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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.

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 rising complexity of business changes has increasingly highlighted the requirements to provide a comprehensive and empirical framework for the supply chain agility (SCA). A review of extant studies shows that the results are complicated and ambiguous. Moreover, this study is a meta-analytical review of previous empirical studies to identify SCA antecedents and effects of SCA on firm performance.

According to the protocol, 64 studies were chosen as the sample to survey the relationships between five clusters of SC allopoietic properties (SCAPs) (SC connectivity, symbiotic relationship (SR), cognitive openness (CO), homeostasis and collaboration) and SCA, as well as its effects on firm performance.

Among antecedents, horizontal collaboration’s effect on SCA is the strongest, and the relationship between SR-SCA and CO-SCA is less than moderate. SCA affects firm performance and its dimensions, with a stronger effect on financial performance (FP). Furthermore, the SCA study in the framework of allopoietic systems is a good starting point for future research.

Managers are advised to constantly review repetitive interactions between the company and its environment and to learn about interactions between SC and the environment. Learning from these interactions and disseminating their explicit knowledge among company members lead to a quick response to the environmental instability.

As the first meta-analysis on SCA antecedents and its effects on firm performance, this study contributes to the SCA literature and provides research directions for the future.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Globally “non-urgent” health care services were ceased in response to the 2020 outbreak of COVID-19, until 2021, when restrictions were lifted. In the UK, this included speech and language therapy services. The implications of COVID-19 restrictions have not been explored. This study aimed to examine the impact of the UK’s COVID-19 response on speech and language therapy services.

An online survey of the practice of speech and language therapists (SLTs) in the UK was undertaken. This explored SLTs’ perceptions of the demand for their services at a time when COVID-19 restrictions had been lifted, compared with before the onset of the pandemic. The analysis was completed using descriptive statistics and content analysis.

Respondents were mostly employed by the UK’s National Health Service (NHS) or the private sector. Many participants reported that demands on their service had increased compared with before the onset of the pandemic. The need to address the backlog of cases arising from shutdowns was the main reason for this. Contributing factors included staffing issues and redeployment. Service users were consequently waiting longer for NHS therapy. Private therapy providers reported increased demand, which they directly attributed to these NHS challenges.

This presents the only focused account of the impact of the national response to COVID-19 on speech and language therapy services in the UK. It has been identified that services continue to face significant challenges, which indicate a two-tier system is emerging. Healthcare system leaders must work with service managers and clinicians to create solutions and prevent the system from being overwhelmed.

This study aims to answer the question: What dynamic capabilities do diverse humanitarian organizations have?

We examine this question through the lens of dynamic capabilities with sensing, seizing and reconfiguring capacities. The research team interviewed 15 individuals from 12 humanitarian organizations that had (a) different geographic scopes (global versus local) and (b) different missions (emergency response versus long-term development aid). We also gathered data from secondary sources, including standard operating procedures, company websites, and news databases (Factiva, Reuters and Bloomberg).

The findings identify the operational and dynamic capabilities of global and local humanitarian organizations while distinguishing between their mission to provide long-term development aid or emergency relief. (1) The global organizations, with their beneficiary responsiveness, reconfigured their sensing and seizing capacities throughout the COVID-19 pandemic by pivoting quickly to local procurement or regional supply chains. The long-term development organizations pivoted to multi-year supplier agreements with fixed pricing to counter price uncertainty and accessed social capital with government bodies. In contrast, emergency response organizations developed end-to-end supply chain visibility to sense changes in supply and demand. (2) Local humanitarian organizations developed the capacity to sense demand and supply changes to reconfigure based on their experiential learning working with the local community. The long-term-development local organizations used un-owned and scalable relief infrastructure to seize opportunities to rebuild affected areas. In contrast, emergency response organizations developed their capacity to seize opportunities to provide aid stemming from their decentralized decision-making, a lack of structured procedures, and the authority for increased expenditure.

We propose a theoretical framework to identify humanitarian organizations' operational and dynamic capabilities, distinguishing between global and local organizations and their emergency response and long-term aid missions.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.

Following the traditions of stakeholder salience theory, this paper aims to contend that some institutional investor activists and tactics have more power, legitimacy and urgency than others.

The author undertakes an empirical test of a saliency table looking at the effects of institutional investor heterogeneity on portfolio firm responses using ordinal logistic regression.

This study found heterogeneity for institutional investor type to drive firm responses but not tactic type raising the importance of the attributes of each type of investor activist. The author found a rank ordering of public pension plans, hedge funds and then private multiemployer funds in saliency to portfolio firms. In addition, the use of proxy-based tactics did not help or hurt each investor type. Both findings challenge prior empirical work.

The rank ordering based upon the heterogeneity of institutional investor activists and their tactical interactions are tested providing empirical evidence of the most influential activist investors and tactics in one study, which is rare in the literature.