Search results

This study aims to aid theory building, the use of decision theory (DT) concepts in sustainable supply chain management (SSCM) research is examined.

An abductive approach considers two DT concepts, Snowden’s Cynefin framework for sense-making and Keeney’s value-focussed decision analysis, in a systematic literature review of 160 peer-reviewed papers in English.

Around 60 per cent of the papers on decision-making in SSCM come from operational research (OR), which makes explicit use of DT. These are almost all normative and rationalist and focussed on structured decision contexts. Some exceptions seek to address unstructured decision contexts via Complex Adaptive Systems or Soft Systems Methodology. Meanwhile, a second set, around 16 per cent, comes from business ethics and are empirical, behavioural decision research. Although this set does not explicitly refer to DT, the empirical evidence here supports Keeney’s value-focussed analysis.

There is potential for theory building in SSCM using DT, but the research only addresses SSCM research (including corporate responsibility and ethics) and not DT in SCM or wider sustainable development research.

Use of particular decision analysis methods for SSCM may be improved by better understanding different decision contexts.

The research shows potential synthesis with ethical DT absent from DT and SCM research.

Empirical behavioural decision analysis for SSCM is considered alongside normative, rational analysis for the first time. Value-focussed DT appears useful for unstructured decision contexts found in SSCM.

Empirical, behavioural decision analysis for SSCM is considered alongside normative rational analysis for the first time. Value-focussed DT appears useful for unstructured decision contexts found in SSCM.

Green consumption is fundamental to sustainable development, as it involves adopting practices and technologies that reduce the environmental impact of human activities. This study aims to analyze the influence of consumers’ green orientation on their environmental concerns and green purchase decisions. Furthermore, the study investigates the mediating role of consumers’ environmental concerns in the relationship between pro-sustainable orientation and green purchase decisions.

This study uses a quantitative methodology, applying the partial least squares method to a sample of 927 Portuguese consumers of green products. The sample was collected through an online survey.

Perceived benefits and perceived quality of products play a positive and significant role in influencing green behavior, especially when consumers are endowed with greater environmental concerns. In addition, consumers’ awareness of the prices of green products and their expectations regarding the future benefits of sustainable consumption positively impact green consumption behavior, further intensifying their environmental concerns.

According to the present findings, companies should adopt a holistic and integrated approach to promote green consumption. This means creating premium eco-friendly products, communicating their benefits, addressing the cost factor, emphasizing the future impact of eco-friendly options and raising consumers’ environmental awareness.

It is critical that environmental education is a priority in schools and that there are political incentives for green behaviors. In addition, media campaigns can be an important tool to raise awareness in society.

The results of this study provide important insights for companies on consumer engagement in the circular economy. Deepening knowledge of the antecedents of consumers’ environmental concerns contributes to a deeper understanding of green purchasing decision behavior, allowing companies to support new business strategies.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

The research is based on a critically analyzed literature review focused on the unanticipated outcomes, trade-offs and tensions of sustainable operations and supply chain management (OSCM), including the articles selected for this special issue.

The authors introduce the key concepts, issues and theoretical foundations of this special issue on “The hidden side of sustainable operations and supply chain management (OSCM): Unanticipated outcomes, trade-offs and tensions”. The authors explore these issues within this context, and how they may hinder the authors' transition to more sustainable practices.

The authors present an overview of unanticipated outcomes, trade-offs, tensions and influencing factors from the literature, and identify how such problems may emerge. The model addresses these problems by highlighting the crucial effect of the underlying state of knowledge on sustainable OSCM decision-making.

The authors limited the literature review to journals that ranked 2 and above as defined by the Chartered Association of Business Schools Academic Journal Guide. The main implication for research is a call to focus attention on unanticipated outcomes as a starting point rather than only an afterthought. For practitioners, good intentions such as sustainability initiatives need careful consideration for potential unanticipated outcomes.

The study provides the first critical review of unanticipated outcomes, trade-offs and tensions in the sustainable OSCM discourse. While the literature review (including papers in this special issue) significantly contributes toward describing these issues, it is still unclear how such problems emerge. The model developed in this paper addresses this gap by highlighting the crucial effect of the underlying state of knowledge concerned with sustainable OSCM decision-making.

The purpose of this paper is to review the food supply chain management (FSCM) in terms of systems and implementations so that observations and lessons from this research could be useful for academia and industrial practitioners in the future.

A systematical and hierarchical framework is proposed in this paper to review the literature. Categorizations and classifications are identified to organize this paper.

This paper reviews total 192 articles related to the data-driven systems for FSCM. Currently, there is a dramatic increase of research papers related to this topic. Looking at the general interests on FSCM, research on this topic can be expected to increase in the future.

This paper only selected limited number of papers which are published in leading journals or with high citations. For simplicity without generality, key findings and observations are significant from this research.

Some ideas from this paper could be expanded into other possible domains so that involved parties are able to be inspired for enriching the FSCM. Future implementations are useful for practitioners to conduct IT-based solutions for FSCM.

As the increasing of digital devices in FSCM, large number of data will be used for decision-makings. Data-driven systems for FSCM will be the future for a more sustainable food supply chain.

This is the first attempt to provide a comprehensive review on FSCM from the view of data-driven IT systems.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

The purpose of the paper is to propose an effective approach of artificial intelligence (AI) addressing social-humanitarian reality comprising non-formalizable representation. The new task is to describe processes of integration of AI and humans in the hybrid systems framework.

Social-humanitarian dynamics contradict traditional characteristics of AI. Suggested methodology embraces formalized and non-formalized parts as a whole. Holonic and special convergent approaches are combined to ensure purposefulness and sustainability of collective decision-making. Inverse problem solving on topology spaces, control thermodynamics and non-formalizable (considering quantum and relativistic) semantics include observers of eigenforms of reality.

Collective decision-making cannot be represented only by formal means. Thus, this paper suggests the equation of hybrid reality (HyR), which integrates formalizable and non-formalizable parts conveying and coalescing holonic approaches, thermodynamic theory, cognitive modeling and inverse problem solving. The special convergent approach makes the solution of this equation purposeful and sustainable.

The suggested approach is far reaching with respect of current state-of-the-art technology; medium-term limitations are expected in the creation of cognitive semantics.

Social-humanitarian events embrace all phenomena connected with individual and collective human behavior and decision-making. The paper will impact deeply networked experts, groups of crowds, rescue teams, researchers, professional communities, society and environment.

New possibilities for advanced AI to enable purposeful and sustainable social-humanitarian subjects. The special convergent information structuring during collective decision-making creates necessary conditions toward the goals.

Soil quality is in decline in many parts of the world, in part due to the intensification of agricultural practices. Whilst economic instruments and regulations can help incentivise uptake of more sustainable soil management practices, they rarely motivate long-term behavior change when used alone. There has been increasing attention towards the complex social factors that affect uptake of sustainable soil management practices. To understand why some communities try these practices whilst others do not, we undertook a narrative review to understand how social capital influences adoption in developed nations. We found that the four components of social capital – trust, norms, connectedness and power – can all influence the decision of farmers to change their soil management. Specifically, information flows more effectively across trusted, diverse networks where social norms exist to encourage innovation. Uptake is more limited in homogenous, close-knit farming communities that do not have many links with non-farmers and where there is a strong social norm to adhere to the status quo. Power can enhance or inhibit uptake depending on its characteristics. Future research, policy and practice should consider whether a lack of social capital could hinder uptake of new practices and, if so, which aspects of social capital could be developed to increase adoption of sustainable soil management practices. Enabling diverse, collaborative groups (including farmers, advisers and government officials) to work constructively together could help build social capital, where they can co-define, -develop and -enact measures to sustainably manage soils.

This research aims to compare consumer responses to pro-environmental communication and appeals to recycle packaging when these messages come from a high-familiarity versus a low-familiarity brand.

Two online between-subjects experimental studies evaluate consumer perceptions and the willingness to comply with recycling appeals in response to pro-environmental communications from a high-familiarity versus a low-familiarity brand. To test the hypotheses, the studies examine the moderating role of sustainability habits and the mediating role of shared environmental responsibility.

Findings show that communicating a brand’s adoption of sustainable packaging is more salient to consumers when the appeal comes from a low-familiarity rather than a high-familiarity brand, especially when sustainability habits are weaker. The mediating role of shared environmental responsibility partly explains consumers’ commitment to act pro-environmentally.

Sustainability officials and policymakers should consider the impact of pro-environmental interventions that encourage collective recycling between brands and consumers. Practitioners are encouraged to examine revised waste management schemes such as extended producer responsibility programs to elicit the collaboration of consumers in initiatives that boost recycling and stimulate pro-environmental behaviors.

Using the diagnosticity–accessibility framework and habit theory, to the best of the author’s knowledge, this research is among the first to empirically examine the role of sustainability habits in consumer responses to pro-environmental brand communications. It also highlights consumers’ willingness to comply with brands’ take-back programs in a shared effort to reduce plastic waste and encourage a circular economy.