Effectiveness of food quality and safety management systems in Oman’s food supply chain

doi:10.21203/rs.3.rs-3867358/v1

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Effectiveness of food quality and safety management systems in Oman’s food supply chain

https://doi.org/10.21203/rs.3.rs-3867358/v1

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This study has offered an extensive analysis of the effectiveness of Oman’s food supply chain’s quality and safety management. The study focused on the complex relationships among several variables, such as information technology, food traceability, supply chain risks, performance measures, and food safety regulations, and how these elements all work together to shape the efficiency of the food supply chain. The research examined data gathered from middle- and top-level managers in Oman’s food supply chain using a practical research strategy that combined quantitative and qualitative approaches and employed structural equation modelling to investigate the interconnections and effects on the performance of the supply chain from the previously listed factors. The key findings of this research have demonstrated the vital significance of strict food safety measures by revealing a substantial positive association between food safety standards and the performance of the food supply chain. Information technology has been found to be a significant catalyst for improving regulatory compliance and supply chain performance. The mediation study has highlighted how information technology is interdependent with other elements and demonstrated how it supports the robustness, agility and resilience of supply chains. This study further established that improving food safety standards, utilising cutting-edge information technology to enhance compliance and traceability, implementing thorough risk management plans, encouraging cooperation among supply chain participants and raising consumer knowledge of food safety are important suggestions to improve public health and economic growth. These suggestions are meant to assist Oman’s food supply chain in being robust, effective and safe. The research provided valuable insights for supply chain professionals, policymakers, and researchers who are interested in food safety and supply chain management, especially in the context of Oman and other GCC countries.

Health sciences/Diseases

Health sciences/Health care

Health sciences/Risk factors

Physical sciences/Energy science and technology

food quality

safety management systems

Oman’s food supply chain

regulatory compliance

traceability

quality and safety management

“Food is essential to life; hence food safety is a basic human right. Billons of people worldwide are at risk of unsafe food. Many millions become sick, while hundreds of thousands die yearly”¹. Food safety is a major concern as billions of people worldwide are prone to consuming unsafe food, which causes millions to become ill and hundreds of thousands of deaths each year². Safe food is critical for preserving human lives, promoting personal and public health and boosting economic growth in regions that prioritise food safety³. Achieving a safe food supply chain requires a combination of reliable scientific research and equitable law enforcement⁴. With technological innovations, new regulations must be implemented to ensure the continued availability of safe and healthy food products. As living standards continue to rise, the importance of food safety and the risk of potential contaminants remain serious health concerns^5,6. Food quality and safety management systems are critical components of food supply chains. They are specifically designed to ensure that food products are safe, nutritious and of high quality from production to consumption⁶. In Oman, the government has established several regulations and guidelines to ensure food safety and quality. However, it is important to evaluate the effectiveness of these systems to ensure that the food supply chain is free of potential hazards.

Recently, the significance of food quality and safety management systems has increased because researchers have discovered a strong link between the effectiveness of these systems, the success of supply chain management and firm performance⁷. Therefore, it is critical to implement effective supply chain quality management to achieve high-quality food production. This ensures that products meet or exceed customer expectations, leading to increased satisfaction, loyalty and repeat purchases⁸. Supply chain management systems are critical to the success of any industry. Effective supply chain management ensures that businesses can meet customer demands while remaining efficient and profitable⁷.

Hong et al. and Soares et al. called for the implementation of co-regulation in the food industry to ensure the safety of the nation’s food supply⁷. Co-regulation involves collaboration among stakeholders to establish industry standards and ensure that companies are held accountable for any activities that may be detrimental to the society and the environment⁹. The implementation of co-regulation in the food industry will have a positive impact on the safety and quality of the food supply, ultimately benefiting the consumers⁷. Co-regulation also ensures that businesses in the food industry operate responsibly, which further helps to maintain a sustainable industry in the long term. By adopting a collaborative approach to food safety and quality management, the food industry can build trust with consumers and establish itself as a reliable source of safe and nutritious food^7,9,10.

Masengu and Mangwenda have emphasised the need for an effective Hazard Analysis and Critical Control Points (HACCP) system in food safety management¹⁰. Fotopoulos et al. concentrated on HACCP company components such as programs, equipment, authentic systems, commitment and training, whereas Govender, and Masengu and Mangwenda have investigated the aspects of identifying, assessing and controlling food safety hazards in various environments¹⁰. The researchers concurred that monitoring the macro- and micro-environments is essential for effective food safety management in businesses. Similarly, Maiberger and Sunmola, and Nguyen and Hoang emphasised the importance of taking culture, inspection, verification, knowledge, legal regulation and necessary programs to manage food safety¹¹.

In the European Union (EU), food quality and safety management systems are a significant concern. Recently, the focus has been on creating a harmonised approach to implement the EU’s requirements for Good Hygiene and Practice (GHP) and HACCP procedures⁹. This approach aims to establish a common understanding of food safety, align it with the international standards, conduct audits and follow-up measures, and provide audit plan templates and checklists to support HACCP audits^7,10. These factors are essential for ensuring the necessary quality standards of food products and allowing for checks and balances of any shortcomings in the supply chain. EU regulations on food quality and safety management systems are crucial for this discussion. Regulation (EC) No. 852/2004 of the European Parliament and Council makes it mandatory for food operators to implement procedures based on the HACCP principles¹². It covers holistic preventive and emergency arrangements within the EU, self-checks to ensure food safety, adequate cleaning, disinfection and personal hygiene to ensure safe and appropriate food. Similar studies have critically looked at food safety across the globe¹³ focusing on quality assurance in the food industry, Good Manufacturing Practices (GMP), HACCP, International Organization for Standardization (ISO) standards^4,9, sectorial advocacy, supervision, instruction and training on food hygiene issues¹⁴, and certification of producing companies to ensure compliance. These scholars agreed that achieving the desired level of food quality necessitates a tailored approach and assessment of the extent to which each system contributes to overall product quality and balances the tools used to achieve quality and safety goals along the supply chain.

Realising the effects of food safety risks, Gulf Corporation Council countries came up with a unified food standard. These rules and regulations are in line with the FAO’s Codex Alimentary which is benchmarked for international practice³. The standards cover a wide spectrum of biotechnologies, novel foods, food-colouring agents and other food additives. The Gulf standards (G/S)9/1995 and 150/1993 safeguard shelf-life standards for food items. The Oman Ministerial Decree No. 74/2000 covers labelling of food and safety, labelling information on food and food products, and ensures that food products shall not contain alcohol, pork, lard or their derivatives (International Trade Administration, 2021 ). The Sultanate of Oman has placed great emphasis on benchmarking food safety to international standards. The country has adopted ISO 2200.2018, which specifies interactive communication, system management, pre-requisite programs and the use of HACCP principles to guarantee food safety in supply chains¹⁵. In addition, Oman has established the Food Safety and Quality Centre¹⁶ to ensure food safety across different sectors in Oman. It has been maintained that the enactment of regulations and the implementation of ISO 22000.2018 enable food producers to identify expected hazards that occur in the supply chain because of food safety¹⁷. Despite this regulation, the Sultanate of Oman faces a plethora of food safety issues. According to Zainab¹⁵, citing Dr Hussain al Masroori, Director General of the Food Safety and Quality Centre, during Food Safety Conference, “The strategic objective is to ensure food safety and quality in Oman; however, Oman faces challenges in the form of lifestyle changes, food consumption, an increase in unhealthy handling practices and the emergence of biological and chemical risks.” It is pertinent to note that, due to the challenge of food safety and quality issues, stakeholders need to enhance consumer awareness and generate a sense of responsibility for producers at all stages of production^5,18 to ensure conformance with international standards.

Pertinent to this research is the interesting discourse on food safety and risk. The Sultanate of Oman recently recorded seizing food items that did not follow safety standards and regulations. Companies have also been caught tempering with labels, manufacturing and expiry dates¹⁹. The Directorate General of Health Affairs’ statistics indicated that quantities of imported food items were not suitable for human consumption. On several occasions, the Directorate General of Health Affairs ordered the closure of shops, flouting health regulations. According to the World Health Organization (WHO), unsafe food contains harmful bacteria, viruses, parasites and chemical substances that cause more than 200 diseases, including diarrhoea and cancer²⁰. Food safety has become important in the context of changing food habits, mass catering and globalisation of the food supply. This calls for the need to strengthen food supply chains in and between countries to guarantee that food safety and risks are eliminated along the supply chain¹⁹.

A significant analysis and discussion of food safety risk has been conducted by the ²¹World Health Organization Foodborne Disease Burden Epidemiology Reference Group (WHOFDBERG), which shows that 31 food safety hazards caused 420,000 deaths and 33 million disabilities in 2010. Additionally, the key bacterial, viral and protozoal causes of diarrhoeal diseases were food-borne²¹, and contaminated food has been neglected by the global community²². Central to food safety and risk is the concept that food providers in any country need regulatory intervention and quality assurance before purchase (WHOFDBER, 2015). Processing of food, milling, mixing, bottling and baking create pathways for contamination, hence the need to have the best practices in food safety. This study aims to evaluate the effectiveness of food quality and safety management systems in Oman’s food supply chain by examining the current regulations and guidelines in place, their implementation and their impact on ensuring the safety and quality of food products. Furthermore, this research will identify gaps or areas for improvement in existing food quality and safety management systems.

Figure 1: Conceptual model on the factors that improve food supply chain performance

Measurement scales and their sources

Table 1 presents the measurements and their sources in which study used to develop the model constructs and measurements. The next section reflects the model hypothesis development.

Table 1

Measurement scales and their sources
Item Code	Description	Sources of Questions
Risk in food supply chains
FR1	Natural events like tsunamis	^23–26,31
FR2	Financial crisis such as exchange risks
FR3	Labour disputes
FR4	Inadequate and improper storage
FR5	Wrong temperature levels
Food safety requirements
FS1	Security of the food source	^32,37,38,40
FS2	Independent and public health judgement
FS3	Concrete action in the enforcement regulations
FS4	Process control
FS5	Training and education
Food traceability
FT1	Traceability technology adoption	^68–73,79
FT2	Traceability accuracy systems
FT3	Recall management efficiency
FT4	Quality control
FT5	Block chain
Information technology
IT1	Inventory management systems	^{11,45,47,50,52,59–62}
IT2	Supply chain visibility
IT3	Demand forecasting and planning tools
IT4	Transportation technology
IT5	Logistics technology
Performance measures
PM1	Risk analysis and risk investment	^{45,48,50–53,55}
PM2	Risk investment
PM3	Flow of information between supply chain partners
PM4	Customer satisfaction
PM5	Revenue indicators
Regulatory compliance
RC1	Audit findings	^80,83,84,86
RC2	Regulatory compliance score
RC3	Adherence to Food Safety Standards
RC4	Legal compliance costs
RC5	Compliance measures

Hypothesis development

H₀1: Food supply chain risks do not impact Oman’s food supply chain performance.

H₀2: Food safety requirements do not impact Oman’s food supply chain performance.

H₀3: Performance measures in food supply do not impact the performance of Oman’s food supply chain.

H₀4: Information technology does impact the performance of Oman’s food supply chain.

H₀5: Food traceability does not impact Oman’s food supply chain performance.

H₀6: Regulatory compliance does not impact Oman’s food supply chain performance.

Risks in food supply chain and food supply chain performance

Food supply chain management is an old term that describes the systematic connection of different actors, people, activities, technologies, information and resources in the production and distribution of food²³. It’s a combination of different specialisation and logistical steps and includes such phrases as ‘farm to fork or paddock to plate’²⁴. Risks in the food supply can be seen as disruptions that hold or deviate from the food distribution outcome. According to Gurtu and Johny²⁵, risk management in supply chains incorporates strategic development and implementation of plans to manage supply chain networks, continuous risk assessment²⁶, reducing supply chain vulnerability²⁷ and ensuring supply chain resilience²⁸. The hype of international trade, regional integration and technology adoption has exposed food supply chains and distribution centres to incur increased costs^26,29,30. These risks negatively impact a country’s food supply chain performance. The food supply chain operation has complex risks due to a large number of actors^25,31. Risk sources include varying qualities, stock restrictions, regulation and traceability aspects at each level³².

Global disruptions such as the COVID-19 container crisis³³, Gulf War and 11 September crisis²⁵ exposed the vulnerabilities of supply chains during uncertainties. Choi et al. state that to reduce the food supply chain risk, companies need to implement supply chain integration²⁸. The solution lies in designing fully integrated SC that are robust, responsive, efficient and competitive to avoid food supply chain disruptions³⁴.

Similarly, integration of the supply chain improves data interchange processes with customers and suppliers^26,29. It is important to note that SCI does have an incremental benefit to hedge against the opportunistic behaviour of SC members, geopolitical risks, sovereign risks and exchange rate risks which are prevalent in food distribution.

Companies have resorted to building storage facilities, production plants and distribution centres across their supply chains to mitigate food safety risks and integrate their systems. Scholars such as Goetschalckx et al. and Huang and Goetschalckx state that companies need to limit investments in the number of facilities but should carry out cost-benefit analysis between the investment and the cost associated with the disruption^35,36. These risks affect a country’s performance capabilities and food security. Therefore, investment in risk-mitigation tools and contingent planning is of paramount importance to food supply chain management and performance. Players in the food supply chain must understand that the longer a supply chain, the greater the risk of failure³². Consequently, the success factor lies in building a robust supply chain that can withhold the adverse effects of risks.

Food safety requirements and food supply chain performance

The implementation of food safety and quality requirements is not an issue of compliance but has implications for the health and safety of a nation. Conformance to food safety requirements has a positive impact on consumer confidence and global competitiveness³⁷. The ever-growing consumer demands³⁸, changes in consumer behaviour²⁴ and expansion of global trade have created an unprecedented need for food supply chains to have rigorous checks to identify sources of diseases and infections. Governments have a global responsibility to ensure that food security follows the expected standards. The concept ‘from field to table’ allows responsible actors to have full control over food production up to consumption²³. The EU has been instrumental in ensuring that actors in food production comply with the safety standards. For Meena et al. in Croatia and European Legislation, the first step of security is food safety at source, followed by sanitary correctness (food safety)³⁹. The EU calls for the establishment of effective communication across the supply chain to avoid information bottlenecks among actors⁴⁰. The EU emphasises that the key element in food safety in the supply chain is legislation enforcement and control. Countries need to have a regulatory framework that ensures quality and sets mandatory standards through inspection.

The most commonly used quality monitoring systems are the Global Food Safety Initiative, International Food Standards, International Organization for Standardization and Safe Quality Food^31,32. Some of the global scandals such as China melamine milk contamination⁴¹, 2008 and 2009, USA and Canada peanut butter contamination with Salmonella⁴², German bean sprouts contaminated with E. coli³⁸, Ireland pork contamination with dioxin in 2008⁴¹ and the existence of a high series of product recalls have eroded public confidence. These cases have led scholars to conclude that the number of tiers in the supply chain increases the risk of food contamination owing to the existence of interacting elements^37,41.

Food standards cannot be narrowed down to a specific country, but they need to be transparent, consistent and communicated with global supply chain partners to mitigate possible risks⁴⁰. Resultantly, compliance with food safety standards mitigates possible legal issues and positively influences a country’s brand image, market growth, profitability and operational efficiency⁴³. Retailers of food are crucial in promoting food safety in supply chains as they exist as conduits between producers and consumers⁴⁴. Food retailers have the last-mile capability and ability to enforce and reinforce compliance with food safety in the supply chain. Interestingly, the growing trend in global competition is forcing retailers to use food safety as a differentiating strategy for their brands³⁰. This aspect can be taken as a strategic move to build a competitive advantage by reinforcing quality standards in product packaging. It is important to note that the implementation of food quality standards comes with costs, including upgrading of facilities, training of employees and purchasing of necessary elements for quality checks.

Performance measures and food supply chain performance

Supply chain performance measures enable supply chain actors to oversee relevant performance indicators of products⁴⁵ and facilitate the setting of business objectives with periodic reviews⁴⁶. There is a growing concern that companies in the food industry face some challenges in establishing performance measurement systems⁴⁷ due to non-alignment of network goals⁴⁸. Notably, collaboration in the network is a crucial strategy in ensuring efficient performance measures.

The sustainability initiatives⁴⁹ and deteriorating manufacturing ethics ⁵⁰ have resulted in consumers looking for clarity in food integrity, safety and diversity. Furthermore, the use of pesticides and other dangerous chemicals have resulted in authorities demanding accountability of supply chain performance measurements⁵¹. Consumer opinions have been quite clear regarding a broad range of quality standards, shelf life of products and reliability⁵². Timely and accurate information are critical elements of supply chain management, and its success relies on the adoption of right metrics that accurately measure and motivate desired supply chain processes.

A notable aspect that complicates performance measurement in food supply chain is the variation of information prioritisation with each actor⁵³. Co-existence and co-operation of supply chain actors have a win-win result for performance measurements of supply chain actors. Uninterrupted flow of information, clear communication structure and recognition of each actor leads to supply chain success⁵⁴. Various scholars have pointed out that food supply chains lack adequate performance measures for benchmarking and strategic decision-making. Additionally, due to the lack of these performance measures and standardised performance measurement indicators, actors are always putting their resources in fulfilling their goals⁵⁵.

There are two types of performance measurement indicators, namely financial⁵⁶ and non-financial⁵⁷. However, most of the food supply chain players fail to differentiate the two. The complexity of these two remains a barrier in predicting performance measures for the actors. In the manufacturing industry, Childerhouse and Towill cited Li and Obrien model as the best performance measure to improve supply chain efficiency and effectiveness based on profit, lead time delivery, promptness and waste elimination⁵⁸. Some scholars also note that the Supply Chain Operations Reference Model (SCORM) provides an insight in measuring performance through measures of reliability, cost, responsiveness and assets’ utilisation.

The impact of information technology on food supply chain performance

The impact of information technology on food supply chain performance has become important in recent years (2015–2023). How to effectively incorporate information technology to enhance strategic purchasing, logistics’ integration and overall supply chain is also an ongoing debate. The food industry has been marred with uncertainties that have impacted on countries’ food security across the world. A study by Wong et al. reveals that incorporation of information technology has a significant impact on both external and internal operational characteristics of a supply chain⁵⁹. In that regard, the study concluded that integration of information technology improves firms’ ability and performance. Another comprehensive review by Nguyen and Hoang further concluded that the companies in the food industry have been adopting technology at an unprecedented rate to remain competitive¹¹.

Research has also concluded that food supply chain viability relies on the correct gathering and usage of information and technology tools in improving supply chain performance^60,61. Ideally, information technology should have the capability to process, interpret, store and filter information across the different actors of the supply chain. This would enable the dissemination of accurate, high-quality information that can help mitigate risks and reduce uncertainties⁶². Adoption and usage of information technology in food industry acts as a key differentiator against competitors⁵⁹.

In his seminal research, Wedel devoted some attention on the capacity of organisation to gather data using different information technology tools that can facilitate the ability to be agile and make pro-active decisions⁶³. One of the most cited studies is that of Han and Dong, which claims that the collection of data by companies facilitates every stage of the supply chain to gain access to the latest trends and benchmark their operations with international standards⁶⁴. This data is essential as it allows individuals to measure performance and facilitates collaboration and coordination of supply chain actors. The comprehensive reviews by Han and Dong, and Huang et al. concluded that the availability of data among the members of the supply chain is vital in making sure that there are uninterrupted linkages that minimise operational costs^64,65.

Farhan Basheer et al. have also conducted extensive research on the role of information technology in food supply chain management⁶². Their studies have proven that information technology helps in the dissemination of data at a faster rate, reduces lead times and risks among actors. Information technology is critical in improving and maintaining food standards, and it also allows the food companies to integrate with suppliers and customers. The integration is noted by Koçoǧlu et al.⁶⁶, who further assert that the adoption of information technology removes information bottlenecks, increases response to supply chain emergencies, helps to identify problems⁶⁷, and allows the efficiency and effectiveness of the supply chain^60,61. Nguyen and Hoang conclude that investment in information technology is fundamental since it allows internal integration of the organisation and is critical in strengthening the relationship amongst supply chain actors¹¹.

Food traceability impact and food supply chain performance

Food traceability is “the ability to trace the movement of food through specified stages of production, processing, and distribution”⁶⁸. Scholars such as Narsimhalu et al. have proposed that companies in the food industry must have an effective traceability system to detect products that have a short shelf life and high risk of contamination⁶⁹. Examining this issue, Rahman et al. state that to guarantee food traceability, organisations must note three components, namely input traceability, process and output traceability, as products move along the supply chain⁷⁰. These three components are fundamental in reducing contamination which can happen as food items move from one actor to another.

A key study on the halal food industry reported issues regarding traceability⁷¹. Concerns have been raised regarding exact halal requirements as slaughtering methods differ, posing a challenge in implementing a comprehensive traceability system⁷⁰. Interestingly, a study has raised concerns regarding the mislabelling of chicken as ‘halal’ by Kentucky Fried Chicken, known as KFC⁷².

Establishment of the traceability system is not easy especially in the food industry. Traces of pork DNA reported in some food products have led Muslim consumers to call for strict traceability of the supply chain⁷³. Establishment of an integrated traceability system has the capacity to track food products in real time. According to one study, supply chain control should be implemented to increase supply chain visibility, allowing for continuous monitoring and reporting of all actors’ activities⁷⁴. Although there is much debate among academics and industry experts about implementing traceability systems, the majority of the scholars concur that most consumers do not emphasise food traceability^75,76.

Moreover, the traceability system improves quality of food by allowing detection of product failures⁷⁷. In the livestock industry, identification and meat traceability systems have been adopted to reduce mislabelling and provide accurate information to the consumers⁷⁵. It is also worth noting that these systems are crucial for responding to changes in consumer expectations and improving company loyalty^78,79 as well as reducing risks, ensuring safe consumption and lowering medical costs. Most of the companies have tried to harmonise traceability systems by implementing technology ranging from alphanumerical code, barcode and RFID to internet-based tracking and tracing products¹¹.

Regulatory compliance impact and food supply chain performance

In the last decade, governments across the globe have struggled to develop regulations that can be used to ensure food safety in the supply chain so that consumers’ health is guaranteed. This is because the food supply chain has become interconnected and difficult to visualise. Safety concerns and lack of proper regulations are some of the key issues that are raised for producers, manufacturers and retailers in the food industry. The World Trade Organization (WTO) is tasked with overseeing food safety concerns in global trade. In the last decade, (2010–2020) the food industry had gone through significant challenges due to lack of robust regulations and pro-active policies at governmental levels. De Oliveira et al. have emphasised the need to have effective innovations in the production process and reduced time and distance between production and consumption to minimise health risks⁸⁰.

Food-borne diseases pose a major challenge to health institutions worldwide. The WHO noted that unsafe food causes 600 million illnesses per year, food-borne diseases cause 420 million deaths (30% occur among children below five years of age) and 33 million lives are lost due to consumption of unsafe food²⁰. Regardless of the success in the implementation of food safety measures using HACCP in Argentina Fish processing plant⁸¹ and Yogurt plant in Brazil⁸², its universal application is still questionable and not applicable in some sectors.

The most recommended and WTO-approved food management systems used globally are the HACCP, GMP, GHP and Sanitation Standard Operating Procedures (SSOP)⁸³. The HACCP, which is considered as the most appropriate system in both industries, has gained a lot of traction across the globe⁸⁴. Consequently, governments have tried to set regulations, procedures and guidelines that enhance the successful implementation of the HACCP principles. The HACCP food management system consists of a set of activities to ensure food safety where health hazards are likely to occur⁸⁵. HACCP is recommended by the food industry for use in food production because it allows for checks and balances in all stages of food production (from raw materials to final production).

Researchers have recommended the application of HACCP principles through the use of perquisite procedures of GMP, GHP and SSOP which are considered as good housekeeping and preventive measures⁸⁶. GMP and GHP implementation focuses on managerial commitment, staff specialisation, motivation, resources and communication of the program.

Food safety remains a concern across the globe and some scholars have noticed that the food safety systems such as HACCP, SSOP, GMP cannot be applied across all food industries. A study has presented that the application of these principles was especially difficult for small butchers⁸⁷. Another study noted the same difficulties in implementing these principles in the dairy industry due to limitations in time, resources, money and training⁸². Ulusoy Sözen et al. noted that in most cases, employees are not aware of the objectives of food safety principles⁸⁸. Toropilová & Bystrický indicated that no company can implement HACCP without proper training⁸⁶. Ako et al. state that both large and small companies have challenges in implementing food safety⁸⁵.

Research paradigm and methodology

This study applied a pragmatic research paradigm that involved the use of both quantitative and qualitative research approaches in the collection and presentation of results⁸⁹. Due to its robustness and objectivity, the quantitative approach by Saunders et al. was applied in designing the questionnaire and collecting and analysing the data while the qualitative approach was applied in the establishment of the sampling techniques and methods. Data was collected mainly in the cities of Muscat and Sohar, where the major food supply chain centres of Oman are. The targeted population consisted of middle- and top-level managers in the food supply chain across retailing, manufacturing, third-party logistics and other industries. The study used a structured questionnaire with a Likert scale ranging from 1 (disagree) to 5 (strongly agree).

The research results’ presentation, analysis and discussion provide an overview regarding the understanding of food quality and safety management systems in the Sultanate of Oman. This section provides an insight into the participants’ characteristics, which include age, gender, level of education, professional level, geographic location, industry type and years of experience. The study used structural equation modelling using SMART PLS 4. The research constructs and measurements were based on the following independent variables: risk in the food supply chain, supply chain performance measures, supply chain food safety requirements, supply chain regulatory compliance and supply chain food traceability. The mediator of the study was adoption of technology, and the dependent variable was food supply chain performance.

Demographic characteristics

The quality of food products is important for the wellbeing of a country, and it remains one of the most important aspects of consumer consumption studies. In the context of Omanis, their culture revolves around food; therefore, understanding food safety requirements is a national priority. The study of gender distribution indicated that 55% were females, while the remaining were males. These results are not surprising but relevant since they show the dominance of females in food logistics and the supply chain in Oman. The age characteristics of participants indicated that they were drawn from varying career stages within the industry. Most of the participants fell in the age bracket of 36–45 years (40%), followed by the age brackets of 26–35 years (30%), 46–55 years (20%) and 55 years and above (10%). This showed an indicative mixture of age groups capable of providing different insights into food safety supply chains in Oman. The geographical location of the participants indicated that 60% were mainly from Muscat (the hub of Oman’s food industry), 25% from Sohar (the second logistics hub) and 15% from other areas. Food safety management is usually adopted and implemented by top management. The study participants (70%) held top-level management positions, while 30% held middle-level positions. Top- and medium-level managers play an important role in enforcing food safety in supply chains. The study was also inclusive since participants were drawn from the manufacturing industry (45%), retail (25%), 3PL (20%) and other industries (10%). There was also a professionally balanced distribution related to food safety decision-making, with 40% quality control specialists and 60% supply chain managers drawn from 50% medium-food industry and 50% large-scale food industry. The study utilised social media networks, industry connections and other logistics and supply chain platforms to identify the participants. The study used a sample of 268 participants based on the Krecie and Morgan calculation of sample size, which is enough for a population of 57,000 in the Oman logistics industry.

Path analysis, R-square, F-test, construct reliability and validity, factor analysis, direct and indirect relationships, and hypothesis testing were conducted. The next section reflects on the path analysis.

Path analysis

This study used path analysis (Table 2) to understand the comparative strengths of direct and indirect relationships^90,91, such as food supply chain performance, food traceability, information technology, regulatory compliance, food safety requirements, performance measures and risks in the food supply chain. Food safety requirements and food supply chain performance revealed a positive value of 0.366. This result is consistent with the findings of Hong et al., who highlighted the importance of food safety regulations in improving supply chain performance⁷. Additionally, food safety requirements and food traceability show a weak positive relationship, as evidenced by the value of 0.284. This implies that enhancing food safety requirements by one unit will result in a subsequent increase in food traceability by a value of 0.284. Interestingly, improving food safety requirements by one unit will result in a decrease in regulatory requirements, as evidenced by the correlation coefficient value of -0.217. This matches the findings of Gurtu and Johny²⁵, who emphasised the significance of regulation adherence in reducing supply chain hazards. If food safety requirements are increased by one unit, risks in the food supply chain will also go up by a value of 0.243, implying that the relationship is positive but weak. Food traceability and regulatory compliance are regarded as substantially strong, as evidenced by the value of 0.580. This value implies that by increasing food traceability strategies by a unit, regulatory compliance will subsequently be increased by a value of 0.580. This supports the argument of Soares et al. (2017) regarding the significance of traceability in conforming with regulatory standards. Traceability and food supply performance metrics have a moderately strong correlation (Path coefficient: 0.148). This result is consistent with the observations of the role of traceability in supply chain performance presented by Rahman et al⁷⁰. The correlation between food traceability and risks in food supply chain is weak and negative, as evidenced by the value of -0.241. This implies that an increase in food traceability by one unit is associated with a decrease in risks in the food supply chain by a value of -0.241. An effort to increase strategies for information technology by one unit will result in a weak increase in performance measures by a value of 0.094. There is no direct path between regulatory compliance and food supply chain performance. The next section reflects of R-square results.

Table 2

Path analysis
	Food supply chain performance	Food traceability	Information technology	Regulatory compliance
Food safety requirements	0.366		0.284
Food traceability	0.046		0.131
Information technology	0.405
Performance measures	0.293	0.148	0.094	0.193
Regulatory compliance	-0.217		0.580
Risks in food supply chain	0.243		-0.241

R-square

To assess how the independent variable (predictors) explains the independent variable (outcome)^92,93, the study used R-square (Table 3). R-square is applied for co-efficient determination⁹⁴ or goodness of fit of a regression model^95,96. The higher R-square or high-adjusted R-square, the general rule is that the model is regarded as having predictive power⁹⁷. Results show a value of 0.795 for food supply chain performance implying that approximately 79.5% of the variance in the observed latent variable is explained by independent variables. This high R-square value suggests that the model has a significant explanatory power, especially when addressing the application of technology, regulatory compliance and food safety regulations. The outcome is consistent with the observations made by Hong et al.,⁷ who highlighted the complex interplay between both internal and external forces on supply chain performance. However, food traceability (0.022 and 0.018), information technology (0.450 and 0.439) and regulatory compliance (0.037 and 0.034) have lower R-square values than those of food supply chain performance. Although these variables explain less of the variation in food supply chain performance, they are important in the overall explanation of the multi-variate regression model. This is aligned with the findings of Rahman et al.⁷⁰ and Guan et al.⁶⁰ regarding food traceability, information technology and regulatory compliance. The next section presents construct reliability and validity.

Table 3

R-Square
	R-Square	R-Square Adjusted
Food supply chain performance	0.795	0.790
Food traceability	0.022	0.018
Information technology	0.450	0.439
Regulatory compliance	0.037	0.034

Construct reliability and validity

To determine construct reliability and validity, this study used Cronbach’s alpha, Rho_A, composite reliability and average variance extracted (AVE) presented in Table 4. Using the rule of the thumb⁴⁵, values that are > 0.7 show high reliability, while Rho_A values closer to Cronbach’s alpha show internal consistency. The values obtained from any study (Table 4) range show stronger internal consistency, marked by higher values (composite reliability 0.7), further confirming that the reliability of the constructs is high (AVE > 0.5 is considered valid). As highlighted by Hong et al.⁷, the literature places a great emphasis on food safety, which is consistent with this study’s strong internal consistency and validity. The values in this study were above the threshold (0.7), ranging from 0.836 to 0.956, suggesting that the items are a reliable measure (Cronbach’s alpha), and Rho_A had values ranging between 0.844 and 0.958, thereby confirming that there was strong internal consistency of the items selected in the study. In terms of composite reliability, the values obtained in this study were above 0.7, further confirming that the reliability of the constructs was high, and the AVE results ranged from 0.603 to 0,850, which are acceptable and generally good. The discriminant validity was calculated using the Fornell-Larcker criterion and the heterotrait-monotrait (HTMT) ratio.

Table 4

Construct reliability and validity
	Cronbach’s Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)
Food safety requirements	0.891	0.899	0.920	0.696
Food supply chain performance	0.933	0.934	0.949	0.789
Food traceability	0.836	0.844	0.883	0.603
Information technology	0.936	0.936	0.951	0.796
Performance measures	0.956	0.958	0.966	0.850
Regulatory compliance	0.884	0.885	0.916	0.685
Risks in food supply chain	0.885	0.888	0.916	0.686

Discriminant validity

The Fornell-Larcker criterion and HTMT ratio are widely used to measure distinct constructs in structural equation modelling (SEM)⁹⁸. The rule is that HTMT values should be less than 0.9 for discriminate validity, and for the Fornell-Larcker, the correlation of each paired construct should be lower than the square root of the AVE of each construct⁹⁹. The discriminant validity of latent constructs in SEM or confirmatory factor analysis (CFA) was assessed using the Fornell-Larcker criterion^100,101. The results in Tables 5 and 6 satisfy the requirements of discriminant validity, as it is higher than all its correlation values. The results confirm the recommendation from Ab Hamid et al. that the Fornell-Larcker criterion and HTMT ratio can be used in measuring the discriminate validity to increase the robustness of our data before proceeding with the factor loading¹⁰⁰.

Table 5

Fornell-Larcker Criterion
	Food safety requirements	Food supply chain perfomance	Food traceability	Information technology	Perfomance measures	Regulatory compliance	Risks in food supply chain
Food safety requirements	0.834
Food supply chain perfomance	0.758	0.888
Food traceability	0.129	0.254	0.777
Information technology	0.401	0.597	0.334	0.892
Perfomance measures	0.507	0.656	0.148	0.264	0.922
Regulatory compliance	0.348	0.370	0.369	0.605	0.193	0.827
Risks in food supply chain	0.619	0.623	0.256	0.347	0.434	0.583	0.828

Table 6

Heterotrait-monotrait ratio (HTMT)
	Food safety requirements	Food supply chain performance	Food traceability	Information technology	Performance measures	Regulatory compliance
Food supply chain performance	0.823
Food traceability	0.150	0.285
Information technology	0.431	0.639	0.374
Performance measures	0.547	0.696	0.162	0.276
Regulatory compliance	0.387	0.408	0.434	0.663	0.210
Risks in food supply chain	0.689	0.685	0.301	0.379	0.473	0.659

The square root of the AVE for every construct in our research was greater than its correlations with other constructs, which is in line with the Fornell-Larcker criterion. For instance, ‘food safety requirements’ showed a larger square root of AVE than its associations with ‘food supply chain performance’ or ‘information technology’. This result is significant because it confirms the unique nature of food safety requirements, which is different from general supply chain performance or technology elements. This is also corroborated by other existing research⁷.

Each pair of constructs had HTMT ratios that were less than 0.9. This result validates the distinctive benefits of these elements to the food supply chain as stated in the literature by researchers such as Soares et al. and supports the idea of heterogeneity between conceptions such as ‘food safety requirements’ and ‘regulatory compliance’.

Our research’s effective discriminant validity verification highlights the distinct contributions made by every construct to improve our comprehension of Oman’s food supply chain. This is aligned with the literature’s attention to the complexity of food supply chains and the different roles that variables like performance, safety and compliance play, as shown by Wiengarten et al³¹. The next section presents factor loading.

Factor Loading

Factor loading is used to show the strength and direction of the relationship between the observed variables and their corresponding latent variables. High values of factor loading (Table 7) values that are close to 1.0) suggest much stronger relationships that exist between the items and the latent variables they are measuring, and this suggests that the items will be good indicators of the latent construct. Food safety requirements show high values for FR3 and FR4 (0.884 and 0.886, respectively), and FR1, FR2 and FR5 (0.797, 00793 and 0.803, respectively) indicate a strong relationship that exists between these factors and the latent variable they are measuring. The importance of thorough and reliable food safety measurement that has been highlighted in research^7,10 is reinforced by this finding. The food supply chain performance for FS1, FS2, FS3, FS4 and FS5 ranges from 0.817 to 0.907, and these values are an indication of a strong relationship between the items and the latent variable (food supply chain performance) they are measuring. This finding matches the existing literature’s discussion of the complicated aspect of supply chain performance, as presented in the work of Wiengarten et al³¹. Results from the study further establish that values of factor loadings for food traceability range from 0.717 to 0.836, which is a good indication of a reasonable and acceptable relationship between the items and the latent variable they measure.

Table 7

Factor analysis
	Food safety requirements	Food supply chain performance	Food traceability	Information technology	Performance measures	Regulatory compliance
FR1	0.797
FR2	0.793
FR3	0.884
FR4	0.886
FR5	0.806
FS1		0.907
FS2		0.901
FS3		0.817
FS4		0.907
FS5		0.904
FT1			0.758
FT2			0.836
FT3			0.819
FT4			0.747
FT5			0.717
IT1				0.913
IT2				0.898
IT3				0.828
IT4				0.914
IT5				0.905
PM1					0.930
PM2					0.936
PM3					0.872
PM4					0.930
PM5					0.940
RC1						0.756
RC2						0.858
RC3						0.858
RC4						0.816
RC5						0.845

Factor loading values for information technology ranged from 0.828 to 0.914, which shows a strong connection between the items and information technology. The strong factor loadings in this construct match the focal role of IT in the food supply chain, as suggested by Guan et al.⁶⁰ indicating that the items effectively capture the technological aspects relevant to the supply chain.

The relationship between the performance measures and the items used in the study to measure them was found to be strong, as indicated by the factor loadings’ values that ranged from 0.872 to 0.940. Regulatory compliance had a significant association with the items that were used in the study to measure it, as evidenced by the values that ranged from 0.756 to 0.858; these values are high as they are close to one (1.0). The next section reflects on the direct relationship and hypothesis testing.

Direct relationships and hypothesis testing

This section presents the study direct relationship and hypothesis testing (Table 8). The assessment of the difference between the means of two different groups is done by using T-statistics. The higher the T-statistic value, the greater the evidence against the null hypothesis. The T-statistic value (4.417) for the difference between the mean values of food supply chain risks and supply chain performance is high. Gurtu and Johy²⁵ point out that food supply chain risks are complex because Nyamah et al.³² contend that partners in supply chains are numerous, posing greater risks. Food safety requirements and food supply chain performance have a T-statistic value of 7.978, which is high enough to go against the null hypothesis. Enhancing food safety requirements positively affects the performance of food supply chains. This is in accordance with Hong et al.⁷, who emphasised the critical influence that food safety has on the entire effectiveness of the supply chain.

Table 8

Direct relationships and hypothesis testing
	Standard Deviation (STDEV)	T-Statistics (\|O/STDEV\|)
Food safety requirements-> Food supply chain performance	0.046	7.978
Food safety requirements-> Information technology	0.080	3.568
Food traceability -> Food supply chain performance	0.032	1.434
Food traceability -> Information technology	0.051	2.561
Information technology -> Food supply chain performance	0.065	6.193
Performance measures-> Food supply chain performance	0.053	5.544
Performance measures-> Food traceability	0.064	2.291
Performance measures-> Information technology	0.061	1.527
Performance measures -> Regulatory compliance	0.068	2.816
Regulatory compliance -> Food supply chain performance	0.064	3.370
Regulatory compliance -> Information technology	0.061	9.438
Risks in food supply chain -> Food supply chain performance	0.055	4.417
Risks in food supply chain -> Information technology	0.087	2.754

Performance measures in food supply chains are a challenge because of the non-alignment of goals of partners⁴⁸ and how to establish them⁴⁷. This is evidenced by a high T-statistic value (5.544) suggesting that the null hypothesis is rejected.

The relationship between information technology and supply chain performance is high with a T-statistic value of 6.193, suggesting strong evidence against the null hypothesis. Supply chain performance relies on the information that is gathered⁶⁰ to maintain and improve the standards of last-mile delivery⁶², as well as remove bottlenecks, enabling quick responses to emergencies⁶⁷.

A T-statistic value of 2.291 for the relationship between food traceability and food supply chain performance suggests that there is high evidence against the null hypothesis. According to Thomas et al.⁷¹, food traceability has raised concerns about the halal food industry. This is in agreement with Chen et al.³⁷, who contend that food traceability reduces misinformation and provides customers with information, even though scholars are yet to agree on the subject of food traceability. Regulatory compliance and supply chain performance have exhibited a T-statistic value of 3.370, which is considered high enough to reject the null hypothesis.

Figure 2 reflect on the PLS SEM that was used in the study to provide a comprehensive insight into the interconnected relationships of variables and constructs that inform food quality and safety management in Oman. PLS SEM enables research to estimate complex models with many constructs¹⁰² and structural paths¹⁰³ and reflects on the prediction in estimating the statistical model¹⁰⁴. The correlations indicate that food safety requirements, food traceability, performance measures, information technology and regulatory compliance have a significant and positive relationship¹⁰⁵ with food quality and safety management in Oman.

The T-test values are used to assess the impact of food quality and management systems on the endogenous constructs of the study. The results indicate interesting data regarding food quality and safety requirements, in which the relationship between food safety requirements and food supply chain performance is positive and significant. Food safety requirements and food supply chain performance have a T-value = 7.978, information and technology and food supply chain performance have a T-value = 6.193, and information technology and regulatory compliance have a T-value = 9.438. These results signify that the adoption of information technology in the Oman food industry acts as an important element in improving food safety regulation, which aligns with the insights from Guan et al.⁶⁰ and matches the importance of regulatory compliance and supply chain performance as shown in the research of Uyttendaele et al⁸³.

Moreover, the relationships between food safety requirements and information technology (T-value = 3.068), food traceability and information technology (T-value = 2.561), and performance measures and regulatory compliance (T-value = 2.816) also explain the importance of utilising information technology for food quality and safety management in Oman. These results have a significant contribution to the understanding of what the food industry needs to guarantee food safety.

Information technology can also be adopted in the tracking and management of food safety risks, tracing food products in the supply chain and monitoring compliance by government agencies. Furthermore, it can be important in tracking temperature-controlled products (cold storage warehouse and shops. The negative correlation between food safety risks and regulatory compliance (Path coefficient: -0.217) emphasises the crucial role of compliance procedures in risk mitigation efforts, which is reflected in the study by Gurtu and Johny²⁵.

The results further confirm that the adoption of information technology is critical in fostering a robust response in calling out contaminated products and can be used to monitor performance measures through pro-active interventions in the supply chain.

The PLS SEM analyses highlight the intricate interactions between safety, technology, compliance and risks in the food supply chain. This is consistent with the wider debates published on supply chains, especially about the necessity of a holistic approach for the efficient management of these factors, as demonstrated by studies such as Wiengarten et al³¹.

Mediation relationships and hypothesis testing

This section presents the mediation relationship and hypothesis testing (Table 9). In PLS SEM, a third variable can be introduced between two variables to identify the mediation effect¹⁰⁴. This implies that the mediator dictates the nature of the relationship between the exogenous and endogenous constructs¹⁰⁶. Based on Fig. 2, the mediation effect was done using information technology; however, the study went so far as to establish the interconnectedness relationship of these variables to supply chain performance so that we increase the robustness of the results and understanding of the complexities in the supply chain as supported by Aguinis et al¹⁰⁷. In this study, the indirect effects of food safety requirements, performance measures, risks in the food supply chain, regulatory compliance and food traceability were mediated through information technology. The T-tests were used to analyse the mediation of food safety requirements → information technology → food supply chain performance indicates a T-value = 2.713. The results imply that the integration of information technology into food supply chain performance will result in food safety and efficiency in food supply chain performance. This result resonates with researchers who indicate that the accurate collection of data and the adoption of information technology such as blockchains for traceability and artificial intelligence (AI) in quality have a positive impact on a country’s food supply chain performance^60,61.

Table 9

Mediation relationships and hypothesis testing
	Standard Deviation (STDEV)	T-Statistics (\|O/STDEV\|)
Food safety requirements-> Information technology -> Food supply chain performance	0.042	2.713
Performance measures-> Food traceability -> Food supply chain performance	0.007	1.025
Performance measures-> Regulatory compliance -> Food supply chain performance	0.017	2.537
Regulatory compliance -> Information technology -> Food supply chain performance	0.050	4.732
Risks in food supply chain -> Information technology -> Food supply chain performance	0.041	2.351
Performance measures-> Regulatory compliance -> Information technology -> Food supply chain performance	0.017	2.596

To assess the mediation effect of food risk in food supply chains → information technology →food supply chain performance, the results showed a significant and positive T-test value = 2.351. The results helped in the understanding of information technology adoption in the management of food supply chains and established that, due to the complexities of supply chain risks, the incorporation of information technology is crucial in building resilience and agile food supply chains. This reinforces the arguments made by scholars such as Gurtu and Johny²⁵, who emphasise the role that technology plays in mitigating supply chain risks. Furthermore, the mediation of performance measure → food traceability → food supply chain performance showed a T-test value = 1.025. These results show that the mediation effect may be weak, but it is of interest since it shows that food traceability has a low impact on food supply chain performance. This could add to the debate that consumers are less likely to focus on food traceability^75,77.

The mediating effect of performance measures → regulatory compliance → food supply chain performance reflects a significant and positive T-value = 2.537. This validates the fact that regulatory compliance plays an important role in food supply chain performance. This also confirms similar studies that have shown that food industries are failing to meet food safety and quality standards due to noncompliance with international standards⁷. This requires different industry actors to actively measure and manage the compliance issues of supply chain actors to avoid unforeseen disruptions and risks.

Relatedly, the mediation relationship of regulatory compliance → information technology → food supply chain performance shows that technology can be a catalyst in proving regulatory compliance and improving food supply chain performance by a T-test value = 4.732. These results are phenomenal since they show that the ever-increasing regulatory complexities and the need to monitor compliance in real time reporting cannot be understated. The most interesting aspect of this study came from the interconnected relationship among food safety requirements, performance measures, risk in food supply chain, regulatory compliance, food traceability and information technology. The results reflect a significant T-test value = 2.596, confirming similar studies that concluded that integrating these factors among the supply chain actors results in building resilience, agility and robustness of food supply chains, such as the work of Hong et al⁷. This integrated approach highlights the multi-layered nature of supply chain management, where technology, compliance and safety measures are deeply interconnected. It also reflects the insights of Rahman et al.⁷⁰ on the complexity of traceability systems.

Thus, this study’s thorough examination of Oman’s food supply chain’s food quality and safety management systems provides significantly fresh perspectives into the complex relationships between numerous variables affecting the performance of the food supply chains. The participants’ demographics, which included a wide range of ages, genders, professional levels and regions, have focused attention on how complex Oman’s food safety management is. A deeper understanding of the dynamics in the food supply chain has been made possible by the results obtained from the path analysis, R-square, factor analysis, construct validity and reliability, and other tests.

The key findings include the following:

There is a strong positive relationship between food safety requirements and food supply chain performance, emphasising the vital role of strict food safety protocols in ensuring the safety of food.
Information technology has a significant impact on improving supply chain performance and regulatory compliance, as technology can become an accelerator for these two aspects.
The findings of the mediation analysis demonstrate the interdependencies among information technology, food safety regulations, risk management, performance measures and regulatory compliance. Together, these elements support the development of resilience, agility and resilience in the food supply chain.

Recommendations

Based on the findings of the research, the following recommendations are presented for anyone involved with managing and supervising Oman’s food supply chain:

Constantly improving the standards for food safety is crucial. This entails following current laws as well as predicting future safety issues and revising procedures appropriately.
The use of cutting-edge information technologies should be leveraged to ensure regulatory compliance of supply chain activities. This includes the use of data analytics, blockchain, AI and other tools for providing better traceability and control to food supply chains.
Applying a comprehensive strategy to identify and manage risks in the food supply chain is essential. Technology can aid in detecting and handling possible disruptions.
Collaboration among supply chain actors and regulatory entities should be encouraged based on transparency and open communication channels to improve supply chain performance as well as compliance.
Increasing consumers’ awareness of food safety is required and could result in a demand-driven push for higher quality and strict safety standards.

Ethical approval: Author confirms that this manuscript has not been previously published and is not currently under consideration by any other journal. Ethical approval was obtained from the Middle East College Ethical Approval Committee before data collection.

Methods: all methods were carried out in accordance with relevant guidelines and regulations

Consent to participate: The author and respondents participated voluntarily and willingly. All questionnaire included the consent to participate introductory section.

Consent to publish: The author agrees with journal terms and conditions.

Funding: Ministry of Agriculture, Fisheries and Water Resources, Oman-Grant number: MOHERI/SRPP/MOAFWR/1/2022/R1/01

Authorship contribution statement: Author contributed everything from idea generation, data collection and analysis.

Experimental protocols: Not applicable

Declaration of competing interest: No conflict of interest

Data availability: All required data will be available upon request.

Acknowledgments: The author declares that no acknowledgements.

Fung, F., Wang, H. S. & Menon, S. Food safety in the 21st century. Biomed. J. 41, 88–95 (2018).
FAO Guide to Ranking Food Safety Risks at the National Level (FAO, 2020); https://doi:10.4060/CB0887EN (2020).
Food and Agriculture Organization of the United Nations. vol. 100 (XXXX).
Found. FSSC 2200, 22000-5 1 (2020).
Hoffmann, S., Batz, M. B. & Morris, J. G. Annual cost of illness and quality-adjusted life year losses in the United States due to 14 foodborne pathogens. J. Food Prot. 75, 1292–1302 (2012).
Scharff, R. L. Economic burden from health losses due to foodborne illness in the United States. J. Food Prot. 75, 123–131 (2012).
Hong, J., Zhou, Z., Li, X. & Lau, K. H. Supply chain quality management and firm performance in China’s food industry—the moderating role of social co-regulation. Int. J. Logist. Manag. 31, 99–122 (2020).
Quang, H. T. et al. An extensive structural model of supply chain quality management and firm performance. Int. J. Qual. Reliab. Manag. 33, 444–464 (2016).
EUR-Lex - C:2022:355:TOC (EN - EUR-Lex, 2022); https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AC%3A2022%3A355%3ATOC.
Masengu, R. & Mangwenda, S. The impact of hazard analysis and critical control (HACCP) on sales of liquid milk: The case of Manicaland Province, Zimbabwe. Int. J. Economics Commerce Manag. (2016).
Nguyen, Q. N. & Hoang, T. H. L. Estimates of the impact of information technology on the tourism supply chain performance in Vietnam. East.-Eur. J. Enterp. Technol. 6, 96–106 (2022).
Regulation No 852/2004 of the European Parliament and of the Council on the Hygiene of Foodstuffs (FAO).
Rotaru, G., Sava, N., Borda, D. & Stanciu, S. Food quality and safety management systems: a brief analysis of the individual and integrated approaches. XI (2005).
Dracea, S. Z. & Vlădulescu, R. Food Safety in the Context of the European Union comparative study of certification schemes for food safety management systems in the European Union Context Amfiteatru Econ. 20 (2018).
Zainab, N. Oman Observer (2021).
Ministry of Regional Municipalities and Water Resource-Royal Decree No. vol. 24 (2018).
NAMA quality certification (2023).
Wertheim-Heck, S. C. O., Vellema, S. & Spaargaren, G. Food safety and urban food markets in Vietnam: the need for flexible and customized retail modernization policies. Food Policy 54, 95–106 (2015).
Kutty, S. Oman Observer (2020).
Fact Sheets (WHO, 2023).
Hald, T. et al. World Health Organization estimates of the relative contributions of food to the burden of disease due to selected foodborne hazards: a structured expert elicitation. Plos One 11, 0145839 (2016).
Kirk, M. D., Angulo, F. J., Havelaar, A. H. & Black, R. E. Diarrhoeal disease in children due to contaminated food. Bull. World Health Organ. 95, 233 (2017).
Naletina, D., Bendeković, J. & Nola, I. Food safety and food quality in the supply chain (2017).
Thangavel, S., Shenbhagavadivu, T. & Sabastin, S. INvestigating supply chain management problems in food processing industry (2018).
Gurtu, A. & Johny, J. Supply chain risk management: literature review. Risks 9, 1–16 (2021).
Nakamba, C. C., Chan, P. W. & Sharmina, M. How does social sustainability feature in studies of supply chain management? A review and research agenda. Supply Chain Manag. 22, 522–541 (2017).
Chang, W., Ellinger, A. E., Kim, K. K. & Franke, G. R. Supply chain integration and firm financial performance: a meta-analysis of positional advantage mediation and moderating factors. Eur. Manag. J. 34, 282–295 (2016).
Choi, K., Narasimhan, R. & Kim, S. W. Postponement strategy for international transfer of products in a global supply chain: a system dynamics examination. J. Oper. Manag. 30, 167–179 (2012).
Dey, A., LaGuardia, P. & Srinivasan, M. Building sustainability in logistics operations: a research agenda. Manag. Res. Rev. 34, 1237–1259 (2011).
Fearne, A., Garcia Martinez, M. & Dent, B. Dimensions of sustainable value chains: Implications for value chain analysis. Supply Chain Manag. Int. J. 17, 575–581 (2012).
Wiengarten, F., Humphreys, P., Gimenez, C. & McIvor, R. Risk, risk management practices, and the success of supply chain integration. Int. J. Prod. Econ. 171, 361–370 (2016).
Nyamah, E. Y., Jiang, Y., Feng, Y. & Enchill, E. Agri-food supply chain performance: an empirical impact of risk. Manag. Decis. 55, 872–891 (2017).
El Din, M., Masengu, R., Ncube, M. & Al Kaabi, S. The impact of post-Covid-19 Container shortage crisis on global supply chains (2021).
Vahid Nooraie, S. & Parast, M. M. Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities. Int. J. Prod. Econ. 171, 8–21 (2016).
Goetschalckx, M., Huang, E. & Mital, P. Trading off supply chain risk and efficiency through supply chain design. Procedia Comput. Sci. 16, 658–667 (2013).
Huang, E. & Goetschalckx, M. Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk. Eur. J. Oper. Res. 237, 508–518 (2014).
Chen, Z. & Feng, J. Newspaper reports of food safety scandals: evidence from an online take-out application in China. Appl. Econ. Lett. 25, 187–191 (2018).
Marucheck, A., Greis, N., Mena, C. & Cai, L. Product safety and security in the global supply chain: issues, challenges and research opportunities. J. Oper. Manag. 29, 707 (2011).
Meena, P. L., Kumar, G. & Ramkumar, M. Supply chain sustainability in emerging economy: a negative relationship conditions’ perspective. Int. J. Prod. Econ. 261, (2023).
Gray, J. V. & Handley, S. M. Managing contract manufacturer quality in the presence of performance ambiguity. J. Oper. Manag. 38, 41–55 (2015).
Auler, D. P., Teixeira, R. & Nardi, V. Food safety as a field in supply chain management studies: a systematic literature review. Int. Food Agribus. Manag. Rev. 20, 99–112 (2017).
Connolly, A., Luo, L. S. & Connolly, K. P. Global insights into essential elements food safety: The Chinese Example. J. Food Prod. Mark. 22, 584–595 (2016).
Handley, S. M. & Benton, W. C. The influence of exchange hazards and power on opportunism in outsourcing relationships. J. Oper. Manag. 30, 55–68 (2012).
Narayanan, S., Narasimhan, R. & Schoenherr, T. Assessing the contingent effects of collaboration on agility performance in buyer-supplier relationships. J. Oper. Manag. 33–34, 140–154 (2015).
Panjehfouladgaran, H. & Yusuff, R. Fuzzy performance measurement for supply chain management in Malaysian rubber glove manufacturer. Int. J. Logist. Syst. Manag. 24, 178–199 (2016).
Aramyan, L. H., Lansink, A. G. J. M. O., Vorst, J. G. A. J. & Kooten, O. V. Performance measurement in agri-food supply chains: a case study. Supply Chain Manag. 12, 304–315 (2007).
Abdel-Basset, M. & Mohamed, R. A novel plithogenic TOPSIS-CRITIC model for sustainable supply chain risk management. J. Clean. Prod. 247, 119586 (2020).
Sharma, S. K., Srivastava, P. R., Kumar, A., Jindal, A. & Gupta, S. Supply chain vulnerability assessment for manufacturing industry. Ann. Oper. Res. 326, 653–683 (2023).
Anderson, J. C. & Narus, J. A. A model of distributor firm and manufacturer firm working partnerships. J. Mark. 54, 42–58 (1990).
Agrawal, N. & Pingle, S. Mitigate supply chain vulnerability to build supply chain resilience using organisational analytical capability: A theoretical framework. Int. J. Logist. Econ. Glob. 8, 272–284 (2020).
Narimissa, O., Kangarani-Farahani, A. & Molla-Alizadeh-Zavardehi, S. Drivers and barriers for implementation and improvement of Sustainable Supply Chain Management. Sustain. Dev. 28, 247–258 (2020).
Ramos, E., Coles, P. S., Chavez, M. & Hazen, B. Measuring agri-food supply chain performance: insights from the Peruvian kiwicha industry. Benchmarking 29, 1484–1512 (2022).
Panigrahi, R. R., Jena, D., Meher, J. R. & Shrivastava, A. K. Assessing the impact of supply chain agility on operational performances-a PLS-SEM approach. Meas. Bus. Excell. 27, 1–24 (2023).
Soltanmohammadi, A., Andalib Ardakani, D., Dion, P. A. & Hettiarachchi, B. D. Employing total quality practices in sustainable supply chain management. Sustain. Prod. Consum. 28, 953–968 (2021).
Xue, J., Zhang, W., Rasool, Z. & Zhou, J. A review of supply chain coordination management based on bibliometric data. Alex. Eng. J. 61, 10837–10850 (2022).
Dossi, A. & Patelli, L. You learn from what you measure: financial and non-financial performance measures in multinational companies. Long Range Plann. 43, 498–526 (2010).
Kumar, N. & Ganguly, K. K. Non-financial e-procurement performance measures: their interdependence and impact on production cost. Int. J. Product. Perform. Manag. 70, 41–64 (2021).
Childerhouse, P. & Towill, D. Engineering supply chains to match customer requirements. Logist. Inf. Manag. 13, 337–346 (2000).
Wong, C., Lai, K. H. & Cheng, T. Value of information integration to supply chain management: roles of internal and external contingencies. J. Manag. Inf. Syst. 28, 161–200 (2011).
Guan, W., Ding, W., Zhang, B., Verny, J. & Hao, R. Do supply chain related factors enhance the prediction accuracy of blockchain adoption? A machine learning approach. Technol. Forecast. Soc. Change 192 (2023).
Kim, M. & Chai, S. The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: global supply chain perspective. Int. J. Prod. Econ. 187, 42–52 (2017).
Farhan Basheer, M., Siam, A., R., M., Mohammed Awn, A. & Ghulam Hassan, S. Exploring the role of TQM and supply chain practices for firm supply performance in the presence of information technology capabilities and supply chain technology adoption: a case of textile firms in Pakistan. Uncertain Supply Chain Manag. 7, 275–288 (2019).
Wedel, I. Influences of Information Technology on Supply Chain Performance (2015).
Han, G. & Dong, M. Trust-embedded coordination in supply chain information sharing. Int. J. Prod. Res. 53, 5624–5639 (2015).
Huang, G. Q., Lau, J. S. K. & Mak, K. L. The impacts of sharing production information on supply chain dynamics: a review of the literature. Int. J. Prod. Res. 41, 1483–1517 (2003).
Koçoǧlu, I., Imamoǧlu, S. Z., Ince, H. & Keskin, H. The effect of supply chain integration on information sharing: enhancing the supply chain performance. Procedia - Soc. Behav. Sci. 24, 1630–1649 (2011).
Vass, T., Shee, H. & Miah, S. The effect of “Internet of Things” on supply chain integration and performance: an organisational capability perspective (2018).
Hobbs, J. E. Traceability in meat supply chains (2002).
Narsimhalu, U., Potdar, V. & Kaur, A. A case study to explore influence of traceability factors on Australian food supply chain performance. Procedia - Soc. Behav. Sci. 189, 17–32 (2015).
Rahman, A. A., Singhry, H. B., Hanafiah, M. H. & Abdul, M. Influence of perceived benefits and traceability system on the readiness for Halal Assurance System implementation among food manufacturers. Food Control 73, 1318–1326 (2017).
Thomas, A. M., White, G. R. T., Plant, E. & Zhou, P. Challenges and practices in Halal meat preparation: a case study investigation of a UK slaughterhouse. Total Qual. Manag. Bus. Excell. 28, 12–31 (2017).
Wood. Machine slaughter means KFC halal chicken is “not halal.” (2023).
Poniman, D., Purchase, S. & Sneddon, J. Traceability systems in the Western Australia halal food supply chain. Asia Pac. J. Mark. Logist. 27, 324–348 (2015).
Salah El Din, M., Masengu, R. & Bentley, Y. The role of supply chain control towers in facilitating sustainable supply chain management practices. 310–327. https://doi:10.4018/979-8-3693-0019-0.ch016 (2023).
Chan, F. T. S., Wang, Z. X., Goswami, A., Singhania, A. & Tiwari, M. K. Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations. Int. J. Prod. Res. 58, 5155–5174 (2020).
Wang, X., Li, D. & O’Brien, C. Optimisation of traceability and operations planning: an integrated model for perishable food production. Int. J. Prod. Res. 47, 2865–2886 (2009).
Wang, X., Li, D. & O’Brien, C. Optimisation of traceability and operations planning: an integrated model for perishable food production. Int. J. Prod. Res. 47, 2865–2886 (2009).
Alvarez, S., Solís, D. & Hwang, J. Modeling shellfish harvest policies for food safety: wild oyster harvest restrictions to prevent foodborne Vibrio vulnificus. Food Policy 83, 219–230 (2019).
Latif, I. A., Mohamed, Z., Sharifuddin, J., Abdullah, A. M. & Ismail, M. M. A comparative analysis of global halal certification requirements. J. Food Prod. Mark. 20, 85–101 (2014).
Oliveira, C. A. F., Cruz, A. G., Tavolaro, P. & Corassin, C. H. Food Safety: Good Manufacturing Practices (GMP), Sanitation Standard Operating Procedures (SSOP), Hazard Analysis and Critical Control Point (HACCP). in Antimicrobial Food Packaging. 129–139 (2016) https://doi:10.1016/B978-0-12-800723-5.00010-3.
Lupin, H. M., Parin, M. A. & Zugarramurdi, A. HACCP economics in fish processing plants. Food Control 21, 1143–1149 (2010).
Cusato, S. et al. Assessing the costs involved in the implementation of GMP and HACCP in a small dairy factory. Qual. Assur. Saf. Crops Foods 6, 135–139 (2014).
Uyttendaele, M., Boeck, E. & Jacxsens, L. Challenges in food safety as part of food security: lessons learnt on food safety in a globalized world. Procedia Food Sci. 6, 16–22 (2016).
Mol, S., Erdogan, B. E. & Ulusoy, S. Survey into the characteristics, working conditions and deficiencies of Turkish seafood processing firms. Turk. J. Fish. Aquat. Sci. 14, 705–712 (2014).
AKO, H., Suliman, S. & Abdalla, M. Implementation of HACCP and food safety program in Al Ain City Abu Dabi. J. Food Nutr. Disord. 2013, (2016).
Toropilová, J. & Bystrický, P. Why HACCP might sometimes become weak or even fail. Procedia Food Sci. 5, 296–299 (2015).
Ramalho, V., Moura, A. P. & Cunha, L. M. Why do small business butcher shops fail to fully implement HACCP? Food Control 49, 85–91 (2015).
Ulusoy Sözen, B., Hecer, C. & Uygulanabilirliği Zor Olan Bir Sistem Midir, H. Is HACCP a difficult food safety system to implement? DergiparkOrgTrBU Sözen C HecerJournal Biol. Environ. Sci. 7, 33–38 (2013).
Bryman, A. & Bell, E. Business Research Methods (2011).
Christy, L. (2005).
Wolfle, L. M. The Introduction of path analysis to the social sciences, and some emergent themes: An annotated bibliography. Struct. Equ. Model. 10, 1–34 (2003).
Rights, J. D. & Sterba, S. K. New recommendations on the use of R-squared differences in multilevel model comparisons. Multivar. Behav. Res. 55, 568–599 (2020).
Rights, J. D. & Sterba, S. K. R-squared measures for multilevel models with three or more levels. Multivar. Behav. Res. 58, 340–367 (2023).
Gelman, A., Goodrich, B., Gabry, J. & Vehtari, A. R-squared for Bayesian regression models. Am. Stat. 73, 307–309 (2019).
Kasuya, E. On the use of R and R-squared in correlation and regression. Ecol. Res. 34, 235–236 (2019).
Miles, J. R -Squared, adjusted R‐Squared. in Encyclopedia of Statistics in Behavioral Science. https://doi:10.1002/0470013192.BSA526 (2005).
Ozili, P. K. The acceptable R-square in empirical modelling for social science research. SSRN Electron. J. https://doi:10.2139/ssrn.4128165 (2022).
Chiwaridzo, O. T. & Masengu, R. The impact of social media branding and technology adoption on green tourism: the role of tourist behavior as a mediator in developing countries post-COVID-19—context of Zimbabwe. Future Bus. J. 9, 1–14 (2023).
McCabe, D. J., Dalessio, A., Briga, J. & Sasaki, J. The convergent and discriminant validities between the IOR and the JDI: English and Spanish forms. Acad. Manage. J. 23, 778–786 (1980).
Ab Hamid, M. R., Sami, W. & Mohmad Sidek, M. H. Discriminant validity assessment: use of Fornell & Larcker criterion versus HTMT criterion. J. Phys. Conf. Ser. 890, (2017).
Roemer, E., Schuberth, F. & Henseler, J. HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Ind. Manag. Data Syst. 121, 2637–2650 (2021).
Hair, J. F., Risher, J. J., Sarstedt, M. & Ringle, C. M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31, 2–24 (2019).
Gora, A. A., Ştefan, S. C., Popa, Ş. C. & Albu, C. F. Students’ perspective on quality assurance in higher education in the context of sustainability: A PLS-SEM approach. Sustain. Switz. 11, (2019).
Richter, N. F., Cepeda, G., Roldán, J. L. & Ringle, C. M. European Management Journal Special Issue European Management Research Using Partial Least Squares Structural Equation Modeling (PLS-SEM, 2015).
Matthews, L. et al. PLS-SEM: The holy grail for advanced analysis millennials’ purchasing response to firms’ examining the effect of humor in environmentally-friendly advertising sales effort and performance: dark side of customer product knowledge Feisal Murshed and Vinita Sangtani organizing a framework for customer value management in online media relationships. Mark. Manag. J. 28, (2018).
Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M. & Howard, M. C. Beyond a tandem analysis of SEM and PROCESS: use of PLS-SEM for mediation analyses! Int. J. Mark. Res. 62, 288–299 (2020).
Aguinis, H., Edwards, J. R. & Bradley, K. J. Improving our understanding of moderation and mediation in strategic management research. Organ. Res. Methods 20, 665–685 (2017).

No competing interests reported.

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Effectiveness of food quality and safety management systems in Oman’s food supply chain

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Abstract

Figures

Introduction and background to the study

Measurement scales and their sources

Hypothesis development

Literature review

Risks in food supply chain and food supply chain performance

Food safety requirements and food supply chain performance

Performance measures and food supply chain performance

The impact of information technology on food supply chain performance

Food traceability impact and food supply chain performance

Regulatory compliance impact and food supply chain performance

Research paradigm and methodology

Results and discussion

Demographic characteristics

Path analysis

R-square

Construct reliability and validity

Discriminant validity

Factor Loading

Direct relationships and hypothesis testing

Mediation relationships and hypothesis testing

Conclusion

Recommendations

Declarations

References

Additional Declarations

Supplementary Files

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