Context Of Decision Research Articles

Environmental Impact Assessment United States of India Using GRA Method

An environmental impact assessment examines a project's potential effects on the environment while taking into consideration natural, social, and economic factors. More than half of the nations in the world require EIA in some capacity. EIAs had much less of an impact than their initial supporters anticipated. This study institutionalizes the difference, offers methodological justifications, and suggests approaches for future EIA use that will be more successful. Extensive knowledge of environmental impact assessments (EIA) as a tool for management of the ecosystem in the future Its goals and level of success have sparked a lot of discussion. It is evaluated based on the "Performance" of EIA. In particular, the debate shifts away from issues of actual implementation and toward the most crucial EIA objectives and their placement in broader decision-making contexts. EIA's implementation of planning choices is generally weak. Empirical research has supported the effect. The goal of this paper is to address Scale weights concern COPRAS interval-valued integers. The COPRAS method is extended to MCDM issues with unknowable knowledge. It essentially establishes some optimization models based on the traditional COPRAS method's fundamental concept, the determination of scale weights. This alternative in this method has been adopted by Delhi, Ladakh, Jammu and Kashmir, Andaman and Nicobar, Chandigarh, Lakshadweep, and the assessment criteria are Terrestrial, Aquatic, Economic, Social, and Cultural, as well as Air, Water, Soil, Noise, and Solid Waste pollution. As a result of this research Principles of the GRA technique uses the short-range and negative-best solutions to calculate the long-range answer, but the comparison of these distances is not thought to be important. Delhi has therefore been given the top ranking; Ladakh has received a poor ranking. This study demonstrates Delhi's elevated levels of air pollution.

English

Customer profiling tools have been introduced in recent years to protect investors against the increasing complexity of financial instruments. Advisor experts have to combine measures of finance and behavioural finance studies in their evaluation. The need to integrate different competences and compare complex criteria naturally leads to the modelling of the portfolio selection problem as a group decision problem. The Analytic Network Process (ANP) is a useful technique that helps decision-makers evaluate the available alternatives and aggregate their professional knowledge. In this context, the Reaching Consensus Process (RCP) is crucial for the validity of choices due to the strong sensitivity of the ANP to the decision maker's judgement. However, the RCP may be affected by the behavioural characteristics of experts, the vulnerability of judgements and the uncertainty that characterises the decision-making context, leading to unbiased judgements that are not based on different expertise. Similarly, without the involvement of more experts, the process could be inefficient due to the sensitivity of the ANP decision-maker’s judgement. In order to maximise the aggregation of experts' knowledge, to minimise the influence of the individual in quantifying the relationships between criteria and alternatives, and to preserve the impartiality of evaluations, the present paper discusses RCP through Choquet’s discrete integral. This aggregation captures the non-linearity of preferences and includes the fuzziness of judgments. Thus, there is no need to reach a consensus when evaluating the criteria: each expert manages a single ANP whose synthesis are aggregated using Choquet’s discrete integral. In this sense, the aggregation defines an unbiased consensus which mitigates the sensitivity of the ANP to the preferences of the decision-maker. The methodology is applied to a case study in which several experts evaluate investment alternatives of an individual, simultaneously incorporating behavioural criteria and Markets in Financial Instruments Directive (MiFID) client profiling criteria.

Effort Expenditure Increases Risk-Taking for Improbable Rewards.

Previous studies have found that exerting effort can lead people to engage in risk-taking behaviors. While effort can be either cognitive or physical, risk-taking can take place in either a risky context with known outcome probabilities or an ambiguous context with unknown outcome probabilities. The goal of the current research is to investigate how effort type and decision context influence risk-taking after effort exertion. Across three experiments, we find evidence that investing effort increases risk-taking at a short timescale. Importantly, this effect is particularly noticeable when the chance of winning is low, rather than when it is uncertain. Furthermore, the increase in risk-taking happens regardless of whether the effort is cognitive or physical. These findings suggest the existence of a cost-invariant but decision context-variant mechanism for the risk-taking after-effect of effort expenditure, which helps to bring the negative emotions caused by effort exertion back to a state of emotional homeostasis.

Dark personality traits and deception, and the short dark tetrad (SD4) as integrity screening instrument

Dark personality traits (Machiavellianism, Narcissism, Psychopathy, and Sadism) have been associated with aversive, unethical, and criminal conduct. Concise measurement tools such as the Short Dark Tetrad (SD4) are popular, because they lend themselves as screening instruments. As such, the scores on these scales are used in various decision-making contexts, and they can have considerable effects on the lives of people who display an unfortunate scoring pattern. The present study explored to what extent high SD4 scores are actually predictive of deceptive behaviour in a matrix puzzle task, in a general community sample (N = 751). Results indicated that 9.9% of participants lied, that is, exaggerated their performance on the matrix task, hoping to increase their likelihood of financial reward. These cheating participants scored higher on all four dark traits. Nonetheless, the overlap between SD4 distributions made it impossible to determine cut-off scores in an attempt to consider scores as actual predictors of deception proneness. When framed in likelihoods, some scores can be diagnostic of deception proneness. Particularly in the context of statement validity assessment, characterized by tools with modest to poor accuracy, SD4 scores may add to diagnostic accuracy.

Evaluation of Tourist Destinations Carrying Capacity in a Decision-Making Context With Muirhead Means Aggregation Operator in q-Rung Orthopair Fuzzy Hypersoft Environment

Evaluation of Tourist Destinations Carrying Capacity in a Decision-Making Context With Muirhead Means Aggregation Operator in q-Rung Orthopair Fuzzy Hypersoft Environment

Map-Based Ensemble Forecasts for Maritime Operations: An Interactive Usability Assessment with Decision Scenarios

Abstract Previous research indicates that forecast uncertainty can, in certain formats and decision contexts, provide actionable insights that help users in their decision-making. However, how to best disseminate forecast uncertainty, which factors affect successful uptake, and how forecast uncertainty transforms into better decision-making remains an ongoing topic for discussion in both academic and operational contexts. Interpreting and using visualizations of forecast uncertainty are not straightforward, and choosing how to represent uncertainty in forecast products should be dependent on the specific audience in mind. We present findings from an interactive stakeholder workshop that aimed to advance context-based insights on the usability of graphical representations of forecast uncertainty in the field of maritime operations. The workshop involved participants from various maritime sectors, including cruise tourism, fisheries, government, private forecast service providers, and research/academia. Geographically situated in Norway, the workshop employed sea spray icing as a use case for various decision scenario exercises, using both fixed probability and fixed threshold formats, supplemented with temporal ensemble diagrams. Accumulated operational expertise and characteristics of the forecast information itself, such as color coding and different forms of forecast uncertainty visualizations, were found to affect perceptions of decision-making quality. Findings can inform codesign processes of translating ensemble forecasts into usable and useful public and commercial forecast information services. The collaborative nature of the workshop facilitated knowledge sharing and coproduction between forecast providers and users. Overall, the study highlights the importance of incorporating methodological approaches that consider the complex and dynamic operational contexts of ensemble-based forecast provision, communication, and use. Significance Statement We wanted to understand how maps showing uncertainty in weather forecasts can help maritime users in their operational decisions. We organized a workshop with Norwegian maritime stakeholders and forecasters, who interpreted maps that combined layers of maritime operational activities and the likelihood of sea spray icing (an important hazard for ships operating on higher latitudes). The results show that contextual knowledge, and the use visual formats such as traffic light colors may help users to understand the maps. The results will help to better communicate weather forecasts to maritime users and gives suggestions about how to involve users in codesigning forecast products. Follow-up research could use our approach to investigate other hazardous conditions, such as wind, waves and sea ice.

Adverse Events After Isolated Posterior Cruciate Ligament Reconstruction: A National Database Study.

Optimal management after posterior cruciate ligament (PCL) injury remains an active area of research, as reconstruction is technically challenging and poses unique risks in the posterior knee. Studies have reported variable rates of complications. To describe the rates of readmission, emergency department (ED) visits, and postoperative complications within 90 days of isolated PCL reconstruction (PCLR) in a large, national cohort to better understand the perioperative variables that influence a practitioner's decision of whether to pursue operative versus nonoperative management. Descriptive epidemiology study. PCLRs from January 1, 2010, through August 31, 2020, were identified in PearlDiver, a national administrative database. Patients with concomitant ligament surgery and those with fewer than 90 days of postoperative database activity were excluded. Deep vein thromboses, pulmonary embolisms, surgical site infections, compartment syndrome, and vascular events within 90 days of surgery were identified, as were 90-day readmissions and ED visits. Logistic regression models were built in PearlDiver to calculate odds ratios (ORs) for ED utilization. The final cohort consisted of 1154 patients with isolated PCLR (mean age, 34 ± 16 years; 62% male). Most patients were located in the Southern United States (n = 417; 36.1%), and most had commercial insurance (n = 992; 86%). The 90-day rates of adverse events were as follows: deep vein thrombosis (13; 1.1%), pulmonary embolism (19; 1.6%), surgical site infection (<11; <1%), compartment syndrome (<11; <1%), vascular event (<11; <1%), readmission (13, 1.1%), and ED utilization (99; 8.6%). The majority of emergency department visits (52%) occurred in the first 2 weeks postoperatively. Predictive factors for ED utilization included Elixhauser Comorbidity Index score (OR = 1.31 per 2-point increase) and Medicaid insurance (OR = 2.03 relative to commercial insurance). The current study reported rates of adverse events after isolated PCLR in a large, national cohort. The results provide important context for decisions about optimal management of PCL injury.

A multi-attribute decision-making context for hospital site evaluation using distance measures under complex picture fuzzy soft set environment

A multi-attribute decision-making context for hospital site evaluation using distance measures under complex picture fuzzy soft set environment

Towards Shared and Effective Self-Care: A User-Centric Framework for Enhancing HIV Prevention

Background: Despite a declining trend in Human immunodeficiency virus (HIV) prevalence across sub-Saharan Africa, specific vulnerable groups continue to be disproportionately impacted (29.9% for sex workers, 12.9% for gay men having sex with men (MSM) vs 5.9% for adults). In the face of this challenge, it is critical to tailor demand creation for prevention, in particular self-care, for maximum impact and cost-efficiency. This study seeks to provide a behavioral understanding towards HIV prevention that shape the efficacy of prevention strategies among priority populations (female sex workers, MSM and adolescent girls and young women) in southern and eastern Africa. Methods: This study involved a literature review of 110 articles, focusing on HIV prevention in priority populations, behavioral interventions, and policy priorities from governments and funders. Additionally, insights were gained from 11 in-depth interviews and a workshop with 10 experts at the AIDS Impact 2023 conference. Participants included behavioral researchers and program leaders from Global Fund, United States Agency for International Development (USAID), U.S. President's Emergency Plan for AIDS Relief (PEPFAR), Bill and Melinda Gates Foundation, FHI360, Population Services International and Genesis Analytics. A user-centric behavioral framework was devised to identify actionable drivers and barriers in HIV prevention during the consultation exercise. Results: The study highlights the importance of shared and effective self-care in enhancing HIV prevention, particularly in vulnerable groups. The behavioral framework integrates the decision-making contexts and the health ecosystem layers to inform research and demand strategy. Key findings include: (1) effective self-care is not the same as continuous use of Pre-exposure prophylaxis and therefore, programs and policies need to measure them differently; (2) users exercise choice through a combination of prevention products and focus on a few may be sub-optimal, and (3) perceived risk is transient and hence risk-based messaging lacks sustained relevance. Conclusion: Effective self-care strategies extend beyond overcoming access and availability issues. Examining the underlying factors causing the barriers is essential. The behavioral framework presented here suggests that: (a) Self-care is a shared responsibility between the user and healthcare system and (b) demand creation should be de-medicalised, away from products and towards meeting the needs and preferences of users.

ERTool: A Python Package for Efficient Implementation of the Evidential Reasoning Approach for Multi-Source Evidence Fusion.

Background: Multi-source evidence fusion aims to process and combine evidence from different sources to support rational and reliable decision-making. The evidential reasoning (ER) approach is a helpful method to deal with information from multiple sources with uncertainty. It has been widely used in business analytics, healthcare management, and other fields for optimal decision-making. However, computerized implementation of the ER approach usually requires much expertise and effort. At present, some ER-based computerized tools, such as the intelligent decision system (IDS), have been developed by professionals to provide decision support. Nevertheless, IDS is not open source, and the user interfaces are a bit complicated for non-professional users. The lack of a free-to-access and easy-to-use computerized tool limits the application of ER. Methods: We designed and developed a Python package that could efficiently implement the ER approach for multi-source evidence fusion. Further, based on it, we built an online web-based system, providing not only real-time evidence fusion but also visualized illustrations of combined results. Finally, a comparison study between the Python package and IDS was conducted. Results: A Python package, ERTool, was developed to implement the ER approach automatically and efficiently. The online version of the ERTool provides a more convenient way to handle evidence fusion tasks. Conclusions: ERTool, compatible with Python 3 and can be installed through the Python Package Index at https://pypi.org/project/ERTool/, was developed to implement the ER approach. The ERTool has advantages in easy accessibility, clean interfaces, and high computing efficiency, making it a key tool for researchers and practitioners in multiple evidence-based decision-making. It helps bridge the gap between the algorithmic ER and its practical application and facilitates its widespread adoption in general decision-making contexts.

Beyond analytic bounds: Re-evaluating predictive power in risky decision models

Abstract Research in behavioral decision-making has produced many models of decision under risk. To improve our understanding of choice under risk, it is essential to perform rigorous model comparisons over large sets of decision settings to find which models are most useful. Recently, such large-scale comparisons have produced conflicting conclusions: A variant of cumulative prospect theory (CPT) was the best model in a study by He, Analytis, and Bhatia (2022), whereas variants of the model BEAST were the best in two choice prediction competitions. This study delves into these contradictions to identify and explore the underlying reasons. We replicate and extend the analysis by He et al., this time incorporating BEAST, which was previously excluded because it cannot be analytically estimated. Our results show that while CPT excels in systematically hand-crafted tasks, BEAST—originally designed for broader decision-making contexts—matches or even surpasses CPT’s performance when choice tasks are randomly selected, and predictions are made for new, unknown decision makers. This success of BEAST, very different from classical decision models—as it does not assume, for example, subjective transformations of outcomes and probabilities—puts into question previous conclusions concerning the underlying psychological mechanisms of choice under risk. Our results challenge the field to expand beyond established evaluating techniques and highlight the importance of an inclusive approach toward nonanalytic models, like BEAST, to achieve more objective insights into decision-making behavior.

Enhanced Academic Stress-Coping Skills Assessment in College Students: A Comparative Study of Neutrosophic Distance Measure and Proposed Cubic Pythagorean Fuzzy Hypersoft TOPSIS Method

In multi-criteria decision-making scenarios involving real numbers, interval numbers, and a combination of membership and non-membership grades, accurate decision-making is crucial yet challenging. The integration of diverse grade values into a single value poses a significant challenge for decision-makers. To address this issue, this study introduces the concept of a cubic Pythagorean fuzzy hypersoft set, facilitating information aggregation without ambiguity. The characteristics of correlation coefficients and aggregation operators are emphasized, underscoring their importance in decision-making processes. An algorithm based on correlation coefficients (CC) is proposed for the TOPSIS method, which ranks preferences based on their similarity to the ideal solution, applied here to examine how college students cope with academic stress. Furthermore, the efficiency of the proposed method is demonstrated through a comparative study, wherein the correlation coefficient in the TOPSIS method is contrasted with existing distance measures (DMs). Results indicate the superiority of CC in the TOPSIS method over DMs. In addition to comparing the proposed method with existing distance measures, the efficacy of the proposed approach is further demonstrated through a comparative analysis with established neutrosophic distance measures. This comprehensive evaluation highlights the robustness and versatility of the proposed method in addressing the complexities of multi-criteria decision-making scenarios, particularly in assessing stress management strategies among college students, thus providing valuable contributions to decision-making contexts. This study contributes to enhancing decision-making processes, particularly in evaluating stress management strategies among college students, thereby offering valuable insights for academic contexts.

Goal setting for family firm owners: An agency-stewardship perspective

Goal setting for family firms is a vital activity since the performance of businesses is measured against their goals. Using prior research on goal-setting in the context of family firms, this narrative literature review paper aims to bring forth, the importance of goal-setting specific to family business owners who straddle family and business. A review of family-business literature indicates that using the three-circle model comprising family, business, and ownership as the base theory, goal setting in family firms has been examined from the perspective of whether they are economic or non-economic and whether they are family-oriented or business-oriented. Both these approaches to family business goal setting are based on the ideology that the unique feature of family businesses is that they aim to satisfy two groups of stakeholders — family members and business members. While family and business perspectives are captured in these two approaches, goal setting from an owner’s perspective has been largely ignored. Rooted in the concept of corporate responsibility, the agency-stewardship framework is emerging as an alternative perspective for family businesses and hence is proposed as a useful dominant logic framework for decisions on goal setting. This conceptual paper based on a narrative literature review aims to present the agency-stewardship framework as a relevant and important lens in the two most important decision contexts of family businesses, i.e., governance and succession. The paper exemplifies how the agency-stewardship continuum can be used for goal setting to nurture next-generation family business leaders and manage family business operations through goal setting in governance policies and succession processes.

Monitoring-supported value generation for managing structures and infrastructure systems

Abstract To maximize its value, the design, development and implementation of structural health monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-making. We propose a classification of SHM use cases aligning with various dimensions that are closely linked to the respective decision contexts. The types of decisions that have to be supported by the SHM system within these settings are discussed along with the corresponding challenges. We provide an overview of different classes of models that are required for integrating SHM in the decision-making process to support the operation and maintenance of structures and infrastructure systems. Fundamental decision-theoretic principles and state-of-the-art methods for optimizing maintenance and operational decision-making under uncertainty are briefly discussed. Finally, we offer a viewpoint on the appropriate course of action for quantifying, validating, and maximizing the added value generated by SHM. This work aspires to synthesize the different perspectives of the SHM, Prognostic Health Management, and reliability communities, and provide directions to researchers and practitioners working towards more pervasive monitoring-based decision-support.

Design of a multiple criteria decision analysis framework for prioritizing high-impact health technologies in a regional health service.

This study aims to develop a framework for establishing priorities in the regional health service of Murcia, Spain, to facilitate the creation of a comprehensive multiple criteria decision analysis (MCDA) framework. This framework will aid in decision-making processes related to the assessment, reimbursement, and utilization of high-impact health technologies. Based on the results of a review of existing frameworks for MCDA of health technologies, a set of criteria was proposed to be used in the context of evaluating high-impact health technologies. Key stakeholders within regional healthcare services, including clinical leaders and management personnel, participated in a focus group (n=11) to discuss the proposed criteria and select the final fifteen. To elicit the weights of the criteria, two surveys were administered, one to a small sample of healthcare professionals (n=35) and another to a larger representative sample of the general population (n=494). The responses obtained from health professionals in the weighting procedure exhibited greater consistency compared to those provided by the general public. The criteria more highly weighted were "Need for intervention" and "Intervention outcomes." The weights finally assigned to each item in the multicriteria framework were derived as the equal-weighted sum of the mean weights from the two samples. A multi-attribute function capable of generating a composite measure (multicriteria) to assess the value of high-impact health interventions has been developed. Furthermore, it is recommended to pilot this procedure in a specific decision context to evaluate the efficacy, feasibility, usefulness, and reliability of the proposed tool.

Economic Efficacy: Exploring Decision-Making In Islamic School Budgeting For Enhanced Resource Allocation

Making the right decisions in determining Islamic School budgets is a key element to secure the efficiency and sustainability of the education system. The purpose of this study is Economic Efficacy: Exploring Decision-Making in Islamic School Budgeting for Enhanced Resource Allocation. This research method is based on a qualitative approach that allows researchers to understand in depth the context of decision making in Islamic School budgeting. The results of this study show that decision making in Islamic School budgeting plays a key role in the economic effectiveness of education resource allocation. Several key findings highlight the complexity and variety of factors influencing the decision-making process, including education priorities, government policies, and pressure from stakeholders. The results of this study show that Data analysis and the use of accurate information have proven essential in ensuring effective decision making. Islamic Schools that use solid data to evaluate student performance and the effectiveness of educational programs tend to make better decisions in budget allocation

Commissioning [Integrated] Care in England: An Analysis of the Current Decision Context

Background: The emergence of Integrated Care Systems (ICSs) across England poses an additional challenge and responsibility for local commissioners to accelerate the implementation of integrated care programmes and improve the overall efficiency across the system. To do this, ICS healthcare commissioners could learn from the experience of the former local commissioning structures and identify areas of improvement in the commissioning process. &#x0D; Aim &amp; Audience: This study describes the investment decision process in integrated care amid the transition toward ICSs, highlights challenges, and provides recommendations to inform ICSs in their healthcare commissioning role. It is expected that this study encourages an open discussion within the emerging ICSs on the barriers and enablers of the local commissioning of integrated care. We also believe that findings of this study are likely to be of international interest as countries are gradually moving towards integrated care, and local purchasers of healthcare from high-income economies are likely to encounter similar challenges as the ones faced by local commissioners in England.&#x0D; Methods: We conducted twenty-six semi-structured interviews with local commissioners and other relevant stakeholders in South East England in 2021. Interviews were supplemented with existing evidence from the literature.&#x0D; Results: England’s local healthcare commissioning has made the transition towards a new organisational architecture, with some integrated care programmes running, and a dual top-down and bottom-up prioritisation process in place. The commissioning and consequent development of integrated care programmes have been hindered by various barriers, including difficulties in accessing and using information, operational challenges, and resource constraints. Investment decisions have mainly been driven by national directives and budget considerations, with a mixture of subjective and objective approaches. This contrasts with what seems to be a set of desirable prioritisation criteria, with the triple aim framework at the front. A systematic and data-driven framework could replace this ad-hoc prioritisation of integrated care and contribute to a more rational and transparent commissioning process. To develop such a framework, a good reference point could be the health technology assessment framework established at national level to guide priority-setting decisions. &#x0D; Conclusion &amp; next-steps: The local healthcare commissioning structures in England are complex, the evidence available is rich, and integrated care is undoubtedly the goal to achieve. However, the multiple barriers that commissioners face hinder the development of integrated care programmes and limit the local use of evidence in healthcare decision-making. The emerging ICSs seem to open an opportunity for local commissioners to strengthen the commissioning process of integrated care with evidence-based priority-setting approaches similar to the well-established health technology assessment framework at the national level. To support this, we are developing a data-driven framework to monitor and assess ‘integrated care programmes’. &#x0D;

Enabling novel paradigms: a biological questions-based approach to human chemical hazard and drug safety assessment.

Throughput needs, costs of time and resources, and concerns about the use of animals in hazard and safety assessment studies are fueling a growing interest in adopting new approach methodologies for use in product development and risk assessment. However, current efforts to define "next-generation risk assessment" vary considerably across commercial and regulatory sectors, and an a priori definition of the biological scope of data needed to assess hazards is generally lacking. We propose that the absence of clearly defined questions that can be answered during hazard assessment is the primary barrier to the generation of a paradigm flexible enough to be used across varying product development and approval decision contexts. Herein, we propose a biological questions-based approach (BQBA) for hazard and safety assessment to facilitate fit-for-purpose method selection and more efficient evidence-based decision-making. The key pillars of this novel approach are bioavailability, bioactivity, adversity, and susceptibility. This BQBA is compared with current hazard approaches and is applied in scenarios of varying pathobiological understanding and/or regulatory testing requirements. To further define the paradigm and key questions that allow better prediction and characterization of human health hazard, a multidisciplinary collaboration among stakeholder groups should be initiated.

Navigating the uncertainty: A novel taxonomy of vaccine hesitancy in the context of COVID-19.

Vaccine hesitancy remains a significant and evolving public health challenge. The COVID-19 pandemic has created a unique decision context with significant uncertainty caused by the novelty of the disease being targeted, unfamiliarity with the vaccines being offered, misinformation, and strong handed government measures. In an effort to extend our understanding of vaccine hesitancy to the high uncertainty decision environment presented by COVID-19, we present a novel taxonomy of the determinants of vaccine hesitancy, based on an inductive analysis of qualitative data gathered during the COVID-19 pandemic. We report on focus group data from a purposive sample of 18 Canadians with varying sociodemographic characteristics and COVID-19 vaccination attitudes. An inductive thematic analysis of this data reveals eight core themes related to vaccine hesitancy: values, trust, social environment, personal anecdotes, environmental fluctuation, prior knowledge, perceived risk & systems of care. We explore these core themes as well as 25 sub-themes, contrasting them with previous models of vaccine hesitancy and suggesting potential strategies for public health professionals.

Who decides which trucks to buy? Implications for electrifying freight fleets

This study characterizes fleets’ decisions to operate medium- and heavy-duty fossil fuel-powered trucks and compares those decisions with fleets’ consideration of electric trucks. Based on interviews with decision-makers in 25 such organizations, we describe the social context of truck acquisition decision-making. We use concepts of organizational structure and external network heterogeneity to create a typology of decision-making. We use the typology to compare decisions regarding fleets’ actual and prospective consideration and use of conventional vs. electric trucks. The results describe who is involved in truck decision-making and provides insights into the organizational structures and external networks of fleets that have and those who have not acquired electric trucks. We identify fleet types that may require additional support to transition to electric trucks. This support may include connecting fleets with new or additional external actors to facilitate medium- and heavy-duty truck electrification.