Technological Forecasting and Social Change最新文献

Designing a global resilient vaccine supply chain: Forecasting with hybrid neural network 全球弹性疫苗供应链设计：混合神经网络预测

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-02-02 DOI: 10.1016/j.techfore.2026.124524

Ehsan Torshizi , Fatemeh Sabouhi , Ali Bozorgi-Amiri

In the present research, a hybrid decision-making and data-driven optimization approach is developed based on economic management theory to design a global COVID-19 vaccine supply chain. Economic management theory includes three complementary theories of information processing, transaction cost economics, and the resource-based view/dynamic capabilities to examine the logic of the proposed approach. The first phase involves assessing the efficiency of foreign suppliers and manufacturers through non-radial data envelopment analysis. In this phase, the foreign exchange rate parameter is forecasted using the hybrid neural network. Then, the second phase introduces a multi-objective optimization model for designing a vaccine supply chain under uncertain conditions. Flow complexity, node complexity, and node criticality are considered in the model to increase the overall resilience of the network. To deal with the uncertainty of the problem, a stochastic robust optimization model is employed. The objective functions aim to maximize supply chain efficiency and minimize the non-resilience of the network and the total cost. The approach implemented in this research is validated by an actual-world case study in Iran. The findings highlight that resilience indicators can improve economic costs by up to 13% and network efficiency by up to 18% under the worst-case pandemic scenario. Also, the implemented forecasting algorithm performs better than other methods based on R2, RMSE, MSE, and MAE metrics. Lastly, a comprehensive analysis is performed on the computational results obtained, which derives some practical managerial insights.

本文基于经济管理理论，提出了一种基于决策和数据驱动的混合优化方法来设计全球COVID-19疫苗供应链。经济管理理论包括三个互补的理论：信息处理、交易成本经济学和基于资源的观点/动态能力，以检验所提出方法的逻辑。第一阶段涉及通过非径向数据包络分析评估外国供应商和制造商的效率。在这一阶段，使用混合神经网络对外汇汇率参数进行预测。第二阶段引入了不确定条件下疫苗供应链设计的多目标优化模型。模型中考虑了流复杂性、节点复杂性和节点临界性，以提高网络的整体弹性。为了处理问题的不确定性，采用了随机鲁棒优化模型。目标函数旨在使供应链效率最大化，使网络的非弹性和总成本最小化。本研究中采用的方法通过伊朗的实际案例研究得到了验证。研究结果强调，在最坏的大流行情景下，弹性指标可以将经济成本提高13%，将网络效率提高18%。此外，实现的预测算法比基于R2、RMSE、MSE和MAE指标的其他方法表现更好。最后，对计算结果进行了综合分析，得出了一些实用的管理见解。

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引用次数: 0

Generative AI-driven transition to circular and responsible supply chains: Unpacking the dynamics of eco-centric design intelligence and ethical responsiveness 生成式人工智能驱动的向循环和负责任供应链的过渡：揭示以生态为中心的设计智能和道德响应的动态

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-01-29 DOI: 10.1016/j.techfore.2025.124522

Eszter Lukács , Sabrine Mallek , Jiyang Cheng

The study focuses on understanding how the use of generative Artificial Intelligence (AI) can beneficially result in circular supply chain transformation while embedding design intelligence, ethical intelligence, and predictive intelligence within socio-technical systems. This study proposes and validates a model that integrates generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness, which collectively affect circular supply chain resilience and socio-environmental value realization, mediated by Sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To test the hypothesis, data were collected from 264 professionals in supply chain and technology-related industries in the USA. As the findings suggest, generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness significantly enhance sustainable process reconfiguration capability, which drives AI-enabled stakeholder co-creation. A serial mediation model indicates that Generative AI capabilities affect circular supply chain resilience and socio-environmental value realization via sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To our surprise, the regenerative policy ambidexterity negatively moderates the relationship between AI-enabled stakeholder co-creation and the realization of socio-environmental value. The results provide actionable advice for managers implementing generative AI in sustainable supply chains. Instead of focusing solely on algorithmic efficiency, if an organization can develop reconfiguration capability and engage stakeholders, it would generate systemic sustainability benefits.

该研究的重点是了解生成式人工智能（AI）的使用如何在社会技术系统中嵌入设计智能、伦理智能和预测智能的同时，有利于循环供应链的转型。本研究提出并验证了一个集成了生成生态设计智能、预测性循环供应链规划和伦理生成人工智能意识的模型，这些模型共同影响循环供应链弹性和社会环境价值实现，由可持续流程重构能力和人工智能支持的利益相关者共同创造介导。为了验证这一假设，我们从美国供应链和技术相关行业的264名专业人士中收集了数据。研究结果表明，生成式生态设计智能、预测性循环供应链规划和伦理生成式人工智能意识显著增强了可持续流程重构能力，从而推动了人工智能驱动的利益相关者共同创造。序列中介模型表明，生成式人工智能能力通过可持续流程重构能力和人工智能支持的利益相关者共同创造，影响循环供应链弹性和社会环境价值实现。令我们惊讶的是，再生政策的双重性负向调节了人工智能驱动的利益相关者共同创造与社会环境价值实现之间的关系。研究结果为管理人员在可持续供应链中实施生成式人工智能提供了可行的建议。如果一个组织能够发展重构能力并吸引利益相关者，它将产生系统性的可持续性效益，而不是仅仅关注算法效率。

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引用次数: 0

Enhancing data governance through transparency: An empirical study of the data trust model 通过透明度加强数据治理：数据信任模型的实证研究

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-01-28 DOI: 10.1016/j.techfore.2026.124551

Yei Jin Kim , Young Soo Park , Sung-Pil Park

As data-driven ecosystems expand, the Data Trust Model (DTM) has gained attention as a governance framework for secure transactions, yet adoption remains uncertain due to high information asymmetry and the dual burden of evaluating asset quality and transactional risk. Prior research has largely emphasized supply-side institutional design, treating transparency as a monolithic construct and overlooking user heterogeneity. To address these limitations, this study develops a context-specific model integrating the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Transparency is bifurcated into Perceived Data Transparency (adverse selection) and Perceived Transaction Transparency (moral hazard) within an Agency Theory framework. The model is tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (MGA) based on data from 400 potential users. Results show that transparency operates as a conditional enabler mediated by attitude rather than a direct driver. MGA further reveals systematic heterogeneity: experienced users rely more heavily on institutional signals—reputation, security, and warranty—when forming perceptions. Theoretically, this study integrates Agency and Signaling Theories to explain adoption under uncertainty. Practically, findings highlight the need for differentiated transparency mechanisms tailored to user experience.

随着数据驱动生态系统的扩展，数据信任模型（DTM）作为安全交易的治理框架受到了关注，但由于信息高度不对称以及评估资产质量和交易风险的双重负担，采用仍然不确定。先前的研究主要强调供给侧的制度设计，将透明度视为一个整体结构，忽视了用户的异质性。为了解决这些局限性，本研究开发了一个结合技术接受模型（TAM）和计划行为理论（TPB）的情境特定模型。在代理理论框架下，透明度分为感知数据透明度（逆向选择）和感知交易透明度（道德风险）。基于400名潜在用户的数据，采用偏最小二乘结构方程模型（PLS-SEM）和多组分析（MGA）对模型进行了测试。结果表明，透明度是态度介导的条件促成因素，而不是直接驱动因素。MGA进一步揭示了系统异质性：有经验的用户在形成感知时更依赖于制度信号——声誉、安全性和保修。在理论上，本研究结合代理理论和信号理论来解释不确定性下的采用。实际上，研究结果强调了针对用户体验量身定制的差异化透明度机制的必要性。

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引用次数: 0

Leveraging generative AI and circular innovation for equitable and resilient supply chains: The mediating role of transparency and sustainability-oriented decision empowerment 利用生成式人工智能和循环创新实现公平和有弹性的供应链：透明度和面向可持续性的决策赋权的中介作用

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-01-28 DOI: 10.1016/j.techfore.2026.124556

Yanfang Xia , Yong Qiu , Zhuoyu Gu , Liang Zhang , Jiayu Yang

Sustainability is now a business necessity as climate pressures, digital transformation, and supply chain shocks push concerns on the table. In response to this agenda, this study proposes and tests an empirically grounded sociotechnical framework in which three generative AI-enabled enablers act in concert to amplify supply chain transparency. Supply chain transparency allows decisions to be made that lead to fair and resilient supply outcomes. The present research shows how AI-enabled decision intelligence and circular innovation practices can enhance organizational transparency and the manager's potential to make inclusion-oriented, sustainable, equitable and resilient decisions through a socio-technical framework. Survey responses were used to empirically test the prepositions using a structural equation modelling framework. The research expands the theory of socio-technical systems. As such, it shows the need for technical (AI-enabled decision intelligence, circular innovation alignment) and cultural (responsible AI communication culture) capabilities. These should co-evolve with transparency and decision architectures for attaining social resilience. The study has practical implications for managers. They will have to invest money in not just generative AI powered analytics but also responsible communication norms. Moreover, aligning with circular innovation can aid in unlocking data visibility and inclusive decision loop.

随着气候压力、数字化转型和供应链冲击将关注的问题提上日程，可持续发展现在是一项商业必需品。为了响应这一议程，本研究提出并测试了一个基于经验的社会技术框架，其中三个生成式人工智能使能者协同行动，以扩大供应链的透明度。供应链的透明度使决策能够产生公平和有弹性的供应结果。目前的研究表明，人工智能支持的决策智能和循环创新实践如何提高组织透明度，并通过社会技术框架提高管理者做出包容导向、可持续、公平和有弹性决策的潜力。使用结构方程建模框架对调查结果进行实证检验。本研究拓展了社会技术系统理论。因此，它显示了对技术（人工智能支持的决策智能，循环创新对齐）和文化（负责任的人工智能沟通文化）能力的需求。这些应与透明度和决策架构共同发展，以实现社会弹性。该研究对管理者具有实际意义。他们不仅要投资于生成式人工智能分析，还要投资于负责任的沟通规范。此外，与循环创新保持一致有助于解锁数据可见性和包容性决策循环。

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引用次数: 0

Disappointed with Siri: Expectation–experience gaps in human–AI interaction 对Siri的失望：人类与人工智能互动中的预期体验差距

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-01-28 DOI: 10.1016/j.techfore.2026.124540

You Jin Song , Joohye Park , Sun Kyong Lee

Voice assistants such as Siri increasingly mediate everyday tasks, yet negative experiences with these systems remain understudied. Guided by social representation theory, we use a sequential mixed-methods design—topic modeling of user narratives followed by in-depth interviews—to characterize dissatisfaction with a voice-based AI and to explain how its meanings differ by users' gender. Topic modeling surfaces a broad “inconvenience/disruption” cluster alongside frequent references to speech-recognition errors. Interviews then reveal the interpretive logics beneath these signals: men tend to read failures as breaches of technical performance and task logic, whereas women more often construe the same events as violations of social expectations. These gendered interpretations show that dissatisfaction is not merely an individual usability outcome but a socially anchored perception organized by shared representational frames. The study contributes (1) a theoretically grounded account of how gender structures sense-making around AI malfunctions, (2) a methodological synthesis that links computational signals to qualitative representation mapping, and (3) design implications that anticipate divergent expectations without reinforcing stereotypes. By moving beyond frequency counts to interpretive coherence, the work advances understanding of why the same Siri behavior can produce different forms of dissatisfaction across users.

Siri等语音助手越来越多地调解日常任务，但这些系统的负面体验仍未得到充分研究。在社会表征理论的指导下，我们使用顺序混合方法设计-用户叙述的主题建模，然后进行深度访谈-来表征对基于语音的人工智能的不满，并解释其含义如何因用户性别而异。主题建模显示了广泛的“不便/中断”集群，以及频繁提及的语音识别错误。访谈揭示了这些信号背后的解释逻辑：男性倾向于将失败解读为对技术表现和任务逻辑的破坏，而女性则更多地将同样的事件解读为对社会期望的破坏。这些性别化的解释表明，不满意不仅仅是个人可用性的结果，而是由共享的代表性框架组织的社会锚定感知。该研究贡献了(1)基于理论的关于性别如何围绕人工智能故障构建意义的解释，(2)将计算信号与定性表征映射联系起来的方法综合，以及(3)在不强化刻板印象的情况下预测不同期望的设计含义。通过超越频率计数到解释一致性，这项工作促进了对为什么相同的Siri行为会在用户之间产生不同形式的不满的理解。

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引用次数: 0

A socio-technical perspective on product configuration systems: Insights from Grundfos 产品配置系统的社会技术视角：格兰富的见解

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-01-27 DOI: 10.1016/j.techfore.2026.124564

Anders M.S.Ø. Jakobsen, Wim Vanhaverbeke

This study addresses the question of how user roles in Product Configuration Systems (PCS) function as socio-technical agents shaping operational efficiency, digital knowledge integration, and sustainable product customization in manufacturing. Grounded in socio-technical systems theory, the study analyses three years of PCS usage data from Grundfos—a global industrial pump manufacturer—to explore the socio-technical interplay between automated configuration processes and human expertise. Findings reveal that PCS effectiveness varies across Sales, Engineering, and Manufacturing roles: automation accelerates routine configurations, but human expertise remains crucial for complex cases. A regional analysis of global usage patterns indicates that highly automated regions achieve efficiency gains yet require expert oversight, whereas regions reliant on manual processes face digital adoption barriers that limit the system's optimization potential. Moreover, many PCS errors stem from misalignments between system constraints and user adaptations, underscoring the socio-technical nature of these challenges and the need for continuous human–technology alignment. Based on these insights, the study offers three key contributions to theory and practice: (1) a new conceptualization of PCS user roles as socio-technical agents; (2) a theoretical explanation of how user interactions shape PCS outcomes; and (3) a practical framework for embedding sustainability considerations into PCS workflows and decision-making.

本研究解决了产品配置系统（PCS）中的用户角色如何作为社会技术代理人塑造制造中的运营效率、数字知识集成和可持续产品定制的问题。基于社会技术系统理论，该研究分析了全球工业泵制造商格兰富（grundfos）三年的PCS使用数据，以探索自动化配置过程与人类专业知识之间的社会技术相互作用。调查结果显示，PCS的有效性因销售、工程和制造角色而异：自动化加速了常规配置，但对于复杂的情况，人类的专业知识仍然至关重要。对全球使用模式的区域分析表明，高度自动化的地区可以提高效率，但需要专家监督，而依赖人工流程的地区则面临数字采用障碍，限制了系统的优化潜力。此外，许多PCS错误源于系统约束和用户适应之间的不协调，强调了这些挑战的社会技术性质以及持续的人与技术协调的必要性。基于这些见解，本研究提出了三个关键的理论和实践贡献：(1)PCS用户角色作为社会技术代理人的新概念；(2)用户交互如何影响PCS结果的理论解释；(3)将可持续性考虑纳入PCS工作流程和决策的实用框架。

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引用次数: 0

Does AI value the environment? Evaluation of AI value alignment 人工智能重视环境吗？评估人工智能的价值一致性

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-01-26 DOI: 10.1016/j.techfore.2026.124550

Wanggi Jaung

As a general-purpose technology, artificial intelligence (AI) is increasingly shaping environmental outcomes, yet its alignment with environmental values remains unclear. We assess this alignment by comparing environmental valuations from three open-source AI models (Gemma 2, Llama 3.1, and Mistral) with those of human stakeholders. Using choice experiment studies from 21 countries, we drew on estimates of human marginal willingness to pay (MWTP) for environmental attributes and replicated the same designs with the AI models. Across countries and attributes, the models assigned consistently higher MWTP than humans, with larger gaps in Western countries and for non-use values such as existence and bequest values. These results suggest that prevailing human values may be an insufficient benchmark for evaluating AI alignment, even as adopting more stringent AI-driven environmental standards raises practical and ethical concerns. Differences across models further indicate that a diverse AI model ecosystem could support pluralistic rather than homogenized environmental values. Together, these findings provide a quantitative basis for understanding AI–environment value alignment and for designing environmentally responsible AI systems.

作为一种通用技术，人工智能（AI）正日益影响环境结果，但其与环境价值的一致性尚不清楚。我们通过比较三个开源人工智能模型（Gemma 2、Llama 3.1和Mistral）与人类利益相关者的环境评估来评估这种一致性。利用来自21个国家的选择实验研究，我们利用了人类对环境属性的边际支付意愿（MWTP）的估计，并用人工智能模型复制了相同的设计。在不同的国家和属性中，模型分配的MWTP始终高于人类，在西方国家和非使用价值（如存在和遗产价值）方面的差距更大。这些结果表明，普遍的人类价值观可能不足以作为评估人工智能一致性的基准，即使采用更严格的人工智能驱动的环境标准会引发实际和道德问题。模型之间的差异进一步表明，多样化的人工智能模型生态系统可以支持多元化而不是同质化的环境价值。总之，这些发现为理解人工智能-环境价值一致性和设计对环境负责的人工智能系统提供了定量基础。

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引用次数: 0

Unlocking the institutional foundations of green innovation: A machine learning analysis 解锁绿色创新的制度基础：机器学习分析

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-01-26 DOI: 10.1016/j.techfore.2026.124552

Tao Wang , Kaifan Luo , Chao Yu

Institutional elements are crucial drivers of corporate green innovation. Although existing research has acknowledged the important role of institutional elements in corporate green innovation and explored their interrelations, a comprehensive understanding of their relative importance and nonlinear impacts remains limited. To address this gap, this study draws on institutional theory and employs multiple machine learning algorithms, along with SHAP value analysis, using data from Chinese A-share listed companies (2011−2022) to systematically assess the influence of regulative, normative, and cultural-cognitive elements on firms' green innovation. The findings reveal that, among institutional elements, cultural-cognitive elements exert the most significant influence on firm green innovation. Specifically, central inspections, public attention, and industry-level green orientation are the predominant factors within their respective institutional categories. Most institutional elements exhibit significant nonlinear relationships with green innovation. Further analysis indicates that cultural-cognitive elements can, under certain conditions, impede green innovation, whereas regulative and normative elements generally foster it. Moreover, the impact of institutional elements demonstrates considerable heterogeneity across different regions, industries, and firm sizes. This study highlights the importance and interplay of institutional elements in shaping firm green innovation, offering insights for emerging economies to tailor policies and support firms' sustainable transformation.

制度因素是企业绿色创新的关键驱动力。虽然已有的研究已经认识到制度要素在企业绿色创新中的重要作用，并探索了它们之间的相互关系，但对它们的相对重要性和非线性影响的全面认识仍然有限。为了解决这一差距，本研究借鉴制度理论，采用多种机器学习算法，以及SHAP价值分析，利用中国a股上市公司（2011 - 2022）的数据，系统评估监管、规范和文化认知因素对企业绿色创新的影响。研究发现，在制度因素中，文化认知因素对企业绿色创新的影响最为显著。具体而言，中央督查、公众关注和行业层面的绿色导向是各自制度范畴内的主导因素。大多数制度要素与绿色创新表现出显著的非线性关系。进一步分析表明，在某些条件下，文化认知因素会阻碍绿色创新，而监管和规范因素通常会促进绿色创新。此外，制度因素的影响在不同地区、行业和企业规模之间表现出相当大的异质性。本研究强调了制度要素在塑造企业绿色创新中的重要性及其相互作用，为新兴经济体制定政策和支持企业可持续转型提供了见解。

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引用次数: 0

Triple helix trust and spillovers for sustainable innovation: The role of governance, openness, and digital infrastructure 可持续创新的三螺旋信任和溢出效应：治理、开放和数字基础设施的作用

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-01-26 DOI: 10.1016/j.techfore.2026.124548

Lei Liu , Liying Zhou , Prashant Sharma , Ebtesam Abdullah Alzeiby , Snigdha Dash

Knowledge-driven economies rely on innovation ecosystems and solutions that leverage digital infrastructure and viable governance supported by collaborative openness among institutions. This study aims to examine the effect of collaborative institutional openness, multi-level governance alignment, and digital civic infrastructure readiness on socially embedded knowledge spillover and triple helix trust density, and resulting outcomes on sustainable innovation performance and societal knowledge value realisation. Additionally, the study examines the moderating role of mission-oriented entrepreneurial orientation behavior for mission-oriented innovation. Implementing collaborative institutional openness, multi-level governance alignment, and digital civic infrastructure readiness enhances socially embedded knowledge spillover and thereby increases triple helix trust density. The two sequential methods help improve sustainable innovation performance and the realisation of societal knowledge value, underscoring the importance of trust-based relational architectures for innovation systems. Intriguingly, mission-oriented entrepreneurial orientation is negatively associated with the relationship connectedness of socially embedded knowledge spillover–triple helix trust density. If a collaborator focuses solely on a shared objective, it can be challenging to build trust with them. The research explains how structural, digital, and behavioral enablers impact sustainable innovation. Innovation ecosystems need to be open, ready for digital technology, trust-based, capable of changing their mission, and governed reflexively.

知识驱动型经济依赖于创新生态系统和解决方案，这些生态系统和解决方案利用数字基础设施和机构间协作开放支持的可行治理。本研究旨在探讨合作性制度开放、多层次治理一致性和数字公民基础设施就绪程度对社会嵌入式知识溢出和三螺旋信任密度的影响，以及由此产生的可持续创新绩效和社会知识价值实现的影响。此外，本研究还考察了使命导向创业行为对使命导向创新的调节作用。实施协作性制度开放、多层次治理协调和数字公民基础设施就绪，可增强社会嵌入知识溢出，从而提高三螺旋信任密度。这两种顺序方法有助于提高可持续创新绩效和社会知识价值的实现，强调了基于信任的创新系统关系架构的重要性。有趣的是，任务导向的创业导向与社会嵌入知识溢出-三螺旋信任密度的关系连通性呈负相关。如果一个合作者只专注于一个共同的目标，那么与他们建立信任是很有挑战性的。该研究解释了结构、数字和行为驱动因素如何影响可持续创新。创新生态系统需要是开放的，为数字技术做好准备，以信任为基础，能够改变其使命，并进行反射性管理。

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引用次数: 0

Waste management–related trust, acceptance, and reputation: A multidisciplinary big data analysis across knowledge domains 废物管理相关的信任、接受和声誉：跨知识领域的多学科大数据分析

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-01-23 DOI: 10.1016/j.techfore.2026.124553

Kalle Nuortimo , Janne Härkönen , Kristijan Breznik

Addressing global waste management challenges requires understanding not only the technical capabilities of products and technologies but also the factors shaping their development and deployment across the waste hierarchy. Deployment outcomes are strongly influenced by acceptance, reputation, and trust, distinct yet interrelated constructs whose dynamics remain insufficiently understood. Deepening this understanding can enhance stakeholder engagement and improve decision-making in waste management. This study examines waste-to-energy incineration as a representative case to investigate these dynamics across global, regional, and local levels. A multidisciplinary, data-driven approach is applied, combining artificial intelligence, big data analytics, opinion mining, Correspondence Analysis on Generalized Aggregated Lexical Tables, and content classification to assess acceptance, trust, and reputation in multiple knowledge domains. The analysis clarifies these constructs as interwoven but individually influential factors shaping technology deployment and explores their interplay with public perception. A novel method is also introduced for generating indicative reputation scores derived from sentiment analysis. The findings show that AI-enhanced analytical tools, when integrated with established methods, yield valuable insights into stakeholder sentiment and public discourse. These insights can inform more targeted stakeholder engagement and strategic communication in waste management planning. Overall, the study demonstrates the potential of emerging analytical tools to produce timely, structured indicators of trust, acceptance, and reputation, key dimensions for navigating the socio-political challenges of technology deployment in the waste sector.

解决全球废物管理挑战不仅需要了解产品和技术的技术能力，还需要了解影响其发展和在废物层次结构中部署的因素。部署结果受到接受度、声誉和信任的强烈影响，这些不同但相互关联的结构的动态仍然没有得到充分的理解。深化这种理解可以加强利益相关者的参与，改善废物管理方面的决策。本研究以垃圾焚烧发电为代表案例，在全球、区域和地方层面调查这些动态。采用多学科、数据驱动的方法，结合人工智能、大数据分析、意见挖掘、广义聚合词法表对应分析和内容分类，评估多个知识领域的接受度、信任和声誉。分析澄清了这些结构是相互交织的，但单独的影响因素塑造技术部署，并探讨了它们与公众认知的相互作用。还介绍了一种从情感分析中产生指示性声誉分数的新方法。研究结果表明，人工智能增强的分析工具与现有方法相结合，可以对利益相关者的情绪和公共话语产生有价值的见解。这些见解可以为废物管理规划中更有针对性的利益相关者参与和战略沟通提供信息。总体而言，该研究展示了新兴分析工具在产生及时、结构化的信任、接受度和声誉指标方面的潜力，这些指标是应对垃圾行业技术部署所面临的社会政治挑战的关键维度。

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引用次数: 0