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A traceability system is a key success factor in global food trade, but implementing it in vegetable oil industry is one of the most difficult undertakings in food supply chain management. This study aims to (1) identify typical operational barriers in the implementation of bulk-liquid traceability system in the Indonesian vegetable oil industry by considering the perspective of experts and (2) model the relationship between the barriers structurally in order to improve the reliability of the traceability system.

To do so, data from in-depth interviews with experts were examined by using content analysis. Then the authors used a combination of decision-making trial and evaluation laboratory (DEMATEL), interpretive structural modelling (ISM) and matrice d'impacts croisés multiplication appliqué un classement (MICMAC) to construct the hierarchical model and to cluster the typical barriers based on their driving power and dependence power.

In total, 20 typical traceability barriers along the internal chain (supplier-input-process-output-customer) were identified. The interrelationships between these barriers were modeled in a hierarchical structure, seeking to answer why it is difficult to implement a traceability system and what actions should be taken to remove these barriers.

The model can shed light on how to manage barriers in bulk-liquid food commodity industry, especially in the vegetable oil industry. An action map has been proposed to overcome the operational barriers. This model will also help tracing the critical points of the traceability system.

Compared to other food commodities, operational barriers in vegetable oil chain has never been studied specifically. In fact, there are many operational aspects that hinder traceability. The Indonesian context entails social, economic and environmental factors as well, so it can inform decision-makers in formulating an action map.

By exploring the halalness and food safety risks from the perspective of technology and the relationship among them, this study aims to make quantitative predictions of such risks in the broiler supply chain to determine the critical control points (CCPs) in Hazard Analysis Critical Control Point (HACCP).

This study integrates Interpretive Structural Modeling (ISM) and Bayesian Network (BN) to achieve the objectives. Data were collected from focus group discussions (FGDs) with experts and direct observations at the broiler supply chain.

This paper identified 19 risks in the Indonesian broiler supply chain. The risk for halalness and food safety reached 30.92%, indicating that assuring halalness and food safety remains improbable or unlikely. The two CCPs of halalness and food safety are the knife’s sharpness and the vehicle’s storage temperature.

This study quantifies the halalness and food safety risks in the Indonesian broiler supply chain, but it only involves one step forward and one step backward in the slaughterhouse’s chain.

The findings can provide insights for stakeholders, such as business owners, employees, management system auditors and consumers, regarding the critical control points of halalness and food safety in the broiler supply chain to improve the halalness and food safety management systems.

This study’s novelty lies in the examination of halalness and food safety risks using a risk prediction model to determine CCPs for the HACCP plan in the broiler supply chain in Indonesia.

Businesses nowadays face unprecedented pressures from stakeholder groups to become more transparent by issuing comprehensive reports describing their environmental, social and governance (ESG)-related activities, strategies and policies. This paper’s primary motivation is to understand which ESG disclosure factors are relevant for large Czech companies.

To achieve the above-stated goal, the total ESG disclosure index, consisting of three subindexes (ESG) was constructed and calculated for the 100 largest Czech companies. Furthermore, the relationships between firm-level factors and ESG disclosure indexes were estimated by using censored regression models.

This study found that revenue, number of employees and profitability positively influenced the total ESG disclosures. On the level of the three ESG components, this study found that revenue positively impacted environmental and governance disclosures while the number of employees positively affected social and governance disclosures. Moreover, profitability affected social and governance disclosures positively for large Czech companies. However, this study did not observe a significant relationship between board attributes and ESG disclosures.

This paper extends academic literature on ESG disclosures by verifying the significance of firm-level factors in the context of Czech business realities before the adoption and transposition of the Corporate Sustainability Reporting Directive. More specifically, this study has investigated the ESG reporting together and separately for ESG factors. This separation is vital as firms vary in reporting processes across these factors.

This study aims to investigate the performance of Sustainable Development Goals (SDGs) of public listed companies (PLCs) in Malaysia through their SDGs disclosure. In addition, it examines the impact of integrated reporting (IR) quality on the SDGs’ performance.

Data are collected from an initial sample of Malaysia’s top 100 market-leading PLCs from 2016 to 2020. Univariate and multivariate analyses were used to test the research hypotheses.

The results reveal an increasing trend in SDGs’ performance. Companies contributing toward the 17 SDGs grew from 14% in 2016 to 78% in 2020. On a priority basis, the average score of the five years showed that the Malaysian PLCs are paying more attention to SDG 8 Decent Work and Economic Growth (53%); SDG 12 Responsible Consumption and Production (43%); and SDG 13 Climate Action (42%). In addition, the fixed effects regression analysis proves that companies with higher IR quality are more likely to provide better SDGs disclosure.

This study provides insights to policymakers, investors and management on the vital role of businesses in supporting the SDGs’ achievement and how IR reveals a turning point in achieving the United Nations SDGs’ agenda.

This study provides a clearer understanding of the activities seeking to achieve the SDGs and the influence of IR on them. This opens the debate for future research.

To the best of the authors’ knowledge, this study is a pioneer in examining whether the quality of IR influences SDGs disclosure among large companies in one of the emerging economies in Southeast Asia in its early application stage.

The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.