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Neo-institutional theories and their constructs have so far only received limited attention in supply chain management literature. As recent supply chain disruptions and their ripple effects affect actors on a broader institutional level, supply chains are confronted with multiple new and emerging, often conflicting, institutional demands. This study aims to unpack the notion of institutional complexity behind supply chain disruptions and present a novel institutional framework to lower supply chain susceptibility and increase supply chain resilience.

The authors identify the patterns of complexity that shape the supply chain susceptibility, namely, distance, diversity and ambiguity, and present three institutional responses to susceptibility to increase supply chain resilience, namely, institutional entrepreneurship, institutional alignment and institutional layering.

This paper analyses the current situational relevance to better understand the various and patterned ways how logics influence both supply chain susceptibility and the supply chain resilience. The authors derive six propositions on how complexity can be reduced for supply chain susceptibility and can be increased for supply chain resilience.

By expanding and extending research on institutional complexity to supply chains, the authors broaden how researchers in supply chain management view supply chain susceptibility, thereby providing managers with theory to think differently about supply chains and its resilience.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

A specific area of interest in the coaching literature is focused on exploring the intersection of personality and coaching; however, research has yet to explore whether coaching exerts reciprocal effects on personality traits (i.e. if personality trait change can accompany coaching). Utilizing the explanatory theoretical framing of the Demands-Affordances TrAnsactional framework (Woods et al., 2019), we propose that coaching may indirectly facilitate personality trait change by firstly enabling the coachee to reflect on their behaviors, second, implement desired behavioral changes which consequently facilitate personality trait change.

A quasi-experiment was conducted to explore coaching and personality trait change. Students participating in a demanding, work-based team simulation (N = 258), were assigned to either an intervention group (and received one-to-one coaching) or a control group (who received no intervention). Personality traits were measured before and after coaching and positioned as the dependent variable.

Results indicate that participants in the coaching group exhibited significant changes in self-reported agreeableness, conscientiousness, extraversion and core self-evaluations, which all significantly decreased after coaching; however, no change was observed for the control group.

We provide the first exploration of coaching and personality trait change, contributing to both the coaching literature, by providing evidence regarding the efficacy of coaching to facilitate personality trait change in coachees, and the personality literature, by highlighting coaching as an important tool for those interested in personality trait change. Our research also has implications for other interventions such as mentoring, as we provide support for the notion that interventions can support personality trait change.

The paper aims to clarify the relationship of various enablers of supply chain (SC) management like lean enablers, agile enablers and leagile enablers. It proposes modeling the enablers to find the most appropriate strategy or methodology for determining the lean enabled SC agility.

The paper proposed the fuzzy SWARA-WASPAS methodology for determining the role of lean in enabling the SC agility. Also in continuation the AHP methodology is applied to find the priority weightage and ranking of leagile enablers, and a comparative analysis is done to select the best approach among the above two methodologies so that it would be beneficial for all the stakeholders.

The paper provides the investigation and identification of 28 lean enablers, 11 agile factors which are highly responsible to affect any SC specially focusing of automobile sector. Apart from above 9 leagile enablers were also identified in the paper. Finally, the comparative analysis has been done in the results obtained by two methodologies – AHP & fuzzy SWARA-WASPAS – to determine the lean enabled SC agility, and also to which strategy should be adopted by the organizations as per the customized requirement of their SC.

The research limitation is that in future, there may be more number of lean, agile and leagile enablers which may be explored by different researchers in their findings, which may vary the output result accordingly. Though the research implications focus on having an advantage and impact on all aspects whether it is social, economic or commercial, there is a possibility of exploration of new and better decision-making tools and approaches in future. Also, the researchers are encouraged to test the proposed propositions further by taking case study of any automobile manufacturing organizations for the validation of the results.

The paper includes implications for the development of a powerful interrelationships between lean enablers, agile enablers and leagile enablers, which will help organization and the managers to take decisions regarding selection of best strategy appropriate to them to enhance their SC. This will also help new researchers of the field to take help of the research findings for exploring new and better optimization tools and techniques in future.

The findings of the research work will definitely help society, as the successful implementation of the lean, agile or leagile strategies in their SC system will leads to an increase in their efficiency and productivity, which will ultimately results in huge advantage to all the stakeholders directly or indirectly connected with the organization. The productivity dynamics cycle will also improve which will lead to more benefits to all in the market and achieving higher living index with better living standards.

This paper fulfills an identified need to study the various enablers of lean, agile and leagile SC, as well as their interrelationships. Also there is a need to understand the importance and effect of lean in enabling the SC agility.

Logistics service provider (LSP) selection involves multiple criteria, alternatives and decision makers. Group decision-making involves vagueness and uncertainty. This paper aims to propose a novel fuzzy method for assessing and selecting agile, resilient and sustainable LSP, taking care of the inconsistency and uncertainty in subjective group ratings.

Eighteen agile, resilient, operational, economic, environmental and social sustainability criteria were identified from the literature and discussion with experts. Interval-valued Fermatean fuzzy (IVFF) sets are more flexible and accurate for handling complex uncertainty, impreciseness and inconsistency in group ratings. The IVFF PIvot Pairwise RElative Criteria Importance Assessment Simplified (IVFF-PIPRECIAS) and IVFF weighted aggregated sum product assessment (IVFF-WASPAS) methods are applied to determine criteria weights and LSP evaluation, respectively.

Collaboration and partnership, range of services, capacity flexibility, geographic coverage, cost of service and environmental safeguard are found to have a greater influence on the LSP selection, as per this study. The LSP (L3) with the highest score (0.949) is the best agile, resilient and sustainable LSP in the manufacturing industry.

Hybrid IVFF-based PIPRECIAS and WASPAS methods are proposed for the selection of agile, resilient and sustainable LSP in the manufacturing industry.

The model can help supply chain managers in the manufacturing industry to easily adopt the hybrid model for agile, resilient and sustainable LSP selection.

The paper also contributes to the social sustainability of logistics workers.

To the best of the authors’ knowledge, IVFF-PIPRECIAS and IVFF-WASPAS methods are applied for the first time to select the best agile, resilient and sustainable LSP in a developing economy context.

Evaluating emergency preparedness exercises is crucial for assessing enhanced knowledge, facilitating learning and implementing knowledge in organizations. The cognitive process of motivation for action is a precursor for action, coping behavior and individual learning. This study aims to focus on how guided evaluation of emergency preparedness exercises can enhance cognitive motivation and influence the mental readiness of exercise participants.

This is a conceptual paper with a model approach design. The main conceptual contribution is suggesting a model for guided evaluation in emergency preparedness exercises. We present a theoretical background for understanding the increase in motivation based on social cognitive learning theory. In particular, this study discusses how different evaluation steps contribute to enhanced motivation and learning for exercise participants.

Increased motivation and enhanced personal performance standards could be achieved through using processes that lead to self-efficacy in guided exercise evaluation. Specifically, sources of enhanced motivation, such as repeated coping experiences, self-regulation processes, mastery motivation and performance motivation, would proliferate the readiness of individual crisis managers and teams.

This article suggests an evaluation model for use in emergency preparedness exercises. This approach combines bottom-up and top-down processes for debriefing, reflection and feedback, both individually and in teams. This approach aims to enhance exercise participants’ motivation and utilize exercise evaluation for organizational learning.

The conceptual discussion leads to developing implications for evaluation practice, suggesting how to structure evaluation and why. This study is novel for its explanation of how to use evaluation in the learning process.

This research delves into a comprehensive examination of Amazon’s Vendor Flex (VF) model, seeking to illuminate the intricacies of supply chain innovation through alliances between Amazon and its suppliers. Employing a multiple case study methodology, the study investigates the reduction of transaction costs, the establishment of strategic alliances for supply chain innovation and governance issues within these alliances.

A multiple case study methodology, incorporating personal interviews and triangulation with primary sources, was employed to unravel the dynamics of the VF model.

Results indicate that the VF model aligns with the reduction of transaction costs by leveraging Amazon’s specialized knowledge, although not necessarily through direct knowledge sharing. Amazon suppliers highlight competitive advantages gained through VF, showcasing efficient navigation of peak seasons and a focus on core activities with online retailing integration. The VF alliance represents a collaborative model where Amazon’s technological prowess enables a streamlined and innovative supply chain for online retailing, which resembles a vertical integration process.

This research underscores the potential of strategic alliances to drive innovation by incorporating industry-leading practices. The governance issues within the VF alliance reveal power imbalances, emphasizing the need for managers to govern dynamics, disclose information and build trust in large-scale alliances.

The design of effective policies that increase access to agricultural credit should consider understanding credit constraint farmers’ groups and their response to changes in the credit conditions. To contribute to this understanding, this study surveyed farmers from Chile and classified them into five credit constraint categories discussed in credit literature. In addition, these farmers indicated how they would react to a series of hypothetical conditions related to changing interest rates, loan maturity and grace periods. Their responses were employed to measure credit demand scores (i.e. relative elasticities). Regression tests evaluated how different types of farmers reacted to changing credit conditions.

Farmers from Chile were surveyed using a mix of random and convenience sampling. Surveyed farmers were classified into five credit constraint categories proposed by previous research. Farmers rated their demand for credit on a five-point Likert-type scale for hypothetical changes in interest rates, loan maturities and grace periods. Their responses were employed to measure credit demand scores or relative credit elasticities. The study evaluated credit elasticity as a function of farmers’ credit constraint and some control variables using several regressions, including OLS, ordered probit and hierarchical regression.

The study identified 44% unconstrained nonborrowing farmers, 23% unconstrained borrowers, 14% quantity-constrained, 16% risk-constrained and 3% transaction cost-constrained farmers. Unconstrained borrowers and quantity-constrained farmers responded most to changing interest rates and loan maturity conditions. In addition, unconstrained nonborrowers and risk-constrained farmers were statistically less sensitive to changes in credit conditions than unconstrained borrowers. This finding is significant because, as discussed, unconstrained nonborrowers represent 44% of our sample. Furthermore, risk-constrained farmers were the least sensitive to changes in interest rates and loan maturity across all other credit categories.

This study gives insights that can guide agribusiness policies to enhance access to credit in developing countries such as Chile. Agricultural credit capital institutions can better target their clientele by identifying farmers’ possible reactions before implementing policy changes to increase access to credit. This study’s credit constraint categorization and the results discussed can guide that identification. For instance, policies directed toward unconstrained borrowing farmers may find positive responses. However, implementing policies targeting the other three groups (unconstrained nonborrowing, risk-constrained and transaction cost-constrained farmers) is more challenging because these farmers are less responsive to changing credit conditions.

This article correlates farmers’ propensity to borrow and credit constraints across five categories of farmers. Prior research using this categorization framework has not identified farmers into the five groups. Furthermore, in addition to interest rate and loan maturity credit demand relative elasticity, this study adds the grace period elasticity, which has not been included in previous studies on agricultural credit.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.