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Responsibility beyond the point of sale: Leveraging product stewardship to mitigate Scope 3 emissions 超越销售点的责任：利用产品管理来减少范围3的排放

IF 9.8 1区管理学 Q1 BUSINESS

Journal of Business Research

Pub Date : 2026-02-04 DOI: 10.1016/j.jbusres.2026.116040

Lukas Schnabl , Anna-Karina Schmitz , Emily Waltermann , Katharina Göring-Lensing-Hebben

Growing sustainability concerns emphasize companies’ responsibility for their environmental footprint across the entire value chain. Yet most emissions occur beyond companies’ direct control during product use and disposal. Nonetheless, research has largely neglected how companies can manage these downstream Scope 3 emissions. Drawing on stewardship theory and the natural-resource-based view, we conceptualize product stewardship as a process by which companies steer Scope 3 emissions reduction. We build on interviews with 76 managers across consumer goods, retail, and related industries to develop a framework that identifies (1) organizational commitment, competitiveness, and stakeholder involvement as antecedents, (2) a three-stage process encompassing measurement work, initiative implementation (on the levels of product, price, place, and promotion), and evaluation, and (3) consumer-, product-, and company-related boundaries. The framework redefines stewardship as a distributed governance mechanism connecting corporate strategy with consumer behavior and offers guidance for managers and policymakers on reducing consumption phase Scope 3 emissions.

日益增长的可持续性问题强调了企业在整个价值链中对环境足迹的责任。然而，在产品使用和处理过程中，大多数排放都超出了企业的直接控制范围。然而，研究在很大程度上忽视了企业如何管理这些下游的第三类排放。根据管理理论和基于自然资源的观点，我们将产品管理概念化为公司引导范围3减排的过程。我们基于对消费品、零售和相关行业的76位经理的访谈，开发了一个框架，该框架确定了(1)组织承诺、竞争力和利益相关者参与作为先决条件；(2)包括测量工作、倡议实施（在产品、价格、地点和促销层面上）和评估在内的三阶段过程；(3)消费者、产品和公司相关边界。该框架将管理重新定义为一种连接企业战略与消费者行为的分布式治理机制，并为管理人员和政策制定者提供减少消费阶段范围3排放的指导。

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引用次数: 0

Understanding Why and When Coworkers Undermine Employees Who Have Friends in High Places 了解同事为什么以及何时会诋毁那些有高层朋友的员工

IF 10.5 1区管理学 Q1 BUSINESS

Academy of Management Journal

Pub Date : 2026-02-04 DOI: 10.5465/amj.2022.1306

Ruolian Fang, Daniel J. McAllister, Zhen Zhang, Michelle K. Duffy, Lin Wang

Academy of Management Journal, Volume 0, Issue ja, -Not available-.

管理学院学报，第0卷，第ja期，-不可用-。

引用次数: 0

Citizen coproduction motivations: a systematic review and the SPIENM framework 公民合作生产动机：系统回顾和SPIENM框架

IF 4.9 2区管理学 Q1 MANAGEMENT

Public Management Review

Pub Date : 2026-02-04 DOI: 10.1080/14719037.2026.2626348

Yang Jia, Huafang Li, Siyu Lyu, Jinfeng Zhang

引用次数: 0

The subtleties of abolishing "race correction" in clinical artificial intelligence. 废除临床人工智能中“种族矫正”的微妙之处。

IF 4.6 2区医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of the American Medical Informatics Association

Pub Date : 2026-02-04 DOI: 10.1093/jamia/ocag012

Moustafa Abdalla, LLana James, David S Jones, Mohamed Abdalla

Objectives: To explore the complexities of eliminating race correction in clinical artificial intelligence (AI), the pitfalls of naive solutions, and to propose systematic strategies for equitable model development.

Background and significance: Race correction in clinical AI, as in traditional medicine, introduces biases with potentially harmful consequences. Simple removal of race from models is insufficient due to the lasting influence of historically biased data.

Approach: We analyze 4 standardized scenarios to demonstrate how race correction manifests in clinical AI: use of race-corrected variables, explicit inclusion of race, inference via proxy variables, and use of race-specific models.

Results: For each scenario, the intuitive solution to removing race correction fails to eliminate bias, often due to legacy effects embedded in the data. More thoughtful approaches are required.

Discussion: Ending race correction in clinical AI requires deliberate, context-sensitive interventions, inclusion of diverse stakeholders, and strategies to make model reasoning more transparent and auditable.

目的：探讨消除临床人工智能（AI）中种族纠正的复杂性，幼稚解决方案的陷阱，并提出公平模型开发的系统策略。背景和意义：与传统医学一样，临床人工智能中的种族纠正会引入偏见，并带来潜在的有害后果。由于历史上有偏见的数据的持久影响，简单地从模型中删除种族是不够的。方法：我们分析了4种标准化场景，以展示种族纠正如何在临床人工智能中表现出来：使用种族纠正变量，明确包含种族，通过代理变量进行推理，以及使用种族特定模型。结果：对于每种情况，消除种族校正的直观解决方案都无法消除偏见，这通常是由于数据中嵌入的遗留效应。需要更深思熟虑的方法。讨论：在临床人工智能中结束种族纠正需要深思熟虑的、上下文敏感的干预措施，包括不同的利益相关者，以及使模型推理更加透明和可审计的策略。

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引用次数: 0

R&D Disclosure and Carbon Performance: A Machine Learning Analysis of Carbon‐Intensive Firms 研发披露与碳绩效：碳密集型企业的机器学习分析

IF 13.4 1区管理学 Q1 BUSINESS

Business Strategy and The Environment

Pub Date : 2026-02-04 DOI: 10.1002/bse.70594

Albert Acheampong, Albert Danso, Emmanuel Adu‐Ameyaw, Rilwan Sakariyahu

The growing urgency of climate change, alongside global sustainable development initiatives, has brought environmental priorities to the forefront of corporate strategy. This study explores how narrative disclosures related to research and development (R&D) predict carbon performance in European industries with high R&D intensity. Guided by the natural resource‐based view (NRBV), the research examines how qualitative R&D narratives act as strategic tools for communicating innovation‐driven environmental strategies. We introduce a novel methodological approach for analyzing unstructured textual data using advanced machine learning (ML) models, including neural networks (NNs), support vector machines (SVMs), and random forests (RFs). Our results show that firms with extensive and positively framed R&D disclosures are more effective in managing carbon emissions and in progressing toward major sustainability targets such as the Paris Agreement and the EU Green Deal. The findings also reveal that regulation and innovation shape distinct patterns in narrative disclosures across sectors, particularly in technology and pharmaceuticals. Moreover, the tone and thematic focus of these narratives offer strategic insights that go beyond traditional financial indicators, effectively linking innovation with sustainability objectives. This research advances the corporate disclosure literature by deepening our understanding of how sustainability and innovation intersect, while also offering practical guidance for firms and policymakers.

气候变化日益紧迫，加上全球可持续发展倡议，使环境优先事项成为企业战略的重中之重。本研究探讨了研发相关的叙述性披露如何预测研发强度高的欧洲行业的碳绩效。在自然资源基础观点（NRBV）的指导下，本研究探讨了定性研发叙事如何作为沟通创新驱动的环境战略的战略工具。我们介绍了一种新的方法方法来分析非结构化文本数据，使用先进的机器学习（ML）模型，包括神经网络（nn），支持向量机（svm）和随机森林（rf）。我们的研究结果表明，具有广泛和积极框架的研发披露的公司在管理碳排放和向巴黎协定和欧盟绿色协议等主要可持续性目标迈进方面更有效。研究结果还显示，监管和创新在各个行业（尤其是科技和制药行业）的叙事披露中形成了不同的模式。此外，这些叙事的基调和主题重点提供了超越传统财务指标的战略见解，有效地将创新与可持续发展目标联系起来。本研究通过深化我们对可持续性和创新如何交叉的理解，推进了公司信息披露文献的发展，同时也为企业和决策者提供了实践指导。

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引用次数: 0

Development of BERT-based large language models for emergency department triage using real-world conversations. 开发基于bert的大型语言模型，使用真实世界的对话进行急诊科分类。

IF 4.6 2区医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of the American Medical Informatics Association

Pub Date : 2026-02-04 DOI: 10.1093/jamia/ocag007

Sukyo Lee, Sumin Jung, Jong-Hak Park, Hanjin Cho, Sungwoo Moon, Sejoong Ahn

Objectives: Accurate triage in emergency departments (ED) is critical for appropriate resource allocation. While artificial intelligence (AI) has been explored for triage, prior models relied on summarized clinical scenarios. We aimed to develop and evaluate large language models (LLMs) trained on real-world clinical conversations to classify patient urgency.

Materials and methods: We used a nationally curated dataset of anonymized triage-level conversations from 3 tertiary Korean hospitals. Two BERT-based models were developed to classify urgency per the Korean Triage and Acuity Scale (KTAS) into urgent (KTAS 3) or non-urgent (KTAS 4-5). One model tokenized the entire conversation, while the other applied a hierarchical structure with sentence-level tokenization and speaker-role embeddings. Performance metrics included accuracy, precision, recall, and F1-score. We compared our models against ChatGPT GPT-4o and ClinicalBERT, and assessed explainability using SHapley Additive exPlanations (SHAP).

Results: A total of 5244 clinical conversations, 1057 triage-level dialogues were used, with 950 for training and 107 for testing. Our model with hierarchical structure achieved accuracies of 75.94%, significantly outperforming ChatGPT (56.68%) or fine-tuned ClinicalBERT (69.42%). For urgent cases, the best model achieved a recall of 0.9610, outperforming ChatGPT (0.5352). SHapley Additive exPlanations analysis confirmed that our model focused on clinically relevant cues aligned with KTAS criteria.

Conclusion: BERT-based LLMs trained on real-world ED conversations significantly outperform general-purpose models like ChatGPT in triage accuracy. This approach demonstrates the potential for enhancing clinical decision support with interpretable and efficient AI.

目的：在急诊科（ED）准确的分类是关键的适当的资源分配。虽然人工智能（AI）已经被用于分类，但之前的模型依赖于总结的临床场景。我们的目标是开发和评估大型语言模型（llm），这些模型训练于现实世界的临床对话，以对患者的紧急程度进行分类。材料和方法：我们使用了来自3家韩国三级医院的匿名分类级别对话的国家管理数据集。开发了两个基于bert的模型，根据韩国分诊和急性程度量表（KTAS）将紧急程度分为紧急（KTAS 3）或非紧急（KTAS 4-5）。一个模型将整个对话标记化，而另一个模型则应用具有句子级标记化和说话者角色嵌入的分层结构。性能指标包括准确性、精密度、召回率和f1分数。我们将我们的模型与ChatGPT gpt - 40和ClinicalBERT进行比较，并使用SHapley加法解释（SHAP）评估可解释性。结果：共使用5244个临床对话，1057个分诊级别对话，其中950个用于培训，107个用于测试。我们的分层结构模型达到了75.94%的准确率，显著优于ChatGPT（56.68%）或微调后的ClinicalBERT（69.42%）。对于紧急情况，最佳模型的召回率为0.9610，优于ChatGPT（0.5352）。SHapley加性解释分析证实，我们的模型专注于与KTAS标准一致的临床相关线索。结论：基于bert的llm在现实世界的ED对话中训练，在分类准确性方面明显优于ChatGPT等通用模型。这种方法展示了通过可解释和高效的人工智能增强临床决策支持的潜力。

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引用次数: 0

Functional and moral brand lapses sparking negative online brand engagement and anti-brand community growth 功能性和道德品牌的缺失引发了负面的在线品牌参与和反品牌社区的增长

IF 9.8 1区管理学 Q1 BUSINESS

Journal of Business Research

Pub Date : 2026-02-04 DOI: 10.1016/j.jbusres.2026.116038

Xinyu Dong , Cleopatra Veloutsou , Anna Morgan-Thomas

Although brand negativity presents a significant challenge for managers, research has not fully engaged with brand-related factors that contribute to its emergence. Guided by attribution theory and social identity theory and using two online surveys targeting distinct groups (anti-brand communities’ non-members and members), this work examines how the perceptions of functional (brand failure severity, perceived brand quality) and moral (unacceptable brand behaviour) brand lapses shape engagement evolution from individual action (negative online brand engagement) to collective behaviour (intention to join or participate in anti-brand communities). Structural-equation modelling tests the intricate relationship between brand lapses and the four negative online brand engagement dimensions (negative cognition, negative affection, online constructive and destructive behaviours), further disclosing how individual online negativity can drive collective movements within anti-brand communities, showing how lone complaints can escalate into organised opposition. The results offer managers early-warning indicators and segment-specific tactics for containing functional and moral crises.

尽管品牌消极性对管理者来说是一个重大挑战，但研究尚未充分涉及导致其出现的与品牌相关的因素。在归因理论和社会认同理论的指导下，利用针对不同群体（反品牌社区的非成员和成员）的两项在线调查，本研究探讨了功能性（品牌失败严重程度、感知品牌质量）和道德（不可接受的品牌行为）品牌失误的感知如何塑造了从个人行为（消极的在线品牌参与）到集体行为（加入或参与反品牌社区的意图）的参与演变。结构方程模型测试了品牌缺失与四个负面在线品牌参与维度（负面认知、负面情感、在线建设性和破坏性行为）之间的复杂关系，进一步揭示了个人在线消极情绪如何推动反品牌社区内的集体运动，显示了单独的投诉如何升级为有组织的反对。研究结果为管理者提供了早期预警指标和细分市场特定策略，以遏制功能危机和道德危机。

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引用次数: 0

Disclosing the twin transition: digital and green transformation in Italian universities from an institutional perspective 揭示双重转型：从制度角度看意大利大学的数字化和绿色转型

IF 18.1 1区管理学 Q1 BUSINESS

Journal of Innovation & Knowledge

Pub Date : 2026-02-04 DOI: 10.1016/j.jik.2026.100964

Claudia Spilotro, Michele Posa, Giustina Secundo, Ivano De Turi

引用次数: 0

Exploring the impact of visual aesthetic elements in films on audiences’ attitudes towards travel to a destination: An aesthetic research framework 探讨电影中视觉美学元素对观众前往目的地旅游态度的影响：一个美学研究框架

IF 8.3 1区管理学 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM

Journal of Hospitality and Tourism Management

Pub Date : 2026-02-04 DOI: 10.1016/j.jhtm.2026.101411

Xiaohong Wu, Ivan Ka Wai Lai