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Natural disasters cause serious operational risks and disruptions, which further impact the food supply in and around the disaster-impacted area. Resilient functions in the supply chain are required to absorb the impact of resultant disruptions in perishable food supply chains (FSC). The present study identifies specific resilient functions to overcome the problems created by natural disasters in the FSC context.

The quality function deployment (QFD) method is utilized for identifying these relations. Further, fuzzy term sets and the analytical hierarchy process (AHP) are used to prioritize the identified problems. The results obtained are employed to construct a QFD matrix with the solutions, followed by the technique for order of preference by similarity to the ideal solution (TOPSIS) on the house of quality (HOQ) matrix between the identified problems and functions.

The results from the study reflect that the shortage of employees in affected areas is the major problem caused by a natural disaster, followed by the food movement problem. The results from the analysis matrix conclude that information sharing should be kept at the highest priority by policymakers to build and increase resilient functions and sustainable crisis management in a perishable FSC network.

The study suggests practical implications for managing a FSC crisis during a natural disaster. The unique contribution of this research lies in finding the correlation and importance ranking among different resilience functions, which is crucial for managing a FSC crisis during a natural disaster.

There is a growing body of literature discussing the green logistics practices (GLPs) that companies could introduce to reduce the logistics environmental impact. Current approaches also identify several influencing factors within firms that could serve as barriers to, or enablers of, GLPs. However, less is known about the role of extra-firm stakeholders, even though these are crucial to operationalizing green logistics effectively. This study merges current theoretical understanding with empirical evidence to provide a detailed stakeholder analysis of GLPs.

Using stakeholder theory as a theoretical lens, the authors aimed at offering a mid-range contribution by conducting multiple embedded case studies examining Italian logistics service providers and shippers. GLPs and the related influencing factors were examined as sub-units of analysis within broader companies' environmental sustainability strategies.

The authors identified cascading effects among factors influencing the adoption of GLPs (e.g. key economic factors are affected by external factors which also influence organizational and collaboration factors). These effects are moderated by interdependencies between primary and secondary stakeholders, and the study highlights the prominent involvement of secondary stakeholders, such as final consumers.

This paper contributes to better understanding how and why companies adopt GLPs, emphasizing the wide set of stakeholders involved and illustrating how different stakeholders impact on GLPs adoption by affecting a set of influencing factors. By combining insights from the available literature with contemporary empirical data, the authors emphasize how Logistics Service Providers (LSPs) and shippers can no longer address the adoption of GLPs as “focal companies”, but only as part of a “focal network of interconnected stakeholders”, all of them influencing GLPs adoption.

Businesses are increasingly vulnerable and exposed to physical climate change risks, which can cascade through local, national and international supply chains. Currently, few methodologies can capture how physical risks impact businesses via the supply chains, yet outside the business literature, methodologies such as sustainability assessments can assess cascading impacts.

Adopting a scoping review framework by Arksey and O'Malley (2005) and the PRISMA extension for scoping reviews (PRISMA-ScR), this paper reviews 27 articles that assess climate risk in supply chains.

The literature on supply chain risks of climate change using quantitative techniques is limited. Our review confirms that no research adopts sustainability assessment methods to assess climate risk at a business-level.

Alongside the need to quantify physical risks to businesses is the growing awareness that climate change impacts traverse global supply chains. We review the state of the literature on methodological approaches and identify the opportunities for researchers to use sustainability assessment methods to assess climate risk in the supply chains of an individual business.

This paper aims to develop a conceptual framework that jointly considers Environmental, Social and Governance (ESG) factors and organisational resilience (OR) components to ameliorate organisations' understanding of sustainability’s overall requirements and related decision-making processes.

This paper combines ESG and OR through a 3x3 conceptual matrix, where ESG factors are listed along the vertical axis and OR components along the horizontal axis. This results in nine quadrants, which have been read according to two arrangements: (1) static, looking at the specific characteristics of each single quadrant, and (2) dynamic, investigating the relationships between the different quadrants according to the system theory (ST) lens.

The integration between ESG and OR results in nine organisational typologies, each characterised by a specific focus: (1) green visioning, (2) eco ethos, (3) climate guard, (4) inclusive strategy, (5) empathy ethos, (6) community shield, (7) ethical blueprint, (8) integrity ethos and (9) compliance guard. These typologies and related focuses determine the different strategic options of organisations, the decision-making emphasis concerning ESG factors and OR components and the organisation’s behaviour concerning its internal and external environment. According to ST, the nine typologies interact with each other, emphasising the existence of interconnectedness, interdependence and cascading effects between ESG and OR.

The paper represents a unique attempt to interrelate ESG factors and OR components according to a ST lens, emphasising the dynamic nature of their interactions and organisations’ need for continuous adaptation and learning to make decisions that create sustainable long-term value.

The purpose of this paper is to investigate risk propagation and resilience in the agribusiness supply chain (ASC).

The paper undertakes a systematic literature review (SLR). Overall, 94 articles from six databases published between 2000 and 2022 underwent descriptive and thematic analysis.

There is a lack of research on risk propagation and resilience in the ASC for more than two decades. Accordingly, this research fills the gap in the extant literature by advancing the construct of risk propagation and resilience in the ASC and developing a framework proposing directions in risk propagation and resilience in ASC research.

Firstly, only the Web of Science and Scopus databases were mostly used as primary sources while other databases were used as secondary sources to validate search results. Secondly, SLR is based on the peer-reviewed articles, books and conference papers; other non-academic sources relevant to the topic were not included in this paper.

The paper offers a set of constructs for understanding risk propagation and resilience in the ASC, develops a framework proposing directions in risk propagation and resilience in the ASC research and recommends three key themes for future research directions, namely, keep updated with recent constructs of risk propagation and resilience in the ASC, conduct case studies based on empirical studies to determine the current risk dependency and propagation in the ASC and conduct case studies based on empirical studies to determine resilience and sustainability in the ASC.

The purpose of this article is to discuss how the mastery of resilience in operations and supply chains plays a significant role in the transition to a more sustainable future. Furthermore, it is supposed to propose avenues for future research on operational and supply chain resilience, interacting with the sustainability literature in our field.

A conceptual review of resilience and sustainability themes within operations and supply chain management research is conducted. Reflections on the topic are informed by relevant literature published over the last decade.

The major conceptual contributions are threefold: (1) This article elaborates on the understanding of operational resilience and supply chain resilience concepts and reviews their respective primary research streams. (2) It proposes resilience as the missing element in the pursuit of excellence in organizations that want to contribute to a more sustainable future. (3) The article offers a research framework that provides a future research agenda at the intersection of resilience and sustainability in operations and supply chain management research.

The article highlights gaps in current research and illustrates further areas of research that need to be addressed to maximize the contribution of operations and supply chain management research in supporting practitioners to achieve a more sustainable future.

The advent of artificial intelligence (AI) in the accounting landscape marks a significant shift, promising gains in efficiency and accuracy but also eliciting concerns about job displacement (JD) and broader socio-economic implications. This study aims to provide an in-depth understanding of how AI’s integration in accounting contributes to JD, reshapes decision-making processes and reverberates across economic and social dimensions. It also offers evidence-based policy recommendations to mitigate adverse outcomes.

Leveraging a cross-sectional survey disseminated through Facebook, this research used snowball sampling to target a diverse cohort of accounting professionals. The collected data were subjected to meticulous analysis through descriptive and regression models, facilitated by SmartPLS 4 software.

The analysis revealed a significant correlation between AI’s increasing role in accounting and a heightened rate of JD. This study found that this displacement is not isolated; it has tangible repercussions on decision-making paradigms, economic well-being, professional work dynamics and social structures. These insights corroborate existing frameworks, including, but not limited to, theories of technological unemployment and behavioural adjustments.

Although providing valuable insights, this study acknowledges limitations such as the restricted sample size, the cross-sectional nature of the survey and the inherent biases of self-reported data. Future research could aim to extend these initial findings by adopting a longitudinal approach and potentially integrating external data sources.

As AI technology becomes increasingly ingrained in accounting practices, there is an urgent need for coordinated action among stakeholders. Policy recommendations include focused efforts on talent retention, investment in upskilling programs and the establishment of support mechanisms for those adversely affected by AI adoption.

By synthesising a range of theoretical perspectives, this study offers a comprehensive exploration of AI’s multi-dimensional impacts on the accounting profession. It stands out for its nuanced examination of JD and its economic and social implications, thereby contributing to both academic discourse and policy formulation. This work serves as an urgent call to action, highlighting the need for strategies that both exploit AI’s potential benefits and protect the workforce from its disruptive impact.

The first objective of this article is to analyse the reasons to pursue inclusive growth, that is economic growth accompanied by a reduction of social inequalities in different dimensions. The second objective of the article is to develop a systematised framework to understand the different channels and enablers by which heritage can contribute to inclusive growth through a review of specialised literature.

The methodology of this article is based on an exhaustive review of existing literature around models of economic development and their ability to decrease social inequalities. It critically reviews theoretical and empirical studies on existing economic approaches and links them with the heritage policy field.

The article finds that countries should pursue inclusive development since it is a fundamental condition for social cohesion, trust and society's overall well-being and because it enables economic growth to be sustainable through time. It also identifies four channels through which heritage can contribute to inclusive development: in its public good dimension, in its capacity to equalise opportunities, in its ability to reduce social, educational and health disparities and in its capacity to decrease spatial income inequalities through regeneration processes.

The framework, that is developed to categorise the different channels and enablers through which heritage could contribute to inclusive growth, is not empirically tested. Further research could approach this by estimating a difference in difference model. However, data limitations could limit this objective in the short-term.

Its originality relies in the development of a conceptual framework that is aimed at shaping heritage policies that target, at the same time, the reduction of inequalities and economic growth.

With the widespread use of online communications, users are extremely vulnerable to a myriad of deception attempts. This study aims to extend the literature on deception in computer-mediated communication by investigating whether the manner in which popularity information (PI) is presented and media richness affects users’ judgments.

This study developed a randomized, within and 2 × 3 between-subject experimental design. This study analyzed the main effects of PI and media richness on the imitation magnitude of veracity judges and the effect of the interaction between PI and media richness on the imitation magnitude of veracity judges.

The manner in which PI is presented to people affects their tendency to imitate others. Media richness also has a main effect; text-only messages resulted in greater imitation magnitude than those viewed in full audiovisual format. The findings showed an interaction effect between PI and media richness.

The findings of this study contribute to the information systems literature by introducing the notion of herd behavior to judgments of truthfulness and deception. Also, the medium over which PI was presented significantly impacted the magnitude of imitation tendency: PI delivered through text-only medium led to a greater extent of imitation than when delivered in full audiovisual format. This suggests that media richness alters the degree of imitating others’ decisions such that the leaner the medium, the greater the expected extent of imitation.

When a process experiences an out-of-control condition, identification of the change point is capable of leading practitioners to an effective root cause analysis. The change point addresses the time when a special cause(s) manifests itself into the process. In the statistical process monitoring when the chart signals an out-of-control condition, the change point analysis is an important step for the root cause analysis of the process. This paper attempts to propose a model approaching the artificial neural network to identify the change point of a multistage process with cascade property in the case that the process is modeled properly by a simple linear profile.

In practice, many processes can be modeled by a functional relationship rather than a single random variable or a random vector. This approach of modeling is referred to as the profile in the statistical process control literature. In this paper, two models based on multilayer perceptron (MLP) and convolutional neural network (CNN) approaches are proposed for identifying the change point of the profile of a multistage process.

The capability of the proposed models are evaluated and compared using several numerical scenarios. The numerical analysis of the proposed neural networks indicates that the two proposed models are capable of identifying the change point in different scenarios effectively. The comparative sensitivity analysis shows that the capability of the proposed convolutional network is superior compared to MLP network.

To the best of the authors' knowledge, this is the first time that: (1) A model is proposed to identify the change point of the profile of a multistage process. (2) A convolutional neural network is modeled for identifying the change point of an out-of-control condition.