Technological Forecasting and Social Change最新文献_第3页

Learning through decision episodes: A narrative-QCA study of ESG founder typologies in sustainable startups 通过决策事件学习：可持续创业公司ESG创始人类型的叙述性qca研究

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-05-01 Epub Date: 2026-02-05 DOI: 10.1016/j.techfore.2026.124562

María Eizaguirre , Jose Antonio Vicente-Pascual , Andreas Kallmuenzer

This study explores how early-stage sustainable entrepreneurs interpret and navigate trade-offs between financial imperatives and ESG commitments, and how these moments of choice catalyse entrepreneurial learning. Drawing on a mixed-method approach, the research introduces “decision episodes” as critical moments that trigger reflection, identity strain, and strategic action. Based on six life-history interviews with startup founders in the fashion industry, the qualitative phase identifies recurring tensions that structure the fuzzy-set Qualitative Comparative Analysis (fsQCA) of 340 sustainable fashion crowdfunding campaigns, which provide the empirical setting for observing patterns of entrepreneurial sustainability. Findings show that learning is triggered by moral tension and internal dilemmas that expose incoherence between identity and strategy. Circularity emerges as essential to give governance operational force and strategic direction; without credible circular practices, even principled governance fails to generate legitimacy or traction. The study develops a predictive taxonomy of founder types by aligning moral-symbolic ESG framings with operational choices. It contributes to theory by linking entrepreneurial learning to ESG governance through the lens of emancipation and liminality, using crowdfunding as the empirical arena in which this learning becomes observable, and by offering practical insights for ecosystem actors seeking to support value-driven ventures.

本研究探讨了早期可持续发展企业家如何在财务要求和ESG承诺之间解释和权衡，以及这些选择时刻如何催化创业学习。该研究采用混合方法，将“决策事件”作为触发反思、身份紧张和战略行动的关键时刻。基于对时尚行业创业公司创始人的六次生活史访谈，定性阶段确定了反复出现的紧张关系，这些紧张关系构成了340个可持续时尚众筹活动的模糊集定性比较分析（fsQCA），这为观察创业可持续性模式提供了经验背景。研究结果表明，学习是由道德紧张和内部困境引发的，这些困境暴露了身份和策略之间的不一致性。循环对于赋予治理操作力量和战略方向至关重要；如果没有可信的循环实践，即使是有原则的治理也无法产生合法性或吸引力。该研究通过将道德-符号ESG框架与操作选择相结合，开发了一种预测性的创始人类型分类。它通过解放和限制的视角将创业学习与ESG治理联系起来，利用众筹作为经验舞台，使这种学习成为可观察的，并为寻求支持价值驱动型企业的生态系统参与者提供实际见解，从而为理论做出贡献。

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引用次数: 0

AI adoption in healthcare organizations: Spheres of development and the virtue of visible value 人工智能在医疗机构中的应用：发展领域和可见价值的美德

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-05-01 Epub Date: 2026-02-09 DOI: 10.1016/j.techfore.2026.124541

Rikke Duus , Mike Cooray , Simon Lilley

Despite the significant potential of artificial intelligence (AI), many public healthcare organizations struggle to adopt this technology. Simultaneously, empirical research on the factors that influence AI adoption in public healthcare settings remains scarce. We address this gap with a qualitative study with senior healthcare leaders who hold responsibility for the adoption and development of AI-enabled solutions within two leading National Healthcare Service (NHS) Trusts in the United Kingdom. Drawing upon the Technology-Organization-Environment (TOE) framework to make sense of our findings, we reveal nine factors that influence AI adoption. Significantly, we buttress our use of the TOE framework by highlighting how each TOE context can evolve into a sphere of AI development and explore the positively mediating role of a meta-factor that we term ‘visible value’ in that process. Visible value can be explained as the demonstrated or executed positive impact relating to AI adoption activities, where the value can be vividly seen, experienced, agreed upon and, ideally, measured, and helps to drive momentum internally and/or with key external stakeholders. Generation of visible value helps to legitimize and build confidence in continued AI activities. We thus develop existing theoretical resources to advance practical understanding of AI adoption in public healthcare organizations.

尽管人工智能（AI）具有巨大的潜力，但许多公共医疗机构仍在努力采用这项技术。同时，关于影响公共医疗机构采用人工智能的因素的实证研究仍然很少。我们通过对英国两家领先的国家医疗服务（NHS）信托基金中负责采用和开发人工智能解决方案的高级医疗保健领导者进行定性研究来解决这一差距。利用技术-组织-环境（TOE）框架来理解我们的发现，我们揭示了影响人工智能采用的九个因素。值得注意的是，我们通过强调每个TOE上下文如何演变成人工智能开发领域，并探索我们称之为“可见价值”的元因素在该过程中的积极中介作用，来支持我们对TOE框架的使用。可见价值可以解释为与人工智能采用活动相关的演示或执行的积极影响，其中价值可以生动地看到，体验，商定，理想情况下，可以衡量，并有助于推动内部和/或关键外部利益相关者的势头。产生可见价值有助于使人工智能活动合法化并建立信心。因此，我们开发现有的理论资源，以促进对公共医疗保健组织采用人工智能的实践理解。

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引用次数: 0

Sustainable technology evolution in innovation ecosystems: A regulatory framework for (non)-convergent technologies 创新生态系统中的可持续技术演进：（非）趋同技术的监管框架

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-05-01 Epub Date: 2026-02-14 DOI: 10.1016/j.techfore.2026.124593

Christopher Agyapong Siaw , Joseph Amankwah-Amoah

Technological convergence is a critical driver of technological evolution and industrial transformation, yet, convergence also generates complex ethical, sustainability, and regulatory challenges that remain poorly understood and insufficiently theorized. This study advances a conceptual framework that explains the regulatory mechanisms shaping convergence and non-convergence dynamics across three ecosystem layers: components, products and applications, and support and infrastructure. By distinguishing these layers, the framework reveals how interdependent evolution can raise ethical, sustainability, and regulatory concerns at different levels. The study advances theory by (1) reconceptualizing convergence as a multi-layered phenomenon, (2) expanding analysis beyond appropriability indicators such as patents to include development-based mechanisms like licensing, (3) proposing a dual regulatory role in balancing innovation development and appropriation, and (4) showing how regulation conditions the effects of convergence and non-convergence on technological sustainability.

技术融合是技术进化和产业转型的关键驱动力，然而，融合也产生了复杂的伦理、可持续性和监管挑战，这些挑战仍然缺乏理解和充分的理论化。本研究提出了一个概念框架，解释了在三个生态系统层（组件、产品和应用、支持和基础设施）中形成趋同和非趋同动态的监管机制。通过区分这些层次，该框架揭示了相互依赖的进化如何在不同层次上提高伦理、可持续性和监管问题。该研究通过以下几个方面推动了理论的发展：(1)将趋同重新定义为一种多层现象；(2)将分析范围从专利等适宜性指标扩展到许可等基于发展的机制；(3)提出了平衡创新发展和适宜性的双重监管作用；(4)展示了监管如何制约趋同和非趋同对技术可持续性的影响。

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引用次数: 0

A structural-evolutionary rationale for public support of private technological innovation 公共支持私人技术创新的结构演化理论

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-04-01 Epub Date: 2026-01-17 DOI: 10.1016/j.techfore.2026.124539

Kenneth I. Carlaw , Gregory Bridgett

Dynamic competition in technological innovation drives long run economic growth. We find a rationale for public support of private technological innovation in features of this process which are central to our appreciative structural-evolutionary growth theory presented here. Agents face uncertainty and allocate resources to innovative endeavors based on subjective perceptions of potential opportunities that are generated by and limited to the evolving structural context in which they operate. Efforts cannot be evaluated on optimality criteria of equilibrium models because much of the value that may come to be associated with originating innovations is yet to be determined over the uncertain futures of their development trajectories. The system's history determines its present and future states. Influencing agent behaviours with respect to technological innovation alters the evolutionary path of economic growth. Policy that is historically conditioned, selectively focused and embedded in the structure of technology and the economy induces beneficial technological innovation trajectories. This role is absent from the equilibrium approach to economic growth theory in which fully-informed agents allocate resources based on calculations of optimal returns, producing growth on a stationary balanced growth path. In this approach, policy's sole purpose is the correction of divergence between social and private returns.

技术创新的动态竞争驱动着经济的长期增长。我们在这一过程的特征中发现了公共支持私人技术创新的基本原理，这是我们在这里提出的欣赏结构进化增长理论的核心。代理人面对不确定性，并根据对潜在机会的主观感知，将资源分配给创新努力，这些机会是由他们所处的不断变化的结构环境产生的，并受其限制。不能根据均衡模型的最优性标准来评估努力，因为可能与原始创新相关的许多价值尚未在其发展轨迹的不确定未来中确定。系统的历史决定了它现在和未来的状态。技术创新对主体行为的影响改变了经济增长的演化路径。受历史制约、有选择地集中和嵌入技术和经济结构的政策，会产生有益的技术创新轨迹。这种作用在经济增长理论的均衡方法中是不存在的，在均衡方法中，充分知情的主体根据最优回报的计算来分配资源，在平稳的平衡增长路径上产生增长。在这种方法中，政策的唯一目的是纠正社会和私人回报之间的差异。

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引用次数: 0

Digital technology diffusion through supply chain orchestration 数字技术通过供应链协调扩散

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-04-01 Epub Date: 2026-01-22 DOI: 10.1016/j.techfore.2026.124554

Lei Shen , Qingyue Shi , Debadrita Panda , Vinit Parida

Digital technology diffusion is reshaping innovation across supply chain dynamics and ecosystem partners. Prior work often looks at one firm or uniform settings, missing how technology diffusion need to be orchestrated by ecosystem leader across diverse suppliers and other actors. To address this gap, this study employs an exploratory case study of the intelligent vehicle ecosystem, focusing on an original equipment manufacturer (OEM) and six supply chain partners. Although positioned in the supply chain, these actors influence the wider ecosystem thereby affecting technology diffusion beyond dyadic ties. Drawing on 35 interviews, observations, and secondary data, the research advances the technology diffusion and innovation ecosystem literatures in three ways. First, it highlights the evolving role of focal actor as ecosystem leader, demonstrating their progression from transformational to collaborative and ultimately empowering roles across different phases of digital technology diffusion. Second, it identifies three orchestration mechanisms, namely knowledge orchestration, incentive aligned orchestration, and market driven orchestration, and specifies when each should be deployed in response to partner-specific requirements. Third, it offers a novel perspective on how ecosystem leaders shift value propositions to include both core and peripheral partners. From a practical standpoint, the study offers OEMs actionable guidance to diagnose their diffusion context and select appropriate orchestration mechanism. It also provides policymakers with insights for designing targeted instruments that strengthen digital diffusion and support sustainable industrial growth.

数字技术的扩散正在重塑供应链动态和生态系统合作伙伴之间的创新。之前的工作通常只关注一家公司或统一的环境，而忽略了生态系统领导者如何在不同的供应商和其他参与者之间协调技术扩散。为了解决这一差距，本研究采用了智能汽车生态系统的探索性案例研究，重点关注原始设备制造商（OEM）和六个供应链合作伙伴。尽管这些行为体处于供应链中，但它们影响着更广泛的生态系统，从而影响着超越二元关系的技术扩散。利用35个访谈、观察和二手数据，本研究从三个方面推进了技术扩散和创新生态系统的研究。首先，它强调了焦点参与者作为生态系统领导者的角色演变，展示了他们在数字技术扩散的不同阶段从转型到协作并最终赋予权力的角色的进展。其次，它确定了三种编排机制，即知识编排、激励一致的编排和市场驱动的编排，并指定了应在何时部署每种机制以响应特定于合作伙伴的需求。第三，它提供了一个关于生态系统领导者如何将价值主张转变为包括核心和外围合作伙伴的新视角。从实践的角度来看，本研究为oem厂商诊断其扩散环境和选择合适的协调机制提供了可操作的指导。它还为决策者提供了设计有针对性的工具的见解，以加强数字传播和支持可持续的工业增长。

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引用次数: 0

The digital-environmental tension: Managerial attention to digital transformation and energy consumption in healthcare organizations 数字环境的紧张关系：管理对医疗机构数字化转型和能源消耗的关注

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-04-01 Epub Date: 2026-01-10 DOI: 10.1016/j.techfore.2025.124519

Dingli Xi , Minhao Zhang , Gianluca Veronesi

Digital transformation is broadly recognized as a promising approach to solving complex and longstanding organizational challenges. However, its environmental implications, particularly within the public sector, remain underexplored. Drawing on the attention-based view, this study addresses this gap by investigating how managerial attention to digital transformation impacts organizational environmental performance. We utilize a fixed-effects model approach to conduct the analysis based on a sample of 118 NHS Foundation Trusts in England between 2016 and 2021. The results show that managerial attention to digital transformation is positively related to energy consumption intensity. Building on core assumptions from the behavioral theory of the firm, we further investigate the moderating role of R&D income intensity discrepancy on the link between digital transformation attention and energy consumption intensity. Our findings indicate that positive R&D income intensity discrepancy weakens the positive relationship between digital transformation attention and energy consumption, whereas negative discrepancy does not have any impact. This study contributes to the extant literature by advancing the understanding of the intersection between digital transformation and sustainability, while extending the theoretical applications of the attention-based view and behavioral theory of the firm within the public sector. The findings also offer insights for policymakers and practitioners seeking to mitigate unintended environmental consequences and promote more sustainable initiatives to digital transformation.

数字化转型被广泛认为是解决复杂和长期组织挑战的一种有前途的方法。但是，其环境影响，特别是在公共部门内的影响，仍未得到充分探讨。借鉴基于注意力的观点，本研究通过调查管理层对数字化转型的关注如何影响组织环境绩效来解决这一差距。我们利用固定效应模型方法对2016年至2021年间英国118家NHS基金会信托基金的样本进行了分析。结果表明，管理者对数字化转型的关注程度与能源消耗强度呈正相关。基于企业行为理论的核心假设，进一步探讨研发收入强度差异对数字化转型关注与能源消耗强度之间关系的调节作用。研究发现，研发收入强度的正差异削弱了数字化转型注意力与能源消耗之间的正相关关系，而负差异则不产生影响。本研究促进了对数字化转型与可持续发展之间交叉关系的理解，同时扩展了公共部门企业关注基础观点和行为理论的理论应用，为现有文献做出了贡献。研究结果还为寻求减轻意外环境后果和促进更可持续的数字化转型举措的政策制定者和从业者提供了见解。

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引用次数: 0

Integrating human support with algorithmic control: Psychological reactance in platform work 整合人类支持与算法控制：平台工作中的心理抗拒

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change

Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.techfore.2025.124520

Peng Hu , Xuru Wang , Jinqiang Wang

Algorithmic control has become a central feature of platform work, yet existing research primarily treats platform governance as an impersonal form of technological control. Drawing on self-determination theory, this study advances a co-managed human–algorithm governance perspective that conceptualizes workers' reactions as jointly shaped by algorithmic control and human support. We decompose workers' psychological reactance into cognitive and emotional forms and specify their distinct motivational antecedents. Our findings from platform-based delivery workers reveal that algorithmic control disrupts workers' sense of relatedness, giving rise to emotional reactance, while competence-related mechanisms play a limited role in this setting. Importantly, supervisor support attenuates the link between diminished relatedness and emotional reactance, underscoring the compensatory function of human intervention within algorithm-mediated work. By integrating algorithmic and human elements of control and differentiating the motivational structure of reactance, this study enriches theoretical understanding of the social and technological underpinnings of worker agency in platform labor.

算法控制已经成为平台工作的核心特征，然而现有的研究主要将平台治理视为一种非个人形式的技术控制。利用自我决定理论，本研究提出了一个共同管理的人类-算法治理视角，将工人的反应概念化为算法控制和人类支持共同塑造的。我们将工人的心理抗拒分解为认知和情感两种形式，并明确其不同的动机前因。我们对基于平台的送货员的研究结果表明，算法控制破坏了工人的归属感，引起了情绪上的抗拒，而与能力相关的机制在这种情况下发挥的作用有限。重要的是，主管的支持减弱了关系减少和情绪抗拒之间的联系，强调了在算法介导的工作中人为干预的补偿功能。通过整合控制的算法和人的因素，以及区分抗拒的动机结构，本研究丰富了对平台劳动中工人代理的社会和技术基础的理论认识。

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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 : 2026-04-01 Epub 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

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 : 2026-04-01 Epub 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

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 : 2026-04-01 Epub 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