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The consumption of organic food grows worldwide, increasing the need of studies seeking to understand factors influencing its consumption. To extend the understanding of organic food market, the present research seeks to explore the effect of perceived value on consumer trust and purchase intention in Brazil.

A survey with 274 Brazilian consumers of organic food was carried out. Perceived value was measured through four dimensions: functional, economic, social and emotional. The convergent and discriminant validities of latent variables were confirmed. The relationships among them were tested using structural equation modeling.

The results indicate that functional and emotional values positively affect consumer trust and that only emotional value motivates purchase intention. The trust had no direct influence on purchase intention. Thus, the perceived value is an important aspect in the prediction of trust and purchase intention among Brazilian consumers.

The identified results enlarge the understanding of Brazilian organic food market and consumer behavior and point out some opportunities for future research involving the subject.

The purpose of this paper is to develop and implement a new sustainable apparel design and production model, cradle to cradle apparel design (C2CAD), that provides guidelines for apparel designers and manufacturers to solve some of the sustainability problems related to apparel production.

The C2CAD model was developed by integrating McDonough and Braungart's “cradle to cradle” model into existing apparel design and production models. Knitwear design and production was used to implement the C2CAD model as a proof of concept. The performance and cost of the C2CAD knitwear were evaluated.

The C2CAD model has four main steps: problem definition and research; sample making; solution development and collaboration; and production. Following the four steps and with an international collaboration similar to current apparel industry practices, “Four‐season sustainability” children's knitwear prototypes were developed. Produced with an acceptable manufacturing cost, the products have good mechanical and color fastness performance.

The C2CAD model provides practical guidelines for apparel designers and manufacturers and allows them to address all three pillars in sustainable development: economic development, social development, and environmental protection.

The C2CAD is the first apparel design and production model that emphasizes sustainability in addition to functional, expressive, and aesthetic considerations. The production process of “Four‐season sustainability” children's knitwear demonstrated the implementation of C2CAD model in sustainable apparel design and production.

Public data such as news reports, interviews and memos were used to craft the case. In addition, the technical reports released by the National Transportation Safety Board (NTSB), along with secondary data in the form of expert accounts and congressional hearings were used to round out the synopsis of the case study.

This case explores the Boeing–McDonnell Douglas merger and its impact on Boeing’s corporate culture, ethics and strategic decision-making. After the merger, Boeing shifted from a culture focused on engineering excellence to one emphasizing cost-cutting and shareholder value. This cultural shift contributed to the development failures and ethical lapses that resulted in the 737 MAX crisis, which involved two fatal crashes. The case is designed for courses in Strategic Management or Organizational Behavior.

Strategic Management or Organizational Behavior

Extending a model of how teams learn, this paper aims to present a model of multiteam system (MTS) learning, comparing similarities and differences between how MTSs learn and how component teams learn. The paper describes the value of adaptive, generative and transformative learning for increasing MTS development over time.

The model proposes that environmental demands trigger adaptive, generative and transformative MTS learning, which is further increased by the MTS’s readiness to learn. Learning can happen during performance episodes and during hiatus periods between performance episodes.

Learning triggers coupled with readiness to learn and the cycle and phase of MTS process influence the learning process (adaptive, generative or transformative), which in turn influences the learning outcomes.

The study offers a number of research propositions with the idea that the model and propositions will stimulate research in this area.

This model allows MTS and component team leaders and facilitators to recognize that MTS learning is a process that is needed to help component teams work together and help the MTS as a whole perform in current and future situations, thereby improving MTS effectiveness.

Little attention has been given to the notion that MTSs learn and develop. This manuscript is the first to emphasize that MTSs learn and identify processes that can improve learning. Adaptive, generative and transformative processes describe how MTSs learn and produce changes in MTS structure and actions.

The purpose of this paper is to examine inter-organisational cooperation in delivering maintenance services, focusing on the level of maturity of this cooperation and the current role of performance measurement in evaluating its effectiveness. It also determines how performance measurement should be developed to support the needs of networked maintenance.

The empirical evidence is based on two cases of networks operating in the field of industrial maintenance. The first network operates in the energy industry, while the second operates in the mining industry. Both networks consist of machine supplier companies, performance partner companies and system supplier companies. A total of seven companies participated in workshops and interviews in 2013-2014.

As a result of the study, suggestions for improving the performance measurement of service value in maintenance are presented and a framework is offered that meets the needs of the customer, service provider and equipment provider. The significant innovation of the research is its combination of network, service and value perspectives in performance measurement by presenting a framework for measuring created value of cooperation.

The literature calls for new measurement models and frameworks that support the new management challenges. This research creates a framework for practical applications. The framework helps to identify possible development needs and increases the understanding of what is required when cooperation in a maintenance network deepens, moving from machine partner towards value partner.

This paper aims to outline current and emerging opportunities and challenges in applying collective intelligence methods to detection and analysis of weak signals of change.

The article builds on review of current literature on the topic and analysis of projects employing various methods of collective intelligence to scanning and sense making for signals of change.

The article points out possible roles of collective intelligence in analysis of weak signals, specific to the scanning and sensemaking stages in futures research. It identifies key variations of applying collective intelligence to weak signals that shape the result of the research process: selection of participants for collective intelligence (from controlled to open) and ways of collaboration in the collective intelligence process (from fully collective to switching between collective and individual mode). It also gives an example of a successful application of collective intelligence to weak signals analysis and suggests possible models that can be fitting for identifying and interpreting weak signals of change. It further discusses the current implications of AI for foresight and possible future implications of its development (“explainable AI” and human-machine collaboration).

The paper hypothesizes on the emerging challenges in the field of collective intelligence for weak signals of change and suggests a new framework for mapping the field. The paper has not been published before.

Quality Circles in Japan Quality Circles not confined to quality matters narrowly conceived; groups can take up any project which conduces to the improvement either of the product or of the manufacturing process.

Describes the two‐stage training that quick‐service restaurant McDonald's has devised for its customer‐facing employees.

Details the Hospitality Plus workshops, attended by more than 2,700 employees in three weeks at the end of 2004, which covered personal appearance; ensuring that dining areas are clean and inviting; awareness of customers – including the disabled and those with children – needing assistance; creating a friendly atmosphere by acknowledging customers; complaint handling, the ability to answer enquiries on menu ingredients and awareness of McDonald's role in the community. Also describes the Customer Care programme, designed to capture the best parts of the Hospitality Plus workshops, but also to be a sustainable session to help staff to deliver service that customers would view as superior to the competition.

Demonstrates that the training has helped to raise the percentage of customers agreeing that McDonald's service is friendly and courteous, fast and efficient. Reveals that the programmes have also reduced turnover among customer‐facing employees by more than 8% in a year.

Highlights the key role of training for customer‐facing employees in the service sector.

Shows that the training – which is not just a one‐off solution – represents real value for money, given the number of people trained and the cultural impact the company has gained.

The primary aim of this paper is to study a dark side of e-HRM concerning its parallel effect on human resource (HR) socialization and HR service delivery and the consequent impact of perceived HR effectiveness.

The current study started with an in-depth review of the extant literature in the field of e-HRM to derive a set of constructs. Based on the theoretical foundation of the identified constructs, the current study went on to derive a set of hypotheses, which was subsequently validated using the uses the quantitative technique of PLS-SEM. A primary survey, in the form of a structured questionnaire, was used as the source for data collection on a sample size of 276 from the Indian industrial domain. Careful attention was paid to eliminate the common method bias in the study.

The findings of this study show a simultaneous significant full mediation effect of both HR service delivery and HR socialization is the relationship between e-HRM and HR effectiveness. However, e-HRM has a strong and significant negative effect on HR socialization. Though HR socialization is positively related to HR effectiveness, the significantly reduced level of HR socialization as a consequence of adaption of e-HRM had negatively affected the perceived HR effectiveness.

Although the dark side of e-HRM has been recognized by academicians and practitioners alike, its implications have seldom been studied in the academic literature. The current study intends to shed some light on this important, but sparsely discussed topic. Further, this study makes significant and meaningful contributions in the literature of e-HRM by empirically studying together the positive and negative consequences of e-HRM and its effects on HR effectiveness. Several e-HRM scholars have discussed the implications of e-HRM adoption and highlighted the negative impacts of e-HRM, and traversing the same path, the current study advances the literature by empirically investigating the effect of e-HRM on the dehumanization of HR processes and practices.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

Supplements the (A) case.

Supplements the (A) case.

After the company's first aviation fatality, Southwest Airlines's CEO is faced with new and difficult decisions. The alacrity and compassion that characterized the company's response serves as a paradigm for any organization facing a future crisis situation.

To teach students how to manage a crisis situation.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

The purpose of this paper is to propose a method for performance assessment of organizations based on integrated approach of knowledge management and safety management using data envelopment analysis, and the proposed model is then applied in the car industry in Isfahan province to be checked. Therefore, deficiencies can be highlighted and possible strategies can be evolved to improve the performance.

As data envelopment analysis is a robust mathematical tool, it has been used to evaluate organizational performance. For discovering the organizational performance of knowledge management and safety management by data envelopment analysis (DEA), the first step is to specify proper criteria. To this end, in this method, the indices in both approaches of knowledge management and safety management were identified. Then, inputs and outputs were specified. Knowledge management and safety management were determined as input indices, and customer satisfaction and accident indicators were the output indices. It is noteworthy that each output index was used one time. In the next stage, performance of organizations was assessed based on both determined approaches and via data envelopment analysis. Finally, the organizations were ranked.

The suggested method was implemented in the car industry in the Isfahan province. The obtained results disclosed that among 12 decision-making units, 4 units are efficient when customer satisfaction is the output and 5 units are efficient when accidents indices are the output. In ranking with customer satisfaction as the output, Sepahan Atlas Pump Company was ranked first via super efficiency method, data envelopment analysis and similarity to ideal solution. In ranking with accidents as the output, Sepahan Atlas Pump Company ranked first via strong efficiency method and Sanatgar Company ranked first via data envelopment analysis and similarity to ideal solution.

Knowledge has been recognized as one of the valuable resources, and knowledge management would greatly effect improvement of job quality. Knowledge level increase is led by better performance and less errors. Consequently, it can enhance the organizational health and safety. There are some studies which have been conducted on safety management or knowledge management performance analysis. The organizational performance evaluation based on integrated approach of knowledge management and safety management is an important issue which is less considered in theory and practice. Thus, the authors have proposed a method which is able to evaluate the organization based on this integrated approach with functional indices, which resulted in accurate results, and finally, ranking can show the organization status to determine proper strategies.

The purpose of this paper is to unveil any underlying relationships between contractor inputs and performance outputs. The outcome of the reported study is intended to help identify the inputs, which have more significant impacts on contractor performance outputs and therefore, help formulate more reliable “upfront” (ex ante) performance assessment criteria, hence improving approaches to the contractor‐selection process.

A case study was conducted on the Performance Assessment Scoring System (PASS) of a large public client in Hong Kong to determine the Pearson product‐moment correlation between the scores of various input assessments and output assessments.

The findings revealed relationships between some of the input assessment scores and the output results. Emerging as positive, all the discerned relationships confirmed that better outputs did in fact relate well to better inputs.

The PASS system is designed to be very objective, hence the criteria and assessment of inputs may be restricted to easily measurable items. The sample size obtainable was small, but still considered to be adequate for this initial study.

Construction clients could choose to improve their contractor selection processes by identifying and incorporating contractor input factors that are seen to influence performance outputs. Contractors can also improve their outputs by focusing on the identified critical inputs.

Few studies have sought to discern relationships between contractor inputs and their performance outputs through a quantitative approach. This case study provided a methodology, incorporating a statistics‐based approach along with examples and explanations of how inputs can influence contractor outputs.

A theoretical model, which describes continuous improvement and benchmarking processes, was developed. The model was based on the concepts that improvements in management inputs should produce organizational benefits and that management inputs are limited by budget constraints. The model was developed using health and safety as an example of operational management issues. In this example, organizational benefits related to reductions in the number and costs of accidents and management inputs referred to the provision and costs of accident prevention control measures. The utility of the model was demonstrated with five health and safety scenarios, which tested the model against the continuous improvement philosophy and benchmarking with respect to reducing costs, improving performance, minimising organizational change and assessing performance within different work environments.

Ensuring continued compliance with food safety legislation and market requirements is critical to the fresh produce industry. The purpose of this paper is to analyse how a pre‐requisite programme (PRP) and key performance indicators (KPI) for food safety can be developed for the production of fresh produce, in both low input and high input supply chains.

The first stage of the research involved an examination of literature, which was conducted using desk research. The second stage includes a review of food safety management systems (FSMS) in fresh produce supply and the development of a PRP/KPI framework.

Farm assurance is well developed in the high input food supply chain for multiple retail customers. However, low input systems such as organic production, or conventional sources are often non‐assured, and supply wholesale retail and food service markets. Recent food safety incidents such as the E. coli outbreak in Germany have demonstrated that universally an effective FSMS is critical to ensure that produce is safe and wholesome and is not the cause of a food safety outbreak.

This study is of benefit to the produce industry and those in the food supply chain in the further development of quality assurance (QA) standards and business improvement models.

Over the past several decades, the need for safety on offshore oil platforms has attracted significant attention from all its stakeholders. This paper seeks to present a mathematical model that could be used to control unsafe conditions on oil platforms.

The model is based on the intuitive notion of safety underlying practices on an offshore platform. The theory of control system combines the human/operator factor gain, maintenance function effectiveness, the component safety quality assurance system, and the safety measurement system in a holistic manner to attain a controlled status of the oil platform.

It is observed that these components relate in a mutually interlinked system that guarantees maximum safety control if all the components are holistically managed.

Practically, the control of an offshore oil platform guarantees the lives and properties of those who utilize the oil platform.

The work is perhaps the first to advance a model of safety on oil platforms with the use of control systems. The work would be of value to safety managers, employees of oil companies and researchers interested in the control of accidents on oil platforms.

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.

In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.

The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.

The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.

The main purpose of this paper is to create a suitable structure based on neutrosophic numbers to evaluate the safety performance in construction projects in such a way that the shortcomings can be highlighted with the reasoned measurement and possible strategies can be recommended.

Data envelopment analysis (DEA), which is a useful tool for performance appraisal, along with neutrosophic logic, which is one of the most complete tools for handling uncertainty phenomenon, has been used to evaluate the safety performance of construction projects. With this hybrid model, a new strategy is considered as an indicator for safety performance and comparisons are made between different units.

A total of 35 Chinese organizations with construction projects lasting between 1.5 and 2 years were selected for comparison. After processing the data into neutrosophic numbers and using the NN-DEA model, it can be found that projects that pay more attention to safety issues such as training and equipment are more efficient.

Since in the real world, there are uncertainties with different contradictions, and neutrosophical data can handle many of these challenges, using DEA model with neutrosophic numbers to evaluate the performance of construction projects from a safety perspective, can provide significantly better results. Therefore, considering that no study has been presented in this field so far, the authors will deal with this topic.

There is a growing awareness on the use of systems approaches to improve patient safety and quality. While earlier studies evaluated the validity of such approaches to identify and mitigate patient safety risks, so far only little attention has been given to their inputs, such as structured brainstorming and use of system mapping approaches (SMAs), to understand their impact in the risk identification process. To address this gap, the purpose of this paper is to evaluate the inputs of a well-known systems approach, failure modes and effects analysis (FMEA), in identifying patient safety risks in a real healthcare setting.

This study was conducted in a newly established adult attention deficit hyperactivity disorder service at Cambridge and Peterborough Foundation Trust in the UK. Three stakeholders of the chosen service together with the facilitators conducted an FMEA exercise along with a particular system diagram that was initially found as the most useful SMA by eight stakeholders of the service.

In this study, it was found that the formal structure of FMEA adds value to the risk identification process through comprehensive system coverage with the help of the system diagram. However, results also indicates that the structured brainstorming refrains FMEA participants from identifying and imagining new risks since they follow the process predefined in the given system diagram.

While this study shows the potential contribution of FMEA inputs, it also suggests that healthcare organisations should not depend solely on FMEA results when identifying patient safety risks; and therefore prioritising their safety concerns.

The purpose of this paper is to expose computational methods as applied to engineering systems and evolutionary processes with randomness in external actions and inherent parameters.

In total, two approaches are distinguished that rely on solvers from deterministic algorithms. Probabilistic analysis is referred to as the approximation of the response by a Taylor series expansion about the mean input. Alternatively, stochastic simulation implies random sampling of the input and statistical evaluation of the output.

Beyond the characterization of random response, methods of reliability assessment are discussed. Concepts of design improvement are presented. Optimization for robustness diminishes the sensitivity of the system to fluctuating parameters.

Deterministic algorithms available for the primary problem are utilized for stochastic analysis by statistical Monte Carlo sampling. The computational effort for the repeated solution of the primary problem depends on the variability of the system and is usually high. Alternatively, the analytic Taylor series expansion requires extension of the primary solver to the computation of derivatives of the response with respect to the random input. The method is restricted to the computation of output mean values and variances/covariances, with the effort determined by the amount of the random input. The results of the two methods are comparable within the domain of applicability.

The present account addresses the main issues related to the presence of randomness in engineering systems and processes. They comprise the analysis of stochastic systems, reliability, design improvement, optimization and robustness against randomness of the data. The analytical Taylor approach is contrasted to the statistical Monte Carlo sampling throughout. In both cases, algorithms known from the primary, deterministic problem are the starting point of stochastic treatment. The reader benefits from the comprehensive presentation of the matter in a concise manner.

The purpose of this paper is to investigate the impact of access to credit and safety nets on fertilizer adoption in rural Ethiopia.

A panel data set collected in 2005 and 2007 on 278 households and over 5,700 plots from the Southern Highlands of Ethiopia is examined. The authors developed a theoretical model relating input use and credit contract under third-party credit collateral agreement. The estimation is based on instrumental variables regressions to account for the endogeneity of credit access, and safety nets in fertilizer demand equation.

Despite increasing trends in fertilizer and improved varieties adoption since mid-2003, only 22 percent of the plots in the sample is actually received fertilizer. Households with more assets measured by livestock wealth are more likely to adopt fertilizer but less likely to participate in the local credit market as they have better savings that could be used to buy fertilizer/improved seeds without credit contract. This suggests poorer farmers heavily depend on credit than wealthier. Participation in safety nets programs did not contribute for increased use of fertilizer suggesting that the program either competes with agricultural labor or the low wage income was not enough to pay for farm inputs.

The findings show that with a heavier reliance on credit by poorer farmers it appears that much might be gained by targeting policies toward increasing credit access to this group.

Studies that utilize repeated plot- and household-level observations are limited. To the knowledge, this is the first study showing the relationship between credit accesses, public work program and fertilizer adoption over time in rural Ethiopia.

While the investment in construction projects has increased over the past few decades, low construction project productivity (CPP) appeared to be persistent, thereby reflecting an “investment-in-failure” paradox between the investment and CPP. Hence, this paper aims to develop a systematic and holistic CPP evaluation framework to explain the apparent paradox in the construction industry.

The paper first reviews the literature about the theories of system, production, principal–agent and project success evaluation to re-conceptualise the CPP and proposes a two-stage CPP evaluation framework. The framework is subsequently explored through a sequential qualitative mixed-methods design within the context of the Hong Kong construction industry by combining 32 semi-structured interviews with senior industry experts and exploratory case studies, with three real-life construction projects.

The paper identifies three system boundaries for CPP evaluation, that is, parameter, timeframe and stakeholder, and develops a two-stage CPP evaluation framework to indicate site efficiency and utilisation effectiveness, thereby accessing the productivity of both the construction and post-construction stages. The “investment-in-failure” paradox associated with current CPP evaluation approaches is primarily attributed to the narrowly defined CPP boundaries.

The qualitative exploration of the evaluation framework only focusses on the Hong Kong construction industry. Further case studies within other urban contexts could be used to improve the generalisability of the findings. Quantitative research is also necessary to advance theoretical development of the two-stage CPP evaluation.

The systemic CPP conceptualisation and the two-stage CPP evaluation framework support the systems thinking of industry stakeholders and enable them to formulate holistic strategies for long-term CPP enhancement.

The research demonstrates the needs to expand the system boundaries of CPP to reflect its systemic value and to shift the paradigm of CPP evaluation from being output-orientated and quantity-focussed to being outcome-orientated and value-focussed.

The purpose of this paper is to examine the effect of external factors on police efficiency in a sample of Peru’s municipalities. Drawing on the postulates of the contingency theory of organizations, this study argues that because of the public nature of police organizations’ activities, they are heavily influenced by the environment in which they operate, which invariably affects their efficiency.

The study examines police efficiency using a four-stage Data Envelopment Analysis model in a sample of 619 Peruvian municipalities (“distritos”). After adjusting the inputs to the external environment, the remaining inefficiencies are presumed to result from managerial issues and other internal organizational factors.

The results indicate that police efficiency improves after adjusting police inputs to the effect of external factors. The mean efficiency scores improved by 39 percent from 0.57 to 0.79. Because the effects of the external environment can be stronger in larger municipalities, these experience larger improvements in efficiency than smaller municipalities.

There are two important limitations. First, the sample of municipalities in Peru is limited and the results must be interpreted with caution. In addition, the external factors included in the second stage analysis represent only a proportion of all potential external factors which can influence police efficiency. Second, the results presented here explain only one aspect of contingency theory. An important implication of this study suggests that for police managers to make more informed decisions on resource use and allocations, they need a more thorough understanding of the environments in which police operate.

It can assist researchers to ascertain the effects of the external environment on police performance. More importantly, it can assist police managers in making informed decisions about resource use and allocation. Consequently, a better use of resources could lead to better policing and improved citizen safety and security.

This study introduces a new and innovative methodological approach to measure police performance. This multi-stage approach has not been used in the context of democratic policing in Latin America and the Caribbean. The current research fills an important gap on how to measure police performance in developing countries.

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

This chapter examines group communication in medical teams through psychological safety and simulation training research. Research has shown that medical teams are challenged by established hierarchies, power/status differences, temporal stability, changing team memberships, and deeply held beliefs that emphasize individual responsibility. A review of 47 studies (29 psychological safety, 18 simulation) was conducted to understand key findings in relationship to group communication. Results indicate that team leadership promotes team psychological safety, voice, and relationship quality while status differences and hierarchy continue to affect psychological safety within medical teams. Simulation training facilitated interprofessional relationships, attitudes toward teamwork, self-efficacy, and group communication. The findings of this review suggest that psychological safety may be developed through simulation training. The quality of patient care is improved when all members of medical teams have the ability and motivation to communicate effectively.

Food safety risk brought by environmental pollution seriously threatens human health and affects national economic and social development. In particular, heavy metal pollution and nutrient deficiency have caused regional diseases. Thus, the purpose of this paper is to present a risk early warning method of food safety considering environmental and nutritional factors.

A novel risk early warning modelling method based on the long short-term memory (LSTM) neural network integrating sum product based analytic hierarchy process (AHP-SP) is proposed. The data fuzzification method is adopted to overcome the uncertainty of food safety detection data and the processed data are viewed as the input of the LSTM. The AHP-SP method is used to fuse the risk of detection data and the obtained risk values are viewed as the expected output of the LSTM. Finally, the proposed method is applied on one group of sterilized milk data from a food detection agency in China.

The experimental results show that compared with the back propagation and the radial basis function neural networks, the proposed method has higher accuracy in predicting the development trend of food safety risk. Moreover, the causal factors of the risk can be figured out through the predicted results.

The proposed modelling method can achieve accurate prediction and early warning of food safety risk, and provide decision-making basis for the relevant departments to formulate targeted risk prevention and control measures, thereby avoiding food safety incidents caused by environmental pollution or nutritional deficiency.

The growing awareness of climate risks associated with food safety issues has drawn the attention of stakeholders urging the food industry to carry out a sustainable food safety management system (FSMS). This study aims to investigate whether the critical success factors (CSFs) of sustainable FSMS can contribute to achieving climate neutrality, and how the adoption of FSMS 4.0 supported by the Industry Revolution 4.0 (IR 4.0) technologies moderates the impact of the CSFs on achieving climate neutrality.

Survey data from 255 food production firms in China and Vietnam were utilised for the empirical analysis. The research hypotheses were examined using structural equations modelling (SEM) with route analysis and bootstrapping techniques.

The results show that top management support, human resource management, infrastructure and integration appear as the significant CSFs that directly impact food production firms in achieving climate neutrality. Moreover, the results demonstrate that the adoption of FSMS 4.0 integrated with the three components (ecosystems, quality standards and robustness) significantly moderates the impact of the CSFs on achieving climate neutrality with lower inputs in human resources, infrastructure investment, integration and external assistance, and higher inputs in strengthening food safety administration.

This study provides empirical findings that fill the research gap in understanding the relationship between climate neutrality and the CSFs of sustainable FSMS while considering the moderating effects of the FSMS 4.0 components. The results provide theoretical and practical insights into how the food production sector can utilise IR 4.0 to attain sustainable FSMS for achieving climate neutrality.

Slope stability analysis is essential for ensuring the safe design of road embankments. While various conventional methods, such as the finite element approach, are used to determine the safety factor of road embankments, there is ongoing interest in exploring the potential of machine learning techniques for this purpose.

Within the study context, the outcomes of the ensemble machine learning models will be compared and benchmarked against the conventional techniques used to predict this parameter.

Generally, the study results have shown that the proposed machine learning models provide rapid and accurate estimates of the safety factor of road embankments and are, therefore, promising alternatives to traditional methods.

Although machine learning algorithms hold promise for rapidly and accurately estimating the safety factor of road embankments, few studies have systematically compared their performance with traditional methods. To address this gap, this study introduces a novel approach using advanced ensemble machine learning techniques for efficient and precise estimation of the road embankment safety factor. Besides, the study comprehensively assesses the performance of these ensemble techniques, in contrast with established methods such as the finite element approach and empirical models, demonstrating their potential as robust and reliable alternatives in the realm of slope stability assessment.

The purpose of this paper is to present a new approach in viewing the control of safety at crude oil exploration platforms.

The approach utilized in this work is the fusion of artificial neural network and fuzzy logic. The approach is adopted in view of the better presentation of solutions to the safety control problem that neuro‐fuzzy exhibits. It is better than the individual application of either artificial neural network or fuzz logic to the problem at hand. The model captures uncertainties and imprecision that are prevalent in the quantification or data gathering stage of safety control measurement.

It was demonstrated that the application of neuro‐fuzzy is feasible. The results seem applicable to similar settings with similar system characteristics.

Since more confidence is obtained with the use of this more effective tool, there is improvement in decision making based on reliance on the model. Thus, the improved quality of decision made would positively affect lives of workers at the oil platforms or the materials or equipment used for exploration purposes.

The work is original in that it is the first time the neuro‐fuzzy methodology would be applied to offshore oil platform safety control.

The objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO₂ generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO₂) emitted by an industrial fire in a plant located in an industrial region site in Algeria.

The SA was defined for the following input parameters: wind speed, NO₂ emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.

The study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO₂ initial concentration has the most influence on the predicted NO₂ levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO₂ concentration estimation.

The study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

This new paper aims to combine the recent new contributions about direct data driven control and other safety property to form an innovative direct data driven safety control for aircraft flight system. More specifically, within the framework of direct data driven strategy, the collected data are dealt with to get the identified plant and designed controller. After reviewing some priori information about aircraft flight system, a closed loop system with the unknown plant and controller simultaneously is considered. Data driven estimation is proposed to identify the plant and controller only through the ratios of two correlation functions, computed from the collected data. To achieve the dual missions about perfect tracking and safety property, a new notion about safety controller is introduced. To design this safety controller, direct data driven safety controller is proposed to solve one constrain optimization problem. Then the authors apply the Karush–Kuhn–Tucker (KKT) optimality conditions to derive the explicit safety controller.

First, consider one closed loop system corresponding to aircraft flight system with the unknown plant and feed forward controller, data driven estimation is used to identify the plant and feed forward controller. This identification process means nonparametric estimation. Second, to achieve the perfect tracking one given transfer function and guarantee the closed loop output response within one limited range simultaneously, safety property is introduced. Then direct data driven safety control is proposed to design the safety controller, while satisfying the dual goals. Third, as the data driven estimation and direct data driven safety control are all formulated as one constrain optimization problem, the KKT optimality conditions are applied to obtain the explicit safety controller.

Some aircraft system identification and aircraft flight controller design can be reformulated as their corresponding constrain optimization problems. Then through solving these constrain optimization problems, the optimal estimation and controller are yielded, while satisfying our own priori goals. First, data driven estimation is proposed to get the rough estimation about the plant and controller. Second, data driven safety control is proposed to get one safety controller before our mentioned safety concept.

To the best of the authors’ knowledge, some existing theories about nonparametric estimation and tube model predictive control are very mature, but few contributions are applied in practice, such as aircraft system identification and aircraft flight controller design. This new paper shows the new theories about data driven estimation and data driven safety control on aircraft, being corresponded to the classical nonparametric estimation and tube model predictive control. Specifically, data driven estimation gives the rough estimations for the aircraft and its feed forward controller. Furthermore, after introducing the safety concept, data driven safety control is introduced to achieve the desired dual missions with the combination of KKT optimality conditions.

The purpose of this paper is to understand the concerns and factors that impact on hospital quality and safety, particularly related to use of performance data, within a setting of devolved governance.

This qualitative study used thematic analysis of interviews with public hospital medical directors. For additional context, findings were framed by themes from a review of hospital safety and quality in the same jurisdiction.

Varying approaches and levels of complexity were described about what and how performance data are reviewed, prioritised, and quality improvements implemented. Although no consistent narrative emerged, facilitators of improvement were suggested relating to organisational culture, governance, resources, education, and technologies. These hospital-level perspectives articulate with and expand on the system-level themes in a state-wide review of hospital safety and quality.

The findings are not generalisable, but point to an underlying absence of system-wide agreement on how to perceive, retrieve, analyse, prioritise and action hospital performance data.

Lack of electronic medical records and an inefficient incident reporting system limits the extent to which performance and incident data can be analysed, linked and shared, thus limiting hospital performance improvement, oversight and learning.

Variable approaches to quality and safety, standards of care, and hospital record keeping and reporting, mean that healthcare consumers might expect inconsistency across Victorian hospitals.

The views of medical directors have been little researched. This work uses their voice to better understand contextual factors that situate and impact on hospital quality and safety towards understanding the mixed effectiveness of hospital quality improvement strategies.

This paper aims to report on a study that aims to establish a list of systematically classified key performance indicators (KPIs) that are useful for hospital facilities management (FM).

A review of literature was conducted to identify indicators that are applicable to hospital FM. Each indicator was classified using a phase–hierarchy (P-H) model, which is a two-dimensional matrix comprising three phases (input, process and output) of facilities services delivery and three hierarchical FM levels (operational, tactical and strategic). The classified indicators were further shortlisted via a focus group study.

From the literature review, 61 indicators were identified as applicable to hospital FM. Most of the indicators, according to the P-H model, are for evaluating the FM input or output phase, at the strategic or tactical level. Further refinement and shortlisting of the indicators by the focus group experts resulted in 18 KPIs, which fall into 4 aspects: “physical”, “safety”, “environmental” and “financial”.

The study illustrates that the P-H model is useful for classifying the performance indicators systematically along the two fundamental FM dimensions – phase and hierarchy. Further research may use this model to classify performance indicators in other contexts.

The method of this study can be adapted for use in identifying, classifying and shortlisting FM performance indicators for other types of buildings. The shortlisted KPIs can be used for assessing the FM performance of hospitals.

To the best of the authors’ knowledge, this study is the first of its kind that used the P-H model to classify hospital FM performance indicators.

This paper aims to improve operational efficiency and minimize accident frequency in cold storage facilities through adopting an effective occupational safety and health program. The hidden knowledge can be extracted from the warehousing operations to create the comfortable and safe workplace environment.

A fuzzy association rule-based knowledge management system is developed by integrating fuzzy association rule mining (FARM) and rule-based expert system (RES). FARM is used to extract hidden knowledge from real operations to establish the relationship between safety measurement, personal constitution and key performance index measurement. The extracted knowledge is then stored and adopted in the RES to establish an effective occupational and safety program. Afterwards, a case study is conducted to validate the performance of the proposed system.

The results indicate that the aforementioned relationship can be built in the form of IF-THEN rules. An appropriate safety and health program can be developed and applied to all workers, so that they can follow instructions to prevent cold induced injuries and also improve the productivity.

Because of the increasing public consciousness of occupational safety and health, it is important for the workers in cold storage facilities where the ambient temperature is at/below 10°C. The proposed system can address the social problem and promote the importance of occupational safety and health in the society.

This study contributes to the knowledge management system for improving the occupational safety and operational efficiency in the cold storage facilities.

Table grape is one of the most important horticultural crops in China. However, quality and safety have become major constraints for the further development of the Chinese table grape industry, which is dominated by small‐scale vinegrowers. Strategic partnership has been regarded as an effective mechanism to manage the supply chain. The purpose of this paper is to examine the impact of a supply chain model based on different strategic partnerships on the effect of safety and quality control and small‐scale vinegrowers' performance.

The field survey was performed in seven representative districts in China, including Inner Mongolia, Gansu, Tianjin, Liaoning, Shandong, Jiangsu and Zhejiang Provinces. A semi‐structured interview was adopted as the data collection method. Both quantitative and qualitative data were generated and analyzed in this research. All quantitative analyses were conducted using EXCEL 2003 version and a comparative analysis was conducted to test the hypotheses.

The results show that modern supply chains are more efficient than the traditional one in improving the effect of safety and quality control and small‐scale vinegrowers' performance.

This paper first identified different models of table grape supply chain based on different strategic partnerships, and then analyzed the impact of supply chain model on the effect of quality and safety control and small‐scale vinegrowers' performance. It concludes with some policy suggestions relative to further development of the table grape industry in China.

The construction industry is considered worldwide to be a dangerous industry because of its high rate of fatal accidents and serious injuries. This study aims to find ways to improve this situation by focusing on how to improve competence on health and safety (H&S) among newly graduated construction engineers in Norway.

In Norway, the regional safety representatives (RSRs) are considered to be cornerstones in ensuring H&S in the construction industry. Information in this study was gathered by conducting semi-structured in-depth interviews with 15 RSRs.

The results show that RSRs believe that while construction engineers have sufficient theoretical competence on H&S; however, many seem to lack the practical competence needed to ensure and implement H&S measures. The informants underline the importance of work practice on construction sites as part of the education as key to improve H&S competence among graduates.

Input on possible improvements is collected from only one group of stakeholders in the industry. Other stakeholders might have valuable input that should be considered before concluding what improvements in the education are most effective.

The findings may help to decide upon and implement improvements in the education of construction engineers and, thus, contribute to a safer and healthier industry in the future.

The stakeholder perspective of RSRs on education of construction engineers has not been considered in earlier research despite the fact that they represent vital competence on H&S in the construction industry. This study aims to fill some of this gap.

India is in the process to achieve an important place in $2,000bn global food trade. In order to achieve this goal, there is a need to develop a food safety system which is well written down in line with international practices that are highly coordinated based on self-compliance to assure consumer protection. Accordingly, many organizations undergo assessment of their food safety system to verify compliance internally as well as externally. The purpose of this paper is to provide insight on the critical factors and benefits by evaluating the food safety assessment practices.

A questionnaire-based survey is conducted among 96 Indian food business operators and regulators involved in assessment practices to obtain critical factors for the assessment of food safety practices. The questionnaire captures indicators for motivations or challenges and benefits of food safety assessment to identify critical factors using exploratory factor analysis. Model for the food safety assessment practices was developed based on multiple regression analysis by determining the impact of factors on the benefits of food safety assessment.

This paper identifies four factors responsible for assessing food safety practices, namely, business-centric approach, legislative needs, technical practices and organization resentment as a combination of reasons and challenges along with two benefits risk: protection and organization reinforcement. The regression analysis indicates that the organization reinforcement gets positively impacted by business and technical practices and negatively by organization resentment. Risk protection has a significant relationship with legislative needs.

This is the first attempt to systematically explore the factors around the assessment of food safety practices in India. This study provides inputs for the practical application of managers and regulators.

Loitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.

To address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).

The proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.

The proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

The “economic miracle” in postwar Italy was accompanied by a rapid increase in the industrial accident and illness rates. Italian workers demanded occupational safety and health enforcement mechanisms that would be more accessible to grass‐roots workers' groups and unions. In the early 1970s local “Occupational Medicine Services” were voluntarily established in many regions. The entire health care system was decentralised in 1978, giving regions exclusive authority to implement occupational safety and health standards within Local Health Units (USLs). The concrete results of these reforms are investigated and the validity of the assumptions of the calls for decentralisation. The difficulties encountered by leftist‐administered regions in attempting to translate their political commitments into significant health and safety improvements are documented. The track‐record of the USLs is examined. An ironic consequence of decentralisation has been that the concentration of all health care activities in the USL has swallowed up occupational safety and health. As a result it is less politically visible and less responsive to worker input.

New product development (NPD) projects are strategically important for firms’ operations but suffer from high failure rates. Leadership is a key factor for project success. However, in contrast to positive project leadership, project managers’ knowledge hiding has received little attention. Drawing on the input-mediator-output (IMO) framework and model of work team resilience, we explored the effect of project managers’ destructive knowledge hiding (i.e. evasive hiding and playing dumb) on project team performance (i.e. efficiency and effectiveness) and the serial indirect effect through team psychological safety and transactive memory systems.

We conducted a time-lagged multiple-sourcing investigation of Chinese high-tech firms and tested the hypotheses using data collected from 105 NPD project teams.

Our findings demonstrated that project managers’ knowledge hiding negatively affects NPD project team performance and indirectly negatively affects transactive memory systems through team psychological safety. Moreover, project managers’ knowledge hiding exerts a negative indirect effect on team performance through team psychological safety and transactive memory systems in serial.

This study contributes to the literature on operations management (OM) by broadening our understanding of the connection between project managers' destructive knowledge hiding and the failure of NPD projects. In providing such insight, it also offers practical guidance for overcoming team-level obstacles arising from project managers' knowledge hiding.

Organisations either keep spares for their own use, or‐for‐sale to other organisations. In either case, the ultimate need is to be able to replace worn or defective parts in operational machinery or equipment. In an economic sense, spares are kept to meet the needs of the situation in the cheapest way.

Computer vision (CV) offers a promising approach to transforming the conventional in-person inspection practices prevalent within the construction industry. However, the reliance on centralized systems in current CV-based inspections introduces a vulnerability to potential data manipulation. Unreliable inspection records make it challenging for safety managers to make timely decisions to ensure safety compliance. To address this issue, this paper proposes a blockchain (BC) and CV-based framework to enhance safety inspections at construction sites.

This study adopted a BC-enhanced CV approach. By leveraging CV and BC, safety conditions are automatically identified from site images and can be reliably recorded as safety inspection data through the BC network. Additionally, by using this data, smart contracts coordinate inspection tasks, assign responsibilities and verify safety performance, managing the entire safety inspection process remotely.

A case study confirms the framework’s applicability and efficacy in facilitating remote and reliable safety inspections. The proposed framework is envisaged to greatly improve current safety inspection practices and, in doing so, contribute to reduced accidents and injuries in the construction industry.

This study provides novel and practical guidance for integrating CV and BC in construction safety inspection. It fulfills an identified need to study how to leverage CV-based inspection results for remotely managing the safety inspection process using BC. This work not only takes a significant step towards data-driven decision-making in the safety inspection process, but also paves the way for future studies aiming to develop tamper-proof data management systems for industrial inspections and audits.

The purpose of this paper is to report a study in which the feasibility of conducting probabilistic finite element analysis (FEA) for crane hook design has been explored.

This paper presents the results of probabilistic analysis, where in the input random variables are varied and corresponding variations in the output parameters were observed. In this study, material properties and load have been considered as random input variables and the maximum stress, maximum deflection variations were considered as output random variables.

The probability of occurrence of output variation and the sensitivity of output variables on the input variables are the important results generated from this analysis. By performing this probabilistic analysis, the random selection of factor of safety could be avoided.

The implementation study has been carried out for a single product.

The usage of the approach will indicate the importance of probabilistic analysis in product design and development process. This process will enable the organization to compete in the global market.

A case study has been reported to indicate the feasibility of performing probabilistic FEA for crane hook design. Hence, the contributions are original.

The fluctuation of construction fatalities is influenced by both urbanization and economic levels. This study aims to understand the impact of Chinese construction economy development on construction accidents, providing valuable insights for enhancing construction safety and promoting sustainable development in construction.

The Kuznets curve model, multiple linear regression model, and data envelopment analysis (DEA) model are employed to process data sets spanning from 1992 to 2021 for examining the relationship between construction fatalities and the construction economy in China.

Significant correlations have been found between construction fatalities and the construction economy in China. Over the past three decades, as the total output value of construction increased, there have been upward, downward, and downward trends in per capita construction area, the mortality rate per million square meters, and the mortality rate per ten thousand persons respectively. However, it is worth noting that since 2015, there has been a slight upward trend in the fitted U-shaped curve depicting the relationship between the mortality rate per ten thousand persons and the construction economy. This specific trend necessitates the attention of construction safety policymakers. The growth of the construction economy is found to exhibit negative, positive, and positive correlations with the number of construction fatalities, construction area, and the number of employed persons respectively. The changing trends observed in the Kuznets curve model analysis align with the evaluation results obtained from the DEA-based model.

The research offers effective means to identify superior and inferior performance in macro construction safety, providing valuable references for construction safety policymakers to design effective safety strategies and enhance work safety conditions.

Frequent food safety incidents caused widespread consumer concerns. Even though food safety is one of the weakest links in the fresh food supply chain and influences consumer food choice in ways different from the quality dimension, this factor is hardly proposed as one of the key traditional supplier selection criteria (e.g. quality, delivery, and price) in the literature. The purpose of this paper is to develop a business process decision model to assess the non-compensating food safety sub-criteria in order to disqualify fresh food suppliers that cannot reach the minimum threshold for low probable food safety failure. The preferred fresh food suppliers can minimize the risk of food safety failure and the associated huge food safety failure costs spanning from private consumer anguish to social distress that cause unbearable costs of sales loss and damage to brand image in business.

This study proposes a novel approach that combines several well-established multi-criteria decision making (MCDM) techniques, including fuzzy AHP (FAHP), TOPSIS, and ELECTRE, and innovatively apply to analyze supplier performance and prioritize potential fresh food suppliers. This hybrid business process model can enforce compliance to all the five non-compensatory sub-criteria of food safety. Since ELECTRE is a non-compensatory MCDM method, it is therefore particularly applicable for disqualifying high risk fresh food suppliers from further full scale supplier performance evaluation by FAHP and TOPSIS. This hybrid business process decision model is able to capitalize on the strengths of these MCDM methods and offset their deficiencies.

This study uses data of an international supermarket chain to validate feasibility of the proposed model. Results indicate that this model is able to assess the non-compensating food safety sub-criteria via the ELECTRE method in order to disqualify fresh food suppliers that cannot reach the minimum threshold for low probable food safety failure. Only the preferred suppliers with the required food safety capability can proceed to the second stage of the supplier selection process. Assessment via the TOPSIS method reveals the ranking order of those top performing suppliers according to their relative scores along all the supplier selection criteria. The TOPSIS ranking results with the selection of the suppliers C, E, A, and F are robust and consistent across all the different scenarios.

Application to the fresh food industry is possible with the aid of the MCDM methods. The contribution to the body of knowledge in this teaching and research field demonstrates the importance of first identifying the order qualifier for disqualifying those suppliers that do not satisfy the food safety requirements via the ELECTRE method. The proposed assessment procedure complies with the regulatory policy on food safety, and would influence public policy in applying the best practice of food safety regulation. Without first qualifying the potential suppliers on the basis of food safety, wrong decision can be made to select those high food risk suppliers that have relatively higher overall scores in other supplier selection criteria. Using the assessment results has positive economic and commercial impact on the purchasing managers to formulate appropriate purchasing and supplier development strategy to enhance supplier’s food safety performance, whilst maximizing the overall supplier portfolio performance. The improved supplier’s food safety performance will certainly benefit the society’s quality of life as well.

Based on the analytical MCDM methods of FAHP, TOPSIS, and ELECTRE, purchasing managers can operationalize the Hill’s framework of order qualifier and winner that has primarily been used in the literature and manufacturing industry. This study represents the first move to innovatively apply the FAHP, TOPSIS, and ELECTRE methods to operationalize the Hill’s framework of order qualifier and winner that has primarily been used in the literature and manufacturing industry. Application to the fresh food industry to validate the feasibility of the proposed model has been conceived and implemented in this study. Analysis of the data inputs of a supermarket chain via the three MCDM methods generate the results that fulfill the purpose of achieving the research objective of identifying and managing the supplier base that can deliver the best supplier performance, conditional on first passing the fresh food safety test.