct. Finally, Table 8 displays the survey responses on the perceived importance of six factors (F1–F6), representing both barriers and drivers, that influence RTV technology adoption.

Overall, respondents reported a moderately high perception of ease regarding RTV technology usage, with an overall mean of 3.77 across the four related items. Relatively narrowly spread means of individual items suggest general agreement that RTV technologies are intuitive and manageable. Perceived usefulness was rated positively, with an overall mean of 3.82 across six items, indicating strong consensus on RTV’s contribution to improving productivity and visibility at multiple levels—reinforcing the perception that it provides substantial operational value. Attitudes toward RTV adoption were generally favourable, with an overall mean of 3.83 across four items. Mean scores greater than 3.5 reflect a positive disposition, indicating that participants view RTV as a smart and beneficial move for their organisations, suggesting openness to adoption and alignment with strategic goals for digital transformation.

Respondents reported a consistently positive perception of both horizontal collaboration and data-sharing as key enablers of RTV adoption across all supply chain stages, with overall mean scores of 3.81 and 3.76, respectively. Among the three stages, the inventory level emerged as the most critical point for both collaboration and data-sharing, reflecting strong consensus on its foundational role in enabling visibility. At the transport and logistics stage, perceptions were slightly lower yet still favourable, suggesting alignment between physical flow coordination and information flow integration. Similarly, ratings for vendors’ and retailers’ operations were moderately high, indicating a shared appreciation for downstream interoperability. The consistently narrow standard deviations across items underscore broad agreement on the necessity of coordinated and transparent practices to successfully implement RTV technologies.

The findings indicate that the selected factors were generally perceived as significant in shaping RTV adoption decisions. Lack of infrastructure and regulatory complexity emerged as the most influential, highlighting concerns around system readiness and compliance demands. In contrast, factors such as quality control enhancement and regulatory compliance alignment, while still notable, were perceived as relatively less influential. This contrast suggests that while certain benefits of RTV are acknowledged, practical and structural challenges may weigh more heavily in shaping adoption outcomes.

4.2. Correlation Analysis Results

For presentation purposes, Spearman’s Rho correlation coefficients were multiplied by 100 and are reported in Table 9 and Table 10. Each table reports three sets of values:

• Full (Zero-order) Correlations: These reflect the bivariate associations between each survey item and the actual use of RTV technologies without any statistical control.
• Partial Correlations: These coefficients represent the relationship between each survey item and RTV use after statistically controlling for one of the designated barriers or drivers (i.e., the control factors F1 to F6, such as lack of infrastructure).
• Difference (Full–Partial): This value indicates the change in the strength of the association when the influence of the control variable is removed.

The deep green colours indicate a significance level of p < 0.01, while the light green colours indicate a significance level of p < 0.05. The grey colour means statistically insignificant. Partial correlation in these tables measures the relationship between variables while statistically controlling for each of the designated factors. The interpretation of these results is presented in the next sub-section.

Table 9. Spearman correlational coefficient between dependent (EU1–EU4, USF1–USF6, ATT1–ATT4) and independent variables (F1, F2, F3) (full vs. partial, correlations were multiplied by 100).

	F1			F2			F3
Var.	Full	Partial	Difference	Full	Partial	Difference	Full	Partial	Difference
EU1	19.3	24.3	−5.0	19.3	24.0	−4.7	19.3	23.2	−3.9
EU2	33.4	29.4	4.0	33.4	29.3	4.1	33.4	31.0	2.4
EU3	34.7	30.7	4.0	34.7	30.6	4.1	34.7	33.9	0.8
EU4	37.6	34.7	2.9	37.6	34.5	3.1	37.6	34.2	3.4
USF1	35.8	30.0	5.8	35.8	34.5	1.3	35.8	32.3	3.5
USF2	27.6	22.3	5.3	27.6	24.0	3.6	27.6	26.9	0.7
USF3	29.8	26.8	3.0	29.8	28.8	1.0	29.8	26.3	3.5
USF4	26.8	27.4	−0.6	26.8	27.6	−0.8	26.8	25.3	1.5
USF5	30.4	25.1	5.3	30.4	24.4	6.0	30.4	25.2	5.2
USF6	33.9	28.6	5.3	33.9	30.3	3.6	33.9	27.9	6.0
ATT1	40.5	39.1	1.4	40.5	39.4	1.1	40.5	37.9	2.6
ATT2	37.8	35.7	2.1	37.8	35.9	1.9	37.8	34.7	3.1
ATT3	25.0	22.0	3.0	25.5	22.2	3.3	25.0	20.1	4.9
ATT4	24.1	20.8	3.3	24.1	19.3	4.8	24.1	20.6	3.5

Note: The deep green colours indicate a significance level of p < 0.01, while the light green colours indicate a significance level of p < 0.05.

Table 9. Spearman correlational coefficient between dependent (EU1–EU4, USF1–USF6, ATT1–ATT4) and independent variables (F1, F2, F3) (full vs. partial, correlations were multiplied by 100).

	F1			F2			F3
Var.	Full	Partial	Difference	Full	Partial	Difference	Full	Partial	Difference
EU1	19.3	24.3	−5.0	19.3	24.0	−4.7	19.3	23.2	−3.9
EU2	33.4	29.4	4.0	33.4	29.3	4.1	33.4	31.0	2.4
EU3	34.7	30.7	4.0	34.7	30.6	4.1	34.7	33.9	0.8
EU4	37.6	34.7	2.9	37.6	34.5	3.1	37.6	34.2	3.4
USF1	35.8	30.0	5.8	35.8	34.5	1.3	35.8	32.3	3.5
USF2	27.6	22.3	5.3	27.6	24.0	3.6	27.6	26.9	0.7
USF3	29.8	26.8	3.0	29.8	28.8	1.0	29.8	26.3	3.5
USF4	26.8	27.4	−0.6	26.8	27.6	−0.8	26.8	25.3	1.5
USF5	30.4	25.1	5.3	30.4	24.4	6.0	30.4	25.2	5.2
USF6	33.9	28.6	5.3	33.9	30.3	3.6	33.9	27.9	6.0
ATT1	40.5	39.1	1.4	40.5	39.4	1.1	40.5	37.9	2.6
ATT2	37.8	35.7	2.1	37.8	35.9	1.9	37.8	34.7	3.1
ATT3	25.0	22.0	3.0	25.5	22.2	3.3	25.0	20.1	4.9
ATT4	24.1	20.8	3.3	24.1	19.3	4.8	24.1	20.6	3.5

Note: The deep green colours indicate a significance level of p < 0.01, while the light green colours indicate a significance level of p < 0.05.

Table 10. Spearman correlational coefficient between dependent ((EU1–EU4, USF1–USF6, ATT1–ATT4) and independent variables (F4, F5, F6) (full vs. partial, correlations were multiplied by 100).

	F4			F5			F6
Var.	Full	Partial	Difference	Full	Partial	Difference	Full	Partial	Difference
EU1	19.3	22.7	−3.4	19.3	23.2	−3.9	19.3	22.3	−3.0
EU2	33.4	30.9	2.5	33.4	31.0	2.4	33.4	31.8	1.6
EU3	34.7	33.4	1.3	34.7	33.9	0.8	34.7	34.0	0.7
EU4	37.6	36.5	1.1	37.6	34.2	3.4	37.6	36.9	0.7
USF1	35.8	32.8	3.0	35.8	32.3	3.5	35.8	34.9	0.9
USF2	27.6	24.9	2.7	27.6	26.9	0.7	27.6	27.3	0.3
USF3	29.8	27.1	2.7	29.8	26.3	3.5	29.8	28.3	1.5
USF4	26.8	25.5	1.3	26.8	25.3	1.5	26.8	28.5	−1.7
USF5	30.4	27.0	3.4	30.4	25.2	5.2	30.4	28.2	2.2
USF6	33.9	30.6	3.3	33.9	27.9	6.0	33.9	31.8	2.1
ATT1	40.5	39.3	1.2	40.5	37.9	2.6	40.5	40.4	0.1
ATT2	37.8	36.2	1.6	37.8	34.7	3.1	37.8	37.1	0.7
ATT3	25.0	23.1	1.9	25.0	20.1	4.9	25.0	24.3	0.7
ATT4	24.1	22.6	1.5	24.1	20.6	3.5	24.1	24.0	0.1

Note: The deep green colours indicate a significance level of p < 0.01, while the light green colours indicate a significance level of p < 0.05.

Table 10. Spearman correlational coefficient between dependent ((EU1–EU4, USF1–USF6, ATT1–ATT4) and independent variables (F4, F5, F6) (full vs. partial, correlations were multiplied by 100).

	F4			F5			F6
Var.	Full	Partial	Difference	Full	Partial	Difference	Full	Partial	Difference
EU1	19.3	22.7	−3.4	19.3	23.2	−3.9	19.3	22.3	−3.0
EU2	33.4	30.9	2.5	33.4	31.0	2.4	33.4	31.8	1.6
EU3	34.7	33.4	1.3	34.7	33.9	0.8	34.7	34.0	0.7
EU4	37.6	36.5	1.1	37.6	34.2	3.4	37.6	36.9	0.7
USF1	35.8	32.8	3.0	35.8	32.3	3.5	35.8	34.9	0.9
USF2	27.6	24.9	2.7	27.6	26.9	0.7	27.6	27.3	0.3
USF3	29.8	27.1	2.7	29.8	26.3	3.5	29.8	28.3	1.5
USF4	26.8	25.5	1.3	26.8	25.3	1.5	26.8	28.5	−1.7
USF5	30.4	27.0	3.4	30.4	25.2	5.2	30.4	28.2	2.2
USF6	33.9	30.6	3.3	33.9	27.9	6.0	33.9	31.8	2.1
ATT1	40.5	39.3	1.2	40.5	37.9	2.6	40.5	40.4	0.1
ATT2	37.8	36.2	1.6	37.8	34.7	3.1	37.8	37.1	0.7
ATT3	25.0	23.1	1.9	25.0	20.1	4.9	25.0	24.3	0.7
ATT4	24.1	22.6	1.5	24.1	20.6	3.5	24.1	24.0	0.1

Note: The deep green colours indicate a significance level of p < 0.01, while the light green colours indicate a significance level of p < 0.05.

4.2.1. Interpretation of Correlation Coefficient Between Dependent and Independent Variables

Overall, the results reveal that for many survey items, the partial correlations are lower than the corresponding full correlations. This reduction suggests that part of the association observed in the full correlations was attributable to the influence of the control variables. In other words, when the shared variance with factors such as infrastructural limitations, regulatory complexities, or data security concerns is removed, the direct association between the survey items (e.g., perceptions of ease of use or usefulness) and actual RTV technology use is attenuated. This finding implies that the control variables were contributing indirect associations that inflated the zero-order correlations.

Conversely, there are instances where the partial correlation exceeds the full correlation—a phenomenon known as a suppression effect. For example, consider survey item EU1 (“Using an RTV tool often involves a lot of hassle”). In the first block (e.g., when controlling for F1, “Lack of physical and technological infrastructure”), the full correlation with RTV technology use was 19.3, while the partial correlation increased to 22.7. The resulting negative difference of −3.4 indicates that the control variable was obscuring part of the true relationship between perceived hassle and actual RTV use. Once the overlapping influence of infrastructural deficiencies was statistically removed, the unique association became more pronounced. These findings are informative on two levels. First, the attenuation observed in most partial correlations underscores the importance of accounting for extraneous influences when assessing the direct relationships between technology perceptions and their usage. Second, the occurrence of suppression effects, as seen with EU1, suggests that certain control variables may mask the genuine impact of a predictor. When these extraneous influences are removed, the underlying relationship emerges more clearly.

In summary, by isolating the direct effects of the survey items on RTV technology use through partial correlations, the analysis provides a more nuanced understanding of the adoption process. These results suggest that while overall perceptions (e.g., ease of use, usefulness, and attitudes) are related to RTV use, their apparent strength is partly confounded by the external factors examined. Addressing these factors, such as enhancing infrastructural support, could thus unmask and potentially reinforce the underlying positive associations between technology perceptions and actual usage behaviour.

Reduction (attenuation) when controlling for barriers indicates that infrastructure, regulatory complexity, and data security concerns partially explain apparent links between ease/usefulness and adoption: when the enabling conditions are weak, perceived ease/usefulness cannot translate into actual uptake. Conversely, suppression cases (e.g., hassle vs. use) suggest that some negative influences were initially hidden by shared variance with barriers, and only became visible once those confounds were accounted for. Practically, this means that improving enabling conditions (infrastructure, governance) not only strengthens the realised impact of perceived usefulness and ease of use, but also prevents the emergence of hidden frictions (such as perceived hassle) that otherwise remain masked. Together, these dynamics clarify why firms with similar perceptions may diverge in adoption outcomes depending on their infrastructural and regulatory context.

4.2.2. Horizontal Collaboration and Data-Sharing Effect at Different Stages of Meat CSCL

To gain further insights and to examine how horizontal collaboration and data-sharing at different stages of meat CSCL—namely, the inventory level (HC1 and DSH1), transport logistics operations (HC2 and DSH2), and vendors and retailers’ operations (HC3 and DSH3)—might influence the effects of these key items, the procedure was repeated with these variables controlled for. In this iteration, F1 to F6 were entered into the Variables box, thereby generating the data presented in

Table 11. Spearman correlation coefficients between survey items, selected barriers/drivers, and RTV use, controlling for horizontal collaboration at different stages of meat CSCL.

	HC1			HC2			HC3
Var.	Full	Partial	Difference	Full	Partial	Difference	Full	Partial	Difference
EU1	19.3	15.4	3.9	19.3	17.6	1.7	19.3	19.7	−0.4
EU2	33.4	27.3	6.1	33.4	27.0	6.4	33.4	29.7	3.7
EU3	34.7	29.0	5.7	34.7	27.4	7.3	34.7	31.3	3.4
EU4	37.6	31.2	6.4	37.6	30.4	7.2	37.6	34.3	3.3
USF1	35.8	29.8	6.0	35.8	26.9	8.9	35.8	32.7	3.1
USF2	27.6	21.6	6.0	27.6	15.8	11.8	27.6	24.2	3.4
USF3	29.8	23.4	6.4	29.8	21.9	7.9	29.8	26.7	3.1
USF4	26.8	21.4	5.4	26.8	17.9	8.9	26.8	23.4	3.4
USF5	30.4	23.5	6.9	30.4	19.8	10.6	30.4	27.0	3.4
USF6	33.9	27.2	6.7	33.9	26.0	7.9	33.9	31.0	2.9
ATT1	40.5	33.8	6.7	40.5	34.4	6.1	40.5	38.0	2.5
ATT2	37.8	30.4	7.4	37.8	29.8	8.0	37.8	34.6	3.2
ATT3	25.0	16.5	8.5	25.0	14.5	10.5	25.0	20.8	4.2
ATT4	24.1	14.1	10.0	24.1	14.3	9.8	24.1	18.9	5.2
F1	−31.1	−29.7	−1.4	−31.1	−27.3	−3.8	−31.1	−29.6	−1.5
F2	−29.6	−30.8	1.2	−29.6	−25.8	−3.8	−29.6	−27.6	−2.0
F3	−27.4	−26.1	−1.3	−27.4	−22.5	−4.9	−27.4	−27.2	−0.2
F4	−18.5	−16.2	−2.3	−18.5	−15.5	−3.0	−18.5	−16.3	−2.2
F5	−27.7	−27.2	−0.5	−27.7	−25.5	−2.2	−27.7	−25.6	−2.1
F6	−20.8	−19.8	−1.0	−20.8	−19.2	−1.6	−20.8	−19.5	−1.3

Note: The deep green colours indicate a significance level of p < 0.01, while the light green colours indicate a significance level of p < 0.05. The grey colour means statistically insignificant.

and

Table 12. Spearman correlation coefficients between survey items and selected barriers/drivers, and RTV use controlling for data-sharing at different stages of meat CSC.

	DSH1			DSH2			DSH3
Var.	Full	Partial	Difference	Full	Partial	Difference	Full	Partial	Difference
EU1	19.3	13.8	5.5	19.3	18.1	1.2	19.3	18.5	0.8
EU2	33.4	22.0	11.4	33.4	30.0	3.4	33.4	28.2	5.2
EU3	34.7	21.1	13.6	34.7	30.7	4.0	34.7	27.9	6.8
EU4	37.6	24.4	13.2	37.6	34.1	3.5	37.6	31.0	6.6
USF1	35.8	27.7	8.1	35.8	32.5	3.3	35.8	30.2	5.6
USF2	27.6	18.2	9.4	27.6	23.6	4.0	27.6	20.0	7.6
USF3	29.8	20.1	9.7	29.8	26.5	3.3	29.8	23.1	6.7
USF4	26.8	15.8	11.0	26.8	22.1	4.7	26.8	20.8	6.0
USF5	30.4	16.9	13.5	30.4	25.7	4.7	30.4	22.2	8.2
USF6	33.9	24.8	9.1	33.9	30.5	3.4	33.9	26.7	7.2
ATT1	40.5	29.1	11.4	40.5	37.4	3.1	40.5	35.4	5.1
ATT2	37.8	24.0	13.8	37.8	34.3	3.5	37.8	31.4	6.4
ATT3	25.0	12.2	12.8	25.0	19.9	5.1	25.0	15.6	9.4
ATT4	24.1	9.1	15.0	24.1	18.7	5.4	24.1	15.8	8.3
F1	−31.1	−28.9	−2.2	−31.1	−29.4	−1.7	−31.1	−28.2	−2.9
F2	−29.6	−25.8	−3.8	−29.6	−27.5	−2.1	−29.6	−28.5	−1.1
F3	−27.4	−24.1	−3.3	−27.4	−25.8	−1.6	−27.4	−26.5	−0.9
F4	−18.5	−16.8	−1.7	−18.5	−16.0	−2.5	−18.5	−16.0	−2.5
F5	−27.7	−26.1	−1.6	−27.7	−25.9	−1.8	−27.7	−26.0	−1.7
F6	−20.8	−21.2	0.4	−20.8	−19.5	−1.3	−20.8	−20.0	−0.8

Note: The deep green colours indicate a significance level of p < 0.01, while the light green colours indicate a significance level of p < 0.05. The grey colour means statistically insignificant.

. The interpretation of these results is presented in the next sub-section.

4.2.3. Interpretation of Correlation Coefficients Controlling for Horizontal Collaboration

Table 11 reports Spearman’s correlation coefficients between key survey items (EU, USF, ATT) and the actual use of RTV technologies, when controlling for horizontal collaboration at three different stages of meat CSCL: HC1—inventory level, HC2—transport and logistics stage, and HC3—vendors’ and retailers’ operations.

Key observations from this table indicate that two distinct effects are evident—suppression vs. reduction effects, and stage-specific variations, which are described below:

• Suppression vs. Reduction Effects: For F2 “Concerns about data accuracy or reliability” under HC1 (inventory level), the full correlation is −29.6 and the partial correlation is −30.8, giving a positive difference of +1.2. This means that when we control for horizontal collaboration at the inventory level, the negative association becomes stronger (more negative). The increase in the strength of the relationship indicates a suppression effect, where the control variable was masking part of the true association.

Conversely, under HC2 (transport and logistics), the full correlation is −29.6 and the partial correlation is −25.8, resulting in a negative difference of −3.8. Similarly, under HC3 (vendors’ and retailers’ operations), the difference is −2.0. In both cases, controlling for horizontal collaboration reduces the magnitude of the negative association, which is indicative of a reduction effect.

• Stage-Specific Variations: The magnitude of these differences is not uniform across the three stages. For instance, under HC2 (transport and logistics), items such as USF6 show a larger drop (difference of 7.9) compared to the inventory (HC1) or vendors/retailers stage (HC3). This suggests that horizontal collaboration in the transport/logistics stage plays a more pronounced role in influencing the relationship between perceived usefulness and RTV technology adoption.

Attitudinal items (ATT1–ATT4) also exhibit variable changes across the three stages. Under HC2, ATT2’s correlation drops by 8.0 points, indicating that the influence of horizontal collaboration in that phase significantly alters the apparent association between overall attitude and RTV use.

These findings underscore that horizontal collaboration across different supply chain stages both conceals and contributes to the variance in the direct relationship between user perceptions (ease of use, usefulness, and attitudes) and actual technology adoption and use. The results imply that intervention strategies aimed at enhancing RTV technologies may need to account for the specific stage of horizontal collaboration, as its impact is not uniform across the supply chain.

Collaboration—particularly horizontal collaboration at the inventory stage—is a powerful “early alignment” lever: agreeing on counts, units, and handling rules up front reduces uncertainty downstream, which explains the markedly stronger association with adoption than collaboration later in the chain. By contrast, collaboration during transport/logistics mainly dampens shock/variability (e.g., exceptions management), so it moderates the usefulness–adoption link rather than creating it. Collaboration at the retail stage is often constrained by commercial sensitivities, so its marginal contribution is smaller. These differences clarify why collaboration “works” most where shared data definitions and responsibilities are first set.

4.2.4. Interpretation of Correlation Coefficients Controlling for Data-Sharing

Table 12 is divided into two sections. The first section presents the correlations for the survey items (EU, USF, and ATT) when controlling for the data-sharing effect at three different stages of meat CSCL, namely: DSH1—inventory level, DSH2—transport and logistics stage, and DSH3—vendors’ and retailers’ operations.

The second section presents the corresponding correlations for a set of barrier/driver factors (F1–F6), which are inherently negative, indicating that higher perceived barriers are associated with lower RTV technology adoption. Notably, respondents perceive “Lack of Infrastructure” (F1), with a correlation of −31.1—the most negative value among F1 to F6—as the most significant barrier to RTV technology adoption. In terms of the “Survey Items”, the following points are evident:

• Consistent Reduction in Correlations: For nearly all survey items, the partial correlations are substantially lower than the full correlations once data-sharing is controlled. For example, EU2’s full correlation is 33.4, which decreases to 22.0 under DSH1, 30.0 under DSH2, and 28.2 under DSH3—corresponding to differences of 11.4, 3.4, and 5.2 points, respectively. This consistent reduction suggests that data-sharing processes explain a significant portion of the variance in the observed relationships between user perceptions (such as ease of use, usefulness, and attitudes) and RTV technology adoption. Similar reductions are observed for other items, including the usefulness measures (USF1–USF6) and attitudinal items (ATT1–ATT4); for instance, ATT2 drops by 13.8 points under DSH1, 3.5 under DSH2, and 6.4 under DSH3.
• Stage-Specific Variations: The magnitude of these reductions is not uniform across the three data-sharing stages. For example, under DSH1 (inventory stage), USF6 experiences a larger reduction—a difference of 9.1 points—compared to a 3.4-point drop under DSH2 (transport and logistics stage) and a 7.2-point drop under DSH3 (vendors/retailers stage). This pattern indicates that data-sharing in the inventory stage plays a more pronounced role in mediating the relationship between perceived usefulness and RTV technology adoption.

Likewise, attitudinal items show variable changes. For instance, ATT2’s correlation decreases by 3.5 points under DSH2, compared to 13.8 and 6.4 points under DSH1 and DSH3, respectively. This variability suggests that the influence of data-sharing on the association between overall attitude and RTV use is especially significant at the inventory stage.

In terms of the barrier/driver factors (F1–F6), the following can be observed:

• Negative Associations Remain Robust: The negative correlations for these factors (e.g., F1 at −31.1 and F2 at −29.6) remain relatively stable after controlling for data-sharing—with differences generally ranging from about 1.1 to 3.8 points. This relative stability implies that while data-sharing accounts for some shared variance, the direct adverse effects of these barriers on RTV adoption remain strong.
• Implications for Barriers/Drivers: For example, F6 (“Concerns about Data Security and Privacy”) shows minimal differences—only 0.4 under DSH1, −1.3 under DSH2, and −0.8 under DSH3—indicating that its negative association with RTV adoption is largely independent of the data-sharing processes. This suggests that even after accounting for data-sharing, concerns regarding data security and privacy continue to serve as a robust barrier to adoption.

Overall, Table 12 reveals that data-sharing factors substantially mediate the relationships between user perceptions (such as ease of use, usefulness, and overall attitude) and RTV technology adoption. This mediation is evident from the consistent reduction in correlation coefficients when data-sharing is controlled. Notably, the extent of this mediation is stage-specific. For instance, several items—such as USF5 and ATT2—exhibit the largest drops under the inventory stage (DSH1), with differences reaching up to 13.5 and 13.8 points, respectively, compared to more modest reductions at the vendors/retailers (DSH3) or transport and logistics (DSH2) stages. Simultaneously, the barrier/driver factors (F1–F6) continue to display robust, stable negative associations with RTV technology adoption. Their correlations remain largely unchanged across stages, underscoring their independent role in hindering technology uptake. This dual dynamic highlights the necessity of addressing both stage-specific data-sharing processes—particularly in the inventory stage—and the persistent infrastructural or regulatory barriers when developing strategies to enhance technology adoption in complex supply chain environments.

When inventory-level data-sharing exists—shared Stock Keeping Units (SKUs), batch/lot IDs, agreed inventory clocks—concerns about data accuracy and regulatory compliance are less binding because traceability improves and audits are simpler. In transport, data-sharing reduces exception blind spots (e.g., dwell times, temperature excursions) but may not resolve upstream data-model mismatches. In retail, late-stage sharing helps with recalls/customer assurance but rarely shifts the integration burden earlier in the chain. These patterns explain where data-sharing most effectively converts intention into adoption.

4.3. Binary Logistic Regression Analysis

In this analysis, the attitude items were consolidated into a single variable, which was subsequently dichotomised into positive and negative attitudes.

The significant result of the Omnibus Test of Model Coefficients (χ² = 26.829, p < 0.001) confirms that the inclusion of the selected predictors significantly improves the model compared to the null model, indicating that it effectively predicts RTV adoption. The Cox and Snell R² (0.605) and Nagelkerke R² (0.850) both demonstrate a strong relationship between the predictors and the likelihood of adoption. While Cox and Snell provides a conservative estimate of explained variation, Nagelkerke adjusts this measure to allow values closer to 1.0, suggesting that up to 85% of the variance in adoption likelihood can be accounted for by the predictors. Overall, these findings indicate that the model has strong explanatory power. Furthermore, the non-significant Hosmer and Lemeshow Goodness-of-Fit Test result indicates no statistically significant discrepancy between the predicted probabilities and observed outcomes, confirming the adequacy of the model fit.

The results of the binary logistic regression analysis are presented in

Table 13. Binary logistic regression model predicting the attitude towards RTV technology use (adoption).

Variables	B	S.E.	Wald	df	Sig.	Exp(B)
USF2	2.649	1.018	6.764	1	0.009	14.133
USF3	2.769	0.937	8.726	1	0.003	15.943
USF5	1.751	0.759	5.320	1	0.021	5.761
EU3	−1.630	0.732	4.958	1	0.026	0.196
EU4	1.135	0.863	1.729	1	0.189	3.110
EU2	−0.686	0.611	1.258	1	0.262	0.504
USF4	0.503	0.547	0.844	1	0.358	1.653
USF1	−1.639	0.896	3.349	1	0.067	0.194
F6	1.817	0.589	9.507	1	0.002	6.153
F3	1.178	0.481	5.990	1	0.014	3.247
F4	1.818	0.553	10.823	1	0.001	6.159
F2	−1.789	0.539	11.025	1	0.001	0.167
F5	−2.092	0.597	12.270	1	0.000	0.123
F1	−1.310	0.636	4.242	1	0.039	0.270
Age			10.185	5	0.070
Age (1) 18–30	−1.127	1.184	.906	1	0.341	0.324
Age (2) 31–40	−2.371	1.816	1.704	1	0.192	0.093
Age (3) 41–50	−5.531	2.112	6.856	1	0.009	0.004
Age (4) 51–60	−6.273	2.231	7.903	1	0.005	0.002
Age (5) 61–70	−11.671	23,261.948	0.000	1	1.000	0.000
Beef	0.386	1.095	0.124	1	0.724	1.471
Lamb	−2.476	1.176	4.433	1	0.035	0.084
Position (low rank) Reference
Position	−1.164	0.431	7.306	1	0.007	0.312
DSH1	1.789	0.742	5.814	1	0.016	5.981
HC1	3.079	0.773	15.883	1	0.000	21.731
Constant	−23.035	6.368	13.085	1	0.000	0.000

, which includes the estimated coefficients (B), standard errors (S.E.), Wald statistics, significance levels (Sig.), and odds ratios (Exp(B)).

Each coefficient reflects the influence of the corresponding variable on the likelihood of RTV technology adoption. The findings lend empirical support to the Technology Acceptance Model, demonstrating that both perceived ease of use and perceived usefulness significantly influence RTV adoption. Although USF1 and USF4 did not yield statistically significant coefficients in the regression analysis, they were retained for three reasons. First, they capture conceptually relevant dimensions of perceived usefulness within the TAM, ensuring the construct remains multidimensional rather than narrowly defined. Second, prior TAM-based studies (e.g., [61,84,85]) have shown that statistically insignificant items or even entire constructs are often reported and retained to preserve theoretical integrity and comparability across studies. Finally, re-estimating the regression models without USF1 and USF4 confirmed that model fit and the significance of other predictors remained unchanged, demonstrating the robustness of the results. Thus, their inclusion enhances construct validity without compromising statistical rigour. The subsequent Section 4.3.1, Section 4.3.2, Section 4.3.3 and Section 4.3.4 provide a detailed interpretation of these results in relation to the survey items reported in Table 13.

4.3.1. Perceived Usefulness of RTV Technologies (USF2, USF3, USF5)

The positive coefficients indicate that as the perceived usefulness of RTV technologies increases, the likelihood of adoption significantly increases. The odds ratios (Exp(B)) suggest that, for example, an increase in USF2 by one unit increases the odds of adoption by 14.133 times. The significant p-values (p < 0.05) confirm the strong predictive power of these variables.

The strong positive effect of perceived usefulness underscores that organisations adopt RTV technologies primarily for their practical benefits, such as improving operational efficiency, tracking cargo, and enhancing decision-making processes. The significant impact of USF2 (Exp(B) = 14.133, p = 0.009) and USF3 (Exp(B) = 15.943, p = 0.003) suggests that firms with a higher appreciation of RTV’s usefulness are substantially more likely to adopt the technology.

Although USF1 shows a negative trend and USF4 a positive one, neither reaches conventional levels of statistical significance. Based on the presented results, organisations should prioritise interventions that clearly demonstrate the operational benefits of RTV technologies. Awareness campaigns and hands-on training can help emphasise the practical advantages (e.g., improved efficiency and decision-making) linked to higher ratings on USF2, USF3, and USF5.

In perishable chains, RTV directly lowers shrink (temperature excursions, dwell), reduces recall scope through finer-grained traceability, and supports certification audits. Because these outcomes translate into immediate financial and compliance gains, firms that see this value concentrate adoption where spoilage risks and audit exposure are highest. Thus, perceived usefulness is not merely attitudinal—it proxies concrete risk and cost reductions, explaining its strong and consistent association with adoption.

4.3.2. Ease of Use (EU3, EU4)

Notably, the significant negative coefficient for EU3 suggests that higher agreement with the statement “It would be easy for me to become skilful at using an RTV tool” is associated with lower odds of adoption. The effects of EU4 and EU2 are not statistically significant. The counterintuitive negative coefficient for EU3 can be interpreted through several theoretically grounded mechanisms. First, prior studies highlight a ceiling effect in self-assessed skills, where respondents with initially high confidence show attenuated behavioural effects on adoption [86,87]. Second, research on digital systems adoption demonstrates perception–adoption dissonance, in which users who find a system easy to use may still hesitate to adopt if expectations are unmet, triggering cognitive dissonance (e.g., denial, rationalisation) [88,89]. Third, reverse causality has been noted in TAM extensions, where prior adoption behaviours shape later perceptions of ease of use, rather than the reverse [86,90]. Finally, even where ease of use is acknowledged, stage-specific organisational constraints—such as legacy systems, budget limitations, or lack of managerial support—can limit actual uptake [91,92,93]. Taken together, these mechanisms suggest that the negative EU3 coefficient is not anomalous, but reflects complex interactions between perceptions, behaviour, and organisational realities in meat CSCL.

Based on the demonstrated results, although overall ease of use is generally seen as a facilitator, the counterintuitive negative effect observed for EU3 warrants further investigation. Developers should ensure that training programs effectively address the learning curve, and future surveys might need to consider rephrasing or reverse coding problematic items.

Respondents may find tools easy to learn while still expecting integration friction—Application Programming Interface (API) integration, scanner/label changes, Standard Operating Procedures (SOP) rewrites, and retraining. Where integration costs and workflow disruption are salient, “ease” at the interface does not outweigh operational risk. This explains negative or muted coefficients: perceived simplicity is necessary but not sufficient; firms adopt when process integration is credibly solved.

4.3.3. Barriers and Challenges to Adoption

All designated factors were identified as statistically significant determinants of RTV adoption. Among these, three factors functioned as drivers, while the remaining three acted as barriers. Specifically, positive regression coefficients were observed for Data Security and Privacy (F6), Regulatory Complexity (F3), and Enhanced Quality Control (F4). This indicates that a higher score—reflecting a perception of the factor as less problematic—is associated with an increased likelihood of adoption. In contrast, negative coefficients were found for Data Accuracy and Reliability Concerns (F2), Regulatory Compliance (F5), and Lack of Infrastructure (F1), suggesting that as the perceived severity of these barriers decreases, the odds of adoption also decline.

Given that the factor scores were constructed so that lower values reflect greater perceived importance, a positive coefficient indicates that perceiving a factor as less critical (i.e., assigning it a higher score) corresponds with increased odds of adoption. Conversely, the negative coefficients associated with F2 (Data Accuracy and Reliability Concerns), F5 (Regulatory Compliance), and F1 (Lack of Infrastructure) imply that when these are viewed as highly significant barriers (i.e., rated with lower scores), the likelihood of adoption decreases considerably. These findings underscore the importance of addressing key obstacles—such as through targeted technological investment, enhanced data management protocols, and more efficient regulatory frameworks—to foster broader adoption of RTV technologies. It is also recommended to verify the coding of these items to ensure consistency with the intended directional interpretation.

Barriers operate through two mechanisms: feasibility (e.g., infrastructure gaps that prevent continuous sensing/connectivity) and trust (e.g., doubts about data accuracy, privacy, or audit defensibility). Collaboration and data-sharing mitigate both by pooling infrastructure, harmonising data definitions, and codifying access controls. Hence, barrier intensity is lower—and usefulness “converts” more reliably—where inventory-level collaboration/data-sharing already exists. This interplay clarifies why some contexts exhibit large gains from the same RTV capability set.

4.3.4. Influence of Demographic and Contextual Variables

In this study, “Age” was first treated as a multi-level categorical variable and entered into the “Categorical” box in SPSS, with the “Indicator” option selected for the Contrast type. This approach enabled SPSS to generate dummy variables, allowing each age category to be compared against the reference group. The findings indicate that firms’ older respondents are generally less likely to adopt RTV technologies, as reflected by negative coefficients across all age groups. However, only some of these differences reached statistical significance. Notably, firms with respondents aged 41–50 (B = −5.531, p = 0.009, Exp(B) = 0.004) and 51–60 (B = −6.273, p = 0.005, Exp(B) = 0.002) were significantly less likely to adopt RTV technologies compared to the reference category— created by the program. The results regarding the last age group, Age (5), indicate that it is fully non-significant (Sig. = 1.000, which is the highest p-value possible), and the huge standard error (22,444.183) and extreme coefficient (−11.375) suggest that something unusual is happening, which are all due to the low number of participants (four) in this group.

Based on the discussed points, while the overall effect of age as a categorical variable was marginally non-significant (p = 0.070), certain age groups, specifically Age (3) and Age (4), showed statistically significant associations compared to the reference category, which corresponds to the aging groups in the current study. However, when age was modelled as a continuous variable, it showed a significant overall effect (p = 0.002), implying a possible linear relationship with the outcome. Overall, these results may point to underlying generational perceptions, familiarity with technology, or organisational roles that warrant further investigation.

The results indicate that employee position is a statistically significant predictor of RTV technology adoption (B = −1.164, p = 0.007, Exp(B) = 0.312). Note that the original employee position variable was coded such that higher-ranking individuals (e.g., directors) were assigned a lower value (1), while lower-ranking individuals (e.g., operational staff) were assigned a higher value (5). Consequently, a one-unit increase in the employee position score represents a move from a higher rank to a lower rank.

The negative coefficient and an odds ratio of less than 1 indicate that as the employee position score increases—that is, as one moves from managerial to non-managerial roles—the odds of adopting RTV technologies decrease. More specifically, for each one-unit increase in the employee position score, the odds of adopting RTV technologies are approximately 69% lower (1 − 0.312 = 0.688 or 68.8%). This suggests that respondents in lower-ranked positions (non-managerial roles) are less likely to adopt RTV technologies than those in higher-ranked, managerial positions. Therefore, organisational hierarchy and the involvement of employees in decision-making processes appear to be important factors in determining the likelihood of RTV technology adoption.

These findings highlight why and how age and employee role influence RTV adoption. Older respondents and those in non-managerial roles are less likely to adopt RTV technologies, likely due to higher switching costs (e.g., retraining, device handling, exception management) and fewer perceived strategic benefits. In contrast, managers more directly link RTV adoption to compliance, customer commitments, and asset utilisation. Implementing targeted training, role-specific dashboards, and phased responsibility shifts can help frontline staff better perceive the benefits, thereby narrowing adoption gaps across age and role groups.

In the current study, we created two binary variables (beef, lamb) and entered them simultaneously, which essentially treats each as an independent dummy variable. This approach was used because, based on our survey data, most of the firms may handle multiple types of meat simultaneously (i.e., some firms may handle both beef and lamb). Furthermore, the current study aims to assess the independent effect of handling each meat type while adjusting for other variables in the model. Since both variables are included simultaneously, each effect is adjusted for the presence of the other meat type.

While handling beef was not significantly associated with RTV technology adoption (B = 0.386, p = 0.724, Exp(B) = 1.471), firms handling lamb meat were significantly less likely to adopt RTV technologies compared to firms that do not handle lamb (B = −2.476, p = 0.035, Exp(B) = 0.084). This indicates that the lamb-centric industry is significantly associated with lower odds of adopting RTV technologies, even after adjusting for beef and other covariates. These findings suggest that firms dealing with lamb may face specific challenges in adopting RTV technologies, possibly due to supply chain complexities or market characteristics distinct from those of beef handlers.

The findings highlight why the meat type handled in the CSCL industry influences RTV adoption and how lamb-focused firms may be less likely to adopt RTV technologies. Compared to beef handlers, lamb supply chains are often more fragmented, with smaller lots, variable transport routes, stricter handling requirements, and tighter margins. These characteristics can increase integration challenges and per-unit costs of RTV deployment (e.g., more devices per kilo, more partners to coordinate), which may help explain the lower adoption odds observed for lamb-centric firms, even after controlling for other factors in the model.

In terms of contextual factors, the results reveal that both data-sharing and horizontal collaboration at the inventory level are significant predictors of the likelihood of adopting RTV technologies. Specifically, firms engaging in data-sharing at the inventory level are significantly more likely to adopt RTV technologies, as indicated by a positive coefficient (B = 1.789, p = 0.016, Exp(B) = 5.981). This suggests that the odds of adopting RTV technologies are approximately six times higher for firms that share inventory data compared to those that do not. This finding underscores the importance of transparent inventory data exchange as a facilitator for implementing real-time visibility solutions. Likewise, firms participating in horizontal collaboration at the inventory level (HC1) demonstrate a very strong and statistically significant association with RTV technology adoption (B = 3.079, p < 0.001, Exp(B) = 21.731). This implies that firms involved in collaborative inventory management with other stakeholders are about 22 times more likely to adopt RTV technologies than firms without such collaborations. This highlights that inter-firm collaboration and collective inventory management significantly contribute to the likelihood of embracing real-time technologies, possibly because such firms are better positioned to benefit from shared insights and coordinated logistics practices.

5. Discussion

RTV technologies are increasingly regarded as pivotal for enhancing supply chain efficiency, transparency, and overall performance. In Australia, for instance, trials using “Escavox” logging devices on chilled portioned cuts and beef and lamb primals demonstrated how real-time data and predictive analytics can be applied to extend shelf life and reduce spoilage [94,95]. In addition, blockchain-based lifecycle traceability initiatives such as the “Paddock to Plate” pilot and the development of the Australian Agricultural Traceability Protocol (AATP) highlight how advanced digital platforms can improve transparency and accountability across the agrifood supply chain [96,97,98]. Reported outcomes from these initiatives include reduced food waste through early detection of temperature deviations, improved logistics efficiency via real-time data, and enhanced transparency through verifiable traceability that is accessible to regulators and trading partners. However, diffusion has been constrained by uneven digital infrastructure, high upfront costs, data-ownership concerns, and reluctance among SMEs to invest without clear incentives. Conversely, success has depended on strong industry–research partnerships, targeted funding, interoperable platforms, and early evidence of return on investment. Taken together, these cases highlight both the promise of RTV solutions and the structural and organisational barriers that must be addressed before adoption becomes mainstream. These examples substantiate our claim regarding RTV’s pivotal role in enhancing efficiency, transparency, and overall performance. Nevertheless, the decision to adopt RTV technologies can be influenced by a range of factors that may either facilitate or impede successful implementation. This study provides important empirical insights into the multifaceted factors influencing the adoption of RTV technologies in meat CSCL. While the practical benefits of RTV are widely acknowledged, adoption remains uneven due to a combination of perceptual, infrastructural, regulatory, and contextual challenges. Our findings reinforce the significance of user perceptions, particularly perceived usefulness and ease of use, as central predictors of adoption, aligning with TAM. However, our study extends this framework by incorporating contextual moderators—specifically horizontal collaboration and data-sharing—offering a more nuanced understanding of how these dynamics play out across distinct CSCL stages, as discussed in the following sub-sections.

5.1. Reframing Barriers as Modifiable Enablers

The six barrier factors examined—ranging from data security concerns to regulatory complexity—consistently exhibited negative bivariate correlations with RTV adoption, confirming their role as deterrents. The results indicated that higher levels of perceived ease of use, usefulness, and positive attitudes were significantly associated with actual RTV use, consistent with the core propositions of the TAM framework ([60,61,62,63,64]). These results reaffirm earlier studies (e.g., [19,74,99]) that emphasise the central role of perceived performance benefits in driving technology acceptance in logistics settings. However, logistic regression results paint a more dynamic picture: some barriers showed positive associations when controlling for other variables, suggesting that reducing the salience of these barriers can transform them into effective enablers. This reframing challenges the notion of static barriers and supports a systems-level view where structural challenges, once addressed, unlock adoption potential. For instance, concerns over data privacy (F6) remained robust across analyses, underscoring the need for stronger data governance frameworks. Conversely, factors like enhanced quality control and regulatory compliance demonstrated dual roles, functioning both as perceived burdens and as motivators when appropriately managed. These findings align with the literature ([41,55,70]) on conditional constraints, suggesting that barriers can become adoption catalysts under supportive organisational or policy conditions.

5.2. Leveraging Collaboration and Data-Sharing

A key contribution of this study is the stage-specific analysis of how horizontal collaboration and data-sharing influence RTV adoption. These collaborative mechanisms did not uniformly affect all supply chain stages; rather, their impact varied significantly. Data-sharing and collaboration at the inventory stage had the most substantial mediating effect on adoption. For instance, at the inventory level (HC1), several ease-of-use items, such as EU1, experienced a drop in correlation from 19.3 to 15.4—almost a four-point reduction. This suggests that collaborative practices at this stage contribute to explaining the bivariate association. These findings align closely with the notion that horizontal collaboration strengthens digital adoption by improving system integration, enhancing trust, and reducing uncertainty [100,101,102,103]. A study [103] argues that shared platforms and joint planning enhance visibility and operational alignment, thereby facilitating smoother adoption of RTV technologies. Further, a study [101] shows that such collaboration boosts stakeholder trust and information transparency—conditions closely linked to perceived ease of use and usefulness. Meanwhile, a study [102] identifies information integration as a key mediator between collaboration maturity and supply chain performance, reinforcing the importance of collaborative structures in shaping adoption perceptions. These findings further reinforce how collaborative efforts enable firms to collectively overcome challenges such as high implementation costs, limited technical capacity, and fragmented infrastructure.

Having said that, implementing horizontal cooperation and data-sharing at the inventory stage is both highly valuable and particularly challenging. Our results indicate that collaboration at this stage exerts the strongest mediating influence on the relationship between perceptions (ease of use, usefulness) and RTV adoption, underscoring its role as an “early alignment” mechanism for downstream coordination. However, firms face several barriers, including heterogeneous warehouse systems, a lack of data standardisation, a reluctance to share sensitive stock-level information, and limited resources among SMEs. Trust and governance issues—such as unclear data ownership or access rights—further complicate collaboration. Nevertheless, where firms succeed in establishing shared protocols and interoperable systems, the benefits are substantial: improved traceability, stronger data accuracy, and reduced downstream disruptions. Thus, while adoption at the inventory stage is difficult, it remains essential and can be facilitated through targeted investment in digital infrastructure, common data standards, and trust-building mechanisms.

Collaboration in the transport and logistics phase also moderated the influence of user attitudes and perceived usefulness, though to a lesser extent. This finding reflects the operational sensitivity of logistics processes and highlights the potential benefits of real-time integration across carriers, processors, and third-party providers. Specifically, the transport/logistics stage (HC2) exhibited substantial reductions, with some differences exceeding seven points for key perceptual items. In contrast, the vendor/retailer stage (HC3) showed relatively modest changes. These stage-specific variations highlight the differential impact that horizontal collaboration has across the supply chain. This may reflect the transactional nature of these relationships and suggests a need for more formalized or incentivised collaboration frameworks in this stage. They suggest that the collaborative environment in the transport/logistics stage plays a particularly significant role in shaping user perceptions of RTV technologies. These observations are consistent with [20,21], who noted that the transport and logistics phases are the most operationally sensitive and benefit most from RTV and collaborative practices. This is also consistent with the findings of [104], who, through a systematic review of food and grocery retail logistics, observed that maintaining cold chain integrity places higher demands on logistics systems, while retailers tend to adopt more transactional, short-term approaches, as the complexity often discourages deep collaboration across the supply chain.

Overall, these differentiated effects suggest that stage-specific intervention strategies are essential. A one-size-fits-all approach to collaboration or data integration is unlikely to succeed in complex supply chains with diverse actors and risk profiles.

5.3. Demographic and Organisational Drivers

Our findings reveal that demographic characteristics—particularly employee age—and organisational role significantly influence attitudes toward and adoption of RTV technologies in the meat CSCL sector. These results align with well-established frameworks such as TAM [60,62]) and UTAUT [105], both of which underscore the relevance of individual traits, including age, in shaping behavioural intentions toward technology adoption.

In line with the recent literature [55,99], we find that younger employees are more receptive to digital innovations, likely due to greater familiarity with technology and a higher degree of digital literacy. Conversely, older employees tend to exhibit hesitancy or reduced engagement with new digital tools—a phenomenon also reported in studies on digital transformation in supply chains [25,106]. These generational disparities could stem from cognitive overload, lower perceived self-efficacy, or a perceived lack of necessity, particularly when existing manual processes are seen as sufficient.

Furthermore, organisational position emerged as a critical determinant. As reported in [13,107], senior managers often focus on strategic objectives, including cost savings, compliance, and risk mitigation, and thus may support RTV adoption in principle. However, implementation bottlenecks may arise when frontline workers or supervisors, who interact directly with the technology, perceive it as intrusive, difficult to integrate, or poorly aligned with daily workflows [56]. Interestingly, our results echo [108], who found that lower-tier employees were more open to communication and traceability tools when they improved operational transparency or reduced routine burdens.

Another important consideration is the type of meat handled, which affects both perceived need and technology configuration. As [14,58] highlight, perishability, spoilage thresholds, and regulatory standards differ significantly between meat types (e.g., poultry vs. beef), requiring customised RTV technologies. These product-specific characteristics influence both perceived usefulness and urgency for adoption, particularly among operators and QA professionals. A study [59] notes that the degree of automation and data granularity required varies depending on meat processing variability—an insight corroborated by our logistic regression results showing higher adoption likelihood among firms dealing with more temperature-sensitive products.

Thus, adoption decisions are not only technological but socio-organisational—shaped by who is using the tools, their role, their digital readiness, and the context of their work. Tailored interventions (e.g., age-appropriate digital training, workflow-specific interface designs, or product-focused tech calibration) can help close these demographic and organisational gaps.

These findings suggest that internal communication strategies and role-specific training could bridge perception gaps and foster broader organisational buy-in. Managers and policymakers should avoid assuming uniform readiness across demographic or hierarchical lines.

Taken together, our results indicate that adoption is not driven by isolated factors but by configurations. Perceived usefulness yields adoption when enabling conditions exist (infrastructure, data accuracy) and where early-stage collaboration/data-sharing aligns data models. Inventory-stage collaboration functions as a gatekeeper: by standardising counts/identifiers and responsibilities, it amplifies the realised value of RTV and dampens barrier salience downstream. Firm role and demographics shape the distribution of costs/benefits (frontline burden vs. managerial gains), while product context (e.g., lamb) alters the unit economics and partner alignment required. Regulatory context further moderates these relationships: where audit regimes reward granular traceability and data retention, perceived usefulness converts into adoption at lower thresholds. In short, RTV adoption emerges from fit amongst value, capability, and context, rather than from any single driver.

Based on the points discussed and to maximise RTV effectiveness in meat supply chains, solutions should be customised to the specific product category, reflecting differences in storage duration, spoilage risk, regulatory requirements, and environmental sensitivities. For beef, extended storage and transport durations necessitate RTV configurations with prolonged data logging capabilities and predictive analytics modules for shelf-life estimation. Poultry, given its higher spoilage risk, benefits from tighter temperature tolerance monitoring, higher-frequency data capture, and rapid alert protocols to prevent quality degradation. Lamb export chains require additional integration with export compliance platforms and multimodal transport tracking to ensure adherence to international standards. Differences in optimal temperature thresholds, humidity requirements, and shelf-life parameters across beef, lamb, and poultry should guide sensor calibration, alert configurations, and data integration protocols. We recommend that stakeholders, industry experts, and practitioners establish product-specific RTV deployment guidelines incorporating these distinctions to enhance operational precision, compliance assurance, and overall supply chain resilience.

5.4. Practical and Strategic Implications

The insights from this study suggest that effective RTV adoption requires an integrated strategy that addresses not only technological and infrastructural barriers but also human, organisational, and policy dynamics. Addressing these challenges in a coordinated and strategic way could generate a multiplicative effect, enhancing overall adoption rates. Our empirical findings (Table 13) show that age is a significant predictor of the likelihood of adopting RTV tools, although the effect varies across age groups. Among younger cohorts (18–30 and 31–40), the differences associated with the indicator dummy variables created by SPSS were not statistically significant, reflecting their greater readiness, openness, and adaptability towards new information technologies. This aligns with the results from modelling age as a continuous variable, which indicated higher adoption likelihood among respondents aged ≤40 years. In contrast, the regression results demonstrated that respondents in the 41–50 and 51–60 age groups had significantly lower odds of adoption, consistent with the continuous-variable model, highlighting that age-related disparities are most pronounced in mid- and late-career cohorts.

Given that older employees may feel less confident engaging with RTV tools, companies should implement targeted digital skills training and change management initiatives. Prior studies [25,57] emphasise that age-sensitive and contextually relevant training programmes significantly increase the likelihood of technology acceptance, especially when paired with hands-on, role-specific learning. In addition, differences in strategic versus operational priorities between managers and front-line staff can create misalignment. As suggested by [74], inclusive technology rollout processes that involve cross-functional teams in design and decision-making foster ownership and smoother adoption. This is because aligning RTV implementation with both high-level strategic goals and day-to-day operational efficiencies is crucial for uptake. Moreover, given the varying perishability and compliance needs across meat types, RTV solutions should be custom-configured. Several studies [14,109] advocate for modular systems that allow the level of monitoring granularity (e.g., item- or pallet-level) to be adjusted based on meat type, thereby improving perceived usefulness and return on investment (ROI).

Building on these insights and findings from the current research, we recommend the development of targeted, age-oriented digital training programmes tailored to the specific needs of each group. For younger cohorts (≤40 years), training may emphasise more advanced aspects of RTV technologies, and functionalities, such as data analytics applications, systems integration, and mobile-based solutions such as integration with mobile platforms, that are compatible with their higher digital literacy and readiness to experiment with new technologies.

For older participants (>40 years), training modules should prioritise hands-on demonstrations, simplified user interfaces, and step-by-step guidance tailored to practitioners who may be less familiar with advanced digital tools. For the 41–50 age group specifically, training could reinforce confidence in applying digital tools to daily tasks, with emphasis on workflow integration, scenario-based problem solving, and peer-to-peer learning that aligns new technologies with existing professional experience. For the 51–60 cohort, training should prioritise foundational digital skills, simplified user interfaces, and extended hands-on practice supported by one-on-one mentoring to reduce anxiety, build trust, and enable gradual uptake. This addresses their relatively lower perceived ease of use observed in the analysis.

By tailoring both the content and delivery of training to the demonstrated needs of each age group, organisations can more effectively bridge generational gaps in adoption and enhance overall digital readiness across the workforce.

Our results show that collaboration at the inventory stage has the greatest impact on adoption likelihood. This reinforces findings by [2,52], who argue that early-stage data alignment and transparency among supply chain partners create a foundation for trust and shared investment in digital solutions. Concerns about data security and interoperability were among the most cited barriers in our study. According to [42], blockchain and secure cloud infrastructure offer promising avenues for enhancing data trust, though adoption must be supported by interoperability standards and shared governance protocols [44,110]. Many barriers identified—particularly infrastructural limitations—are more acute for small firms and those in remote areas. As per [3,51], public–private partnerships and regional digital infrastructure investments are essential to support equity in adoption. Policymakers could introduce RTV subsidies, regulatory sandboxes, or targeted capacity-building programs to close this gap.

Regulatory uncertainty and fragmented standards were perceived as both barriers and, in some cases, motivators of adoption. Studies [29,40] suggest that clearer, harmonised compliance pathways—particularly for real-time traceability—can reduce the regulatory burden while encouraging innovation. In place of prescriptive mandates, regulators should explore outcome-based compliance models that allow flexibility in how firms meet traceability and safety standards through RTV systems. As highlighted by [111], such adaptive regulatory designs are better suited for emerging technologies in complex sectors like meat logistics.

From a methodological perspective, future studies should exercise caution when interpreting bivariate and multivariate findings. Where feasible, researchers should report both correlation matrices and regression analyses to account for shared variance, suppression effects, and confounding factors that may shape the relationship between perceived barriers and drivers and the actual adoption of technology. Taken together, the results underscore the multifaceted nature of RTV technology adoption within the meat CSCL context. Although the correlation analyses identify F1–F6 predominantly as barriers to adoption, the logistic regression reveals that some of these factors can act like “drivers” once their overlapping influences with other variables are taken into account. This does not constitute a true contradiction; rather, it highlights the importance of contextualising results in light of the chosen statistical approach and the measurement scale. Ultimately, reducing these barriers appears to be a key catalyst for greater uptake of RTV technologies. By strategically enhancing horizontal collaboration and data-sharing across different stages of the supply chain, and by mitigating infrastructural and regulatory hurdles, practitioners and policymakers can foster a more favourable environment for the adoption and sustained use of real-time visibility technologies.

From a managerial implications perspective and to provide a practical playbook for RTV adoption, implementing RTV effectively requires a structured approach covering readiness, data, integration, governance, and people, as informed by our study findings. First, inferred from inventory-stage collaboration, standardisation of counts and identifiers, and the influence of early-stage practices on downstream RTV adoption, readiness and scoping should begin at the inventory stage by agreeing on consistent product identifiers, recording key dates and handoffs, and monitoring operations for 2–4 weeks to establish a baseline of temperature variations, delays, and missing scans, reflecting the importance of early-stage collaboration.

Next, informed by the finding that perceived usefulness depends on enabling conditions such as data accuracy and alignment, it is important to define a Minimal Viable Data Set (MVDS) consisting of only the essential information needed for recalls or audits, such as product or batch Identifiers (IDs), production and shipping dates, temperature summaries, and custody records, consistent with the enabling conditions for perceived usefulness identified in our results.

Our findings indicate that integration patterns are critical from multiple perspectives, including integration challenges, partner alignment, and regulatory context. Drawing on evidence about partner alignment and canonical identifiers, a single identifier should be used across all systems (e.g., Warehouse Management System (WMS), Transport Management System (TMS), and RTV), supported by simple API or Electronic Data Interchange (EDI) connections, and procedures should be documented to ensure updates to labels, scanners, and dashboards remain synchronised.

Our study indicates that adoption varies by product type (e.g., lamb) and firm role, and that benefits are realised when context, capabilities, and value fit. Governance and access mechanisms, informed by role and demographic effects on adoption, should include straightforward data-sharing agreements specifying who can view data, retention periods, and audit rights, with role-based controls to protect frontline privacy while meeting compliance requirements. Pilots should focus on areas with the greatest potential value, guided by product-context effects observed in the study, such as products or transport routes with high spoilage or claim rates (often lamb or long-haul chilled), targeting metrics like a ≥20% reduction in temperature deviations and ≥30% faster resolution of exceptions.

The findings also highlight the role of frontline versus managerial staff and perceived costs and benefits. Therefore, people and workflow considerations, reflecting differences in cost/benefit distribution across roles, are essential: combine frontline training with clear job aids, such as SOPs and exception-handling guides, and provide managers with dashboards tracking key performance indicators such as excursions per 1000 pallets, on-time temperature compliance, and average resolution time for exceptions.

Finally, the study emphasises the importance of measuring adoption, exception resolution, and moderators such as regulatory frameworks. Accordingly, scaling and sustaining the initiative, consistent with the configurational patterns of adoption identified in our findings, involves extending to upstream and downstream partners, keeping the MVDS stable, adding only fields linked to actionable decisions, and tying incentives to shared Key Performance Indicators (KPIs), such as bonus–malus schemes for excursion rates. Practical tools to support this approach include a checklist for the MVDS, a sample Responsible, Accountable, Consulted, Informed (RACI) framework for temperature exceptions, Service-Level Agreement (SLA) documents for data-sharing, and a ROI calculator to assess shrink reduction, claim avoidance, and integration costs.

6. Conclusions

This study provides one of the first comprehensive, empirically grounded analyses of the factors influencing the adoption of RTV technologies in the meat CSCL sector, with a particular focus on the Australian context. By integrating constructs from TAM with contextual variables such as data-sharing and horizontal collaboration, we offer a more holistic understanding of how both individual perceptions and organisational arrangements shape RTV adoption. Six key factors identified from the literature were empirically assessed for their facilitating or hindering impact on RTV adoption. The findings reveal that all six—lack of infrastructure, concerns regarding data accuracy and reliability, complexity of regulatory compliance, enhanced quality control, regulatory compliance pressures, and concerns about data security and privacy—have a statistically significant influence on adoption decisions. Interestingly, while several of these factors act as barriers, enhanced quality control and regulatory compliance also emerge as positive motivators for adoption, indicating that some challenges simultaneously serve as enablers depending on context.

In examining the interplay between perceived ease of use, usefulness, and attitudes toward RTV technologies, the study confirms that these perceptions are shaped by both drivers and barriers, consistent with the Technology Acceptance Model. Higher perceived barriers were associated with more negative user attitudes, while the presence of drivers corresponded with more favourable perceptions and intentions to adopt RTV technologies. These perceptual dynamics were further moderated by collaborative mechanisms. Data-sharing showed the strongest mediating effect at the inventory stage, where its presence significantly reduced the negative impact of perceived barriers. Horizontal collaboration also demonstrated the ability to attenuate barrier effects, most effectively at the inventory stage, moderately in transport/logistics, and minimally in vendor–retailer operations. These findings underscore the importance of stage-specific strategies in facilitating successful RTV adoption.

The results highlight the need for targeted interventions tailored to different stages of the cold supply chain. Data-sharing mechanisms and horizontal collaboration are not universally effective; rather, their impact varies according to operational context. At the inventory stage, these mechanisms were most influential, suggesting that early integration and information exchange are critical for overcoming resistance and uncertainty. In contrast, downstream stages such as vendor–retailer operations exhibited weaker mediation effects, indicating the need for alternative strategies at those points.

Beyond statistical significance, our findings explain why RTV takes hold: when early-stage collaboration/data-sharing standardise identifiers and responsibilities, perceived usefulness is realised despite intrinsic barriers; where infrastructure and data governance are weak, ease and usefulness remain latent. Adoption is therefore context-dependent—varying by supply chain stage, firm role, product type, and regulatory incentives—and is best advanced through targeted, staged implementation that couples a minimal data standard with clear governance, role-specific workflows, and outcome-based metrics.

This study contributes to the literature by developing and empirically testing a novel conceptual framework that integrates collaboration and information-sharing as moderating mechanisms in RTV adoption—an area that remains underexplored, particularly within the Australian meat cold supply chain context. The findings offer actionable insights for key industry stakeholders. Logistics managers and supply chain operators can use the results to identify and prioritise the most impactful enablers and barriers in their operations. Technology providers and integrators may tailor their solutions and support services to address specific cold chain challenges, particularly those related to infrastructure, compliance, and data-sharing. Exporters and meat processors can leverage collaborative networks to enhance visibility, traceability, and compliance—critical factors for accessing and maintaining export market standards.

From a policy perspective, the research provides an empirical foundation for developing initiatives that promote RTV adoption through incentivised data-sharing platforms and open technology standards. Policymakers may also consider co-funded demonstration projects or industry partnerships to lower adoption barriers, especially for small and medium-sized enterprises operating in the perishable goods sector.

Like all empirical studies, this research has several limitations. First, the results derive from a cross-sectional survey using self-reported measures, which constrains causal inference and may introduce desirability or recall bias. Some subgroup estimates—such as the oldest age band or product sub-segments—have limited precision due to small cell sizes. While we theorised interactions and observed stage-contingent attenuation patterns consistent with moderation, not all potential interactions were estimated with full statistical power. Additionally, the focus on Australian meat CSCL may limit the generalisability of findings to other contexts, regions, or industries. Moreover, contextual characteristics such as company size, product variety, geographical location, and levels of digital maturity were not directly incorporated in the present analysis, even though prior studies suggest these factors may influence collaborative practices and technology adoption outcomes.

Future research could address these limitations in several ways. Longitudinal field experiments or quasi-experimental rollouts could better estimate causal impacts of collaboration and data-sharing on barrier salience and the realisation of RTV usefulness over time. Comparative studies across regions, industries, or product types (e.g., beef versus lamb) would help validate generalisability and examine lane-level economics. Moreover, examining how contextual characteristics—such as company size, geographical location, product variety, and levels of digital maturity—shape the effectiveness of HC and DSH practices would provide a richer understanding of adoption dynamics. Linking these contextual influences to sustainability outcomes, such as reduced food waste, optimised transport efficiency, and lower CO₂-equivalent emissions, would also advance knowledge of how RTV can contribute to the broader resilience and sustainability performance of meat supply chains. Further investigation into emerging technologies—such as blockchain, digital twins, IoT analytics, and Artificial Intelligence (AI)-powered systems—may provide additional insights into overcoming persistent adoption barriers. Studies could also explore regulatory heterogeneity across jurisdictions and integrate environmental outcomes (e.g., CO₂ equivalent from avoided waste) into the business case for RTV. Finally, qualitative research involving in-depth case studies or interviews may uncover nuanced organisational, cultural, or workflow factors influencing adoption.