Technology in Society最新文献_第7页

Achieving sustainable development with the application of artificial intelligence: Effects on global green energy security amid geopolitical risk 应用人工智能实现可持续发展：地缘政治风险下对全球绿色能源安全的影响

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-13 DOI: 10.1016/j.techsoc.2025.103201

Muhammad Zubair Chishti , Asif Ali , Muhammad Qasim Javaid , Oktay Özkan

Sustainable Development Goal 7 (SDG-7) aims to ensure 'affordable, reliable, sustainable, and modern energy for all' by 2030, a vision closely aligned with green energy security (GES). GES is a key driver of global sustainable development, fostering environmentally responsible economic growth and societal well-being. Global economies face growing vulnerabilities from climate change, geopolitical instability, and rising energy prices, so their independence is increasingly at risk. In this context, the role of artificial intelligence (AI) as an advanced technology becomes crucial in enhancing GES globally. Hence, our study analyzes the effects of AI on GES amid geopolitical risk (GPR), utilizing the daily data from 6 to 01–2018 to 30-08-2024. The application of two novel Quantile-Quantile TVP-VAR connectedness and the rolling windows wavelet quantile regression methods reveals that AI, in particular, consistently has positive effects on GES across the short, medium, and long run. In contrast, GPR exhibits mixed but predominantly adverse effects in all periods except for the long run, in which it has a mixed influence. The effects of the Paris Agreement (PA), digital financial inclusion (DFI), and green finance (GF) have mixed but predominantly positive effects, specifically in the long run. The results indicate that the relationships between AI, GPR, PA, GF, DFI, and GES are diverse and time-varying, depending on different market conditions. Hence, our study offers several time-horizon-wise policy implications for global authorities to target SDG-7 worldwide.

可持续发展目标7 （SDG-7）旨在确保到2030年“人人享有负担得起、可靠、可持续和现代的能源”，这一愿景与绿色能源安全（GES）密切相关。GES是全球可持续发展的关键驱动力，促进对环境负责的经济增长和社会福祉。由于气候变化、地缘政治不稳定和能源价格上涨，全球经济面临越来越大的脆弱性，因此它们的独立性日益受到威胁。在这种情况下，人工智能（AI）作为一种先进技术，在全球范围内提高GES的作用变得至关重要。因此，本研究利用2018年1月6日至2024年8月30日的日常数据，分析了人工智能对地缘政治风险（GPR）下GES的影响。两种新的分位数-分位数tpv - var连通性和滚动窗口小波分位数回归方法的应用表明，人工智能对短期、中期和长期的GES都有持续的积极影响。相比之下，探地雷达在除长期影响外的所有时期都表现出混合但主要是不利的影响。《巴黎协定》（PA）、数字普惠金融（DFI）和绿色金融（GF）的影响喜忧参半，但主要是积极的，特别是从长远来看。结果表明，AI、GPR、PA、GF、DFI和GES之间的关系具有多样性和时变性，取决于不同的市场条件。因此，我们的研究为全球当局在全球范围内实现可持续发展目标7提供了若干具有时间跨度的政策启示。

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引用次数: 0

Who is lifting the green veil? Climate physical risks and supply chain spillovers of corporate carbon greenwashing 是谁揭开了绿色的面纱？气候物理风险和企业碳漂绿的供应链溢出效应

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-13 DOI: 10.1016/j.techsoc.2025.103203

Yun Zhong , Han Yan , Ziqian Xia

Against the backdrop of intensifying global climate change, corporate greenwashing behavior is attracting widespread attention from regulators and academia. This paper investigates whether the climate physical risk faced by a customer is transmitted through the supply chain, influencing the supplier's selective disclosure of carbon information. Using a sample of Chinese A-share listed companies from 2008 to 2023, we find a significant result: Suppliers exhibit a markedly reduced level of carbon greenwashing when their principal customers are exposed to more severe climate physical risks. Mechanism analysis suggests that this inhibitory effect primarily operates through increased scrutiny on carbon reduction verification and a decline in the supplier management's future expectations. Furthermore, this effect is stronger under specific conditions: when customer concentration is higher, the customer's digital technology level is more advanced, and the supplier's financial flexibility is poorer. Collectively, these findings not only provide new evidence on the cross-firm transmission mechanism of climate risk but also offer important micro-level implications for achieving global climate governance goals.

在全球气候变化加剧的背景下，企业的“漂绿”行为正引起监管机构和学术界的广泛关注。本文考察了客户所面临的气候物理风险是否通过供应链传递，从而影响供应商对碳信息的选择性披露。利用2008 - 2023年中国a股上市公司的样本，我们发现了显著的结果：当供应商的主要客户面临更严重的气候物理风险时，供应商的碳漂绿水平显著降低。机制分析表明，这种抑制作用主要通过加强对碳减排核查的审查和供应商管理层未来期望的下降来实现。此外，在特定条件下，这种效应更强：当客户集中度越高时，客户的数字技术水平越先进，供应商的财务灵活性越差。总的来说，这些发现不仅为气候风险的跨企业传导机制提供了新的证据，而且为实现全球气候治理目标提供了重要的微观启示。

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引用次数: 0

Charting the ChatGPT landscape: Insights from academic discourse on Twitter 绘制ChatGPT景观：来自Twitter学术论述的见解

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-13 DOI: 10.1016/j.techsoc.2025.103199

Raphael Gonda , Jaehun Park

In the wake of the emergence of ChatGPT as a transformative tool, effective policies and regulations for its integration into research and education are vital. This paper addresses challenges in the rapidly evolving AI landscape by identifying key events and discussions. To that end, the current study extracted insights from the academic community that had come during the early stages of ChatGPT's adoption. A dataset of 84,706 sentences sourced from Twitter (X.com) users in the research and academic community and collected over the course of eight months between November 2022 and June 2023 were examined using topic modeling and aspect-based sentiment analysis to explore prevailing reactions and perceptions. Nine key themes such as academic writing, coding, and time-saving tasks were identified. Strong sentiments emerged around policy debates, the issue of data security, and evolving research practices. Further, a causal analysis was performed to identify discussions and events that had triggered shifts in public sentiment. Examples include temporary restrictions on generative AI, new institutional policies, and legislative efforts to ensure responsible AI integration. This paper provides a timeline-linked perspective on how the academic community, thus far, has responded to generative AI. The findings can inform pragmatic, context-sensitive policies that foster innovation while safeguarding academic values.

随着ChatGPT作为一种变革性工具的出现，将其整合到研究和教育中的有效政策和法规至关重要。本文通过确定关键事件和讨论来解决快速发展的人工智能领域的挑战。为此，当前的研究从ChatGPT采用的早期阶段的学术界中提取了一些见解。在2022年11月至2023年6月的8个月期间，研究人员使用主题建模和基于方面的情感分析对来自研究和学术界Twitter （X.com）用户的84,706个句子的数据集进行了检查，以探索普遍的反应和看法。他们确定了9个关键主题，如学术写作、编码和节省时间的任务。围绕政策辩论、数据安全问题和不断发展的研究实践，出现了强烈的情绪。此外，还进行了因果分析，以确定引发公众情绪转变的讨论和事件。例子包括对生成人工智能的临时限制、新的制度政策以及确保负责任的人工智能整合的立法努力。这篇论文提供了一个与时间轴相关的观点，说明到目前为止，学术界是如何回应生成式人工智能的。研究结果可以为务实的、对环境敏感的政策提供信息，这些政策可以在保护学术价值的同时促进创新。

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引用次数: 0

Entrepreneurial decision-making in the age of AI: Sector knowledge at the balance of intuition and analysis 人工智能时代的创业决策：直觉与分析平衡下的行业知识

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-13 DOI: 10.1016/j.techsoc.2025.103200

Matteo Cristofaro , Pier Luigi Giardino , Jeffrey Muldoon

Artificial intelligence (AI) is shaping entrepreneurial decision making today, increasingly informing opportunities recognition, assessment, and exploitation. Yet prior sector knowledge of entrepreneurs remains a fundamental pillar in these cognitive activities, providing the experiential schemas and contextual understanding that anchor entrepreneurial judgment. This study examines the interaction between two forces – AI-driven analysis and sector knowledge – and their influence on entrepreneurial outcomes, encompassing the recognition, assessment, and exploitation of opportunities. Using a controlled laboratory experiment with 124 entrepreneurs, we manipulate AI usage and measure prior sector knowledge to identify the independent and joint effects of these factors on entrepreneurial decision outcomes. Results show that AI increases the number of opportunities recognized and enhances the depth of opportunity assessment, exploitation, and contextual understanding. At the same time, AI reduces novelty in recognition and innovation in exploitation. Sector knowledge restores this creative dimension, enabling entrepreneurs to integrate intuitive insights with AI-supported deliberation. Entrepreneurs who combine AI and expertise achieve the most balanced outcomes, excelling simultaneously in novelty, depth, contextual understanding, and innovation. These results extend dual-process theories of cognition by demonstrating that prior knowledge conditions how AI reshapes the balance between intuitive and deliberative processes. Practically, the study highlights that the strategic value of AI in entrepreneurship lies not in substituting for human judgment but in complementing it with sector-specific expertise that anchors both originality and analytical rigor.

人工智能（AI）正在塑造当今的创业决策，越来越多地为机会识别、评估和利用提供信息。然而，企业家之前的行业知识仍然是这些认知活动的基本支柱，提供了锚定企业家判断的经验图式和背景理解。本研究考察了两种力量之间的相互作用——人工智能驱动的分析和行业知识——以及它们对创业成果的影响，包括对机会的识别、评估和利用。通过对124名企业家进行控制的实验室实验，我们操纵人工智能的使用并测量先前的行业知识，以确定这些因素对创业决策结果的独立和联合影响。结果表明，人工智能增加了识别机会的数量，增强了机会评估、利用和上下文理解的深度。与此同时，人工智能减少了识别上的新颖性和利用上的创新性。行业知识恢复了这一创造性维度，使企业家能够将直觉见解与人工智能支持的审议结合起来。将人工智能和专业知识结合起来的企业家可以获得最平衡的结果，同时在新颖性、深度、背景理解和创新方面表现出色。这些结果扩展了认知双过程理论，证明了先验知识决定了人工智能如何重塑直觉过程和审慎过程之间的平衡。实际上，该研究强调，人工智能在创业中的战略价值不在于取代人类的判断，而在于用特定行业的专业知识来补充人类的判断，这些专业知识既能巩固原创性，又能强化分析的严谨性。

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引用次数: 0

The calculator analogy: Epistemic virtues for using LLMs 计算器的类比：使用法学硕士的认知优势

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-13 DOI: 10.1016/j.techsoc.2025.103198

Cristina Voinea , Sebastian Porsdam Mann , Julian Savulescu , Brian D. Earp

Like the arrival of calculators in 1970s classrooms, large language models (LLMs) provoke both fears of intellectual deskilling and hopes of more efficient learning. In this paper we analyze the calculator analogy, arguing that while it is a useful starting point to understand the potential impact of LLMs in education, it is ultimately insufficient. We show where the analogy holds and, just as importantly, where its limitations reveal the unique pedagogical challenges posed by LLMs. These challenges arise from fundamental differences in how calculators and LLMs mediate learning, reflecting the distinct affordances of each technology. We argue that because of their affordances, realizing the educational potential of LLMs calls for cultivating epistemic virtues suited to human–AI interaction, such as patience, reflective engagement, or intellectual vigilance and humility. Equally, LLM design must actively foster these virtues through features like built-in prompts, feedback loops or reflective questions, to name just a few.

就像20世纪70年代计算器在教室里的出现一样，大型语言模型（llm）既引发了对智力技能丧失的恐惧，也引发了对更有效学习的希望。在本文中，我们分析了计算器的类比，认为虽然它是理解法学硕士在教育中的潜在影响的一个有用的起点，但它最终是不够的。我们展示了类比在哪里成立，同样重要的是，它的局限性揭示了法学硕士所带来的独特教学挑战。这些挑战来自计算器和法学硕士如何调解学习的根本差异，反映了每种技术的独特能力。我们认为，由于法学硕士的能力，实现法学硕士的教育潜力需要培养适合人类与人工智能互动的认知美德，如耐心，反思参与，或智力警惕和谦逊。同样，法学硕士设计必须通过内置提示、反馈循环或反思性问题等功能积极培养这些优点。

引用次数: 0

Artificial intelligence and digital Nationalism: A social media discourse analysis 人工智能与数字民族主义：社交媒体话语分析

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-12 DOI: 10.1016/j.techsoc.2025.103197

Yuhang Li , Chunhao Ma , Lisai Yu

Amid the rapid advancement of generative artificial intelligence (AI), public perceptions and emotional attitudes toward AI have become increasingly complex. Existing research has predominantly focused on micro-level aspects such as risk perception and technology acceptance, with limited attention to its connections with national identity and cultural sentiment. Adopting a nationalism perspective, this study employs computational social science methods to examine whether and how nationalistic expressions emerge in public discussions on the Chinese large language model DeepSeek on the social media platform Weibo. We developed a lexicon-based methodology for nationalism, structured around five dimensions: national pride, national revival, anti-foreign, techno-nationalism, and cultural nationalism. Leveraging large-scale text mining and the Analysis of Topic Model Networks (ANTMN), we identify two distinct discursive clusters named the Utility cluster and the Sociopolitical cluster, and further conducted a comparative analysis of how nationalism was discursively articulated within each cluster. The results show that discussions of DeepSeek prominently reflect nationalistic sentiment, with techno-nationalism emerging as the most salient dimension. Significant structural differences were observed between clusters in the ways nationalism is articulated. This study expands the theoretical scope of AI public opinion research, proposes a quantifiable framework for analyzing nationalism, and offers new empirical insights into the national symbolism and collective emotions embedded in contemporary AI technologies.

随着生成式人工智能（AI）的快速发展，公众对AI的认知和情感态度变得越来越复杂。现有的研究主要集中在微观层面，如风险感知和技术接受，对其与民族认同和文化情感的联系关注有限。本研究采用民族主义视角，运用计算社会科学方法，考察了微博上中文大语言模型DeepSeek的公共讨论中是否出现民族主义表达，以及民族主义表达是如何出现的。我们开发了一种基于词典的民族主义方法论，围绕五个维度构建：民族自豪感、民族复兴、反外国、技术民族主义和文化民族主义。利用大规模文本挖掘和主题模型网络分析（ANTMN），我们确定了两个不同的话语集群，即效用集群和社会政治集群，并进一步对民族主义在每个集群中的话语表达方式进行了比较分析。结果表明，DeepSeek的讨论突出地反映了民族主义情绪，其中技术民族主义成为最突出的维度。在民族主义表达方式的集群之间观察到显著的结构差异。本研究拓展了人工智能舆情研究的理论范围，提出了分析民族主义的可量化框架，并为当代人工智能技术中嵌入的国家象征主义和集体情感提供了新的实证见解。

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引用次数: 0

Cross-layer diffusion in patent networks: Forecasting innovation through enterprise–technology synergy 专利网络中的跨层扩散：通过企业-技术协同预测创新

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-12 DOI: 10.1016/j.techsoc.2025.103193

Dejian Yu , Aoqiu Shen , Wenjin Zuo

Firms face serious challenges in predicting the direction of innovation and identifying strategic partners in rapidly evolving technological fields, as traditional single-layer network models tend to ignore the synergies between inter-firm relationships and cross-field technology diffusion. To address this limitation, we combine the firm perspective with bi-layer patent networks to analyze synergistic technology diffusion links and predict the direction of innovation frontiers. We use a bi-layer diffusion-based patent network model that dynamically combines firm-level technology similarity networks with technology topic co-occurrence networks. By employing diffusion-based network analysis, the model quantifies the propagation of technological elements across organizational and disciplinary boundaries and identifies emerging technological directions and collaboration opportunities. The model is empirically validated on a dataset in the field of "Blockchain + Audit" to effectively predict future research directions and recommend potential technology development fields and partners for firms. This study provides valuable insights into the diffusion process of technology elements in bi-layer networks, and provides strategic insights to guide firms’ innovation decisions in dynamic technological environments.

由于传统的单层网络模型往往忽略了企业间关系和跨领域技术扩散之间的协同作用，企业在快速发展的技术领域预测创新方向和确定战略合作伙伴方面面临着严峻的挑战。为了解决这一局限性，我们将企业视角与双层专利网络相结合，分析了协同技术扩散链，并预测了创新前沿的方向。我们使用了一个基于双层扩散的专利网络模型，该模型动态地结合了企业层面的技术相似网络和技术主题共现网络。通过采用基于扩散的网络分析，该模型量化了技术元素跨组织和学科边界的传播，并确定了新兴的技术方向和合作机会。在“区块链+审计”领域的数据集上对模型进行了实证验证，有效地预测了未来的研究方向，为企业推荐了潜在的技术发展领域和合作伙伴。本研究对双层网络中技术要素的扩散过程提供了有价值的洞见，并为企业在动态技术环境下的创新决策提供了战略指导。

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引用次数: 0

Spatio-temporal shifts, driving mechanisms, and resilience dynamics: Unraveling the evolution of global innovation networks (2003–2023) 时空变迁、驱动机制与弹性动态：全球创新网络的演变（2003-2023）

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-11 DOI: 10.1016/j.techsoc.2025.103192

Jinbao Wen , Xiang Yu , Wei Yang

This study examines the spatio-temporal shifts, driving mechanisms, and structural resilience of Global Innovation Networks (GINs) by leveraging transnational patent data from the World Intellectual Property Organization (2003–2023). Through Social Network Analysis (SNA), Temporal Exponential Random Graph Models (TERGM), and Network Resilience Assessment Modeling (NRAM), we deliver a dynamic and multi-level analysis of GINs. Findings indicate that GINs maintain small-world properties and a stable core-periphery architecture, while experiencing a marked eastward shift in influential nodes. The traditional Western-centered core has expanded to incorporate emerging economies such as China, India, and South Korea, signaling a decentralization of global innovation activity. TERGM results reveal multi-level drivers: endogenous structures such as reciprocity and triadic closure guide self-organization; actor attributes exhibit asymmetric effects, where patent protection strength, political stability, and market size attract innovation inflows, whereas economic scale and trade promote outflows; exogenous proximities show cultural similarity fosters connections, while geographic and administrative distances act as barriers. Notably, knowledge distance's constraining role weakens when accounting for structural embeddedness. NRAM assessments show that GIN resilience has strengthened over time, with improved tolerance to both targeted and random disruptions. Yet systemic vulnerability persists through a limited set of core nations (including the US, China, and Germany)—whose failure may trigger broad instability. By incorporating endogenous dynamics, seldom-studied exogenous factors, and resilience into a unified framework, this research advances GIN theory and offers strategic insights for governance and global patent planning amid systemic uncertainties.

本文利用世界知识产权组织2003-2023年跨国专利数据，分析了全球创新网络的时空变迁、驱动机制和结构弹性。通过社会网络分析（SNA），时间指数随机图模型（TERGM）和网络弹性评估模型（NRAM），我们提供了一个动态和多层次的分析网络安全风险。研究结果表明，新兴经济体保持了小世界特征和稳定的核心-外围结构，但在有影响的节点上经历了明显的东移。传统的以西方为中心的核心已经扩展到包括中国、印度和韩国等新兴经济体，这标志着全球创新活动的分散化。TERGM结果揭示了多层次的驱动因素：互惠和三元闭包等内生结构引导自组织；行动者属性表现出不对称效应，专利保护力度、政治稳定性和市场规模吸引创新流入，而经济规模和贸易促进创新流出；外源性接近表明，文化相似性促进了联系，而地理和行政距离则成为障碍。值得注意的是，当考虑结构嵌入性时，知识距离的约束作用减弱。NRAM评估显示，随着时间的推移，GIN的恢复能力有所增强，对目标中断和随机中断的容忍度都有所提高。然而，系统性脆弱性在少数核心国家（包括美国、中国和德国）中依然存在——这些国家的失败可能引发广泛的不稳定。通过将内生动力学、很少被研究的外生因素和弹性纳入一个统一的框架，本研究推进了GIN理论，并为系统不确定性下的治理和全球专利规划提供了战略见解。

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引用次数: 0

Rethinking AI anthropomorphism: A holistic conceptualization and scale across AI systems and service contexts 重新思考人工智能拟人化：在人工智能系统和服务环境中的整体概念化和规模

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-11 DOI: 10.1016/j.techsoc.2025.103189

Yingwei (Wayne) Xu , Christina G. Chi , Dogan Gursoy , Ruiying (Raine) Cai

Building on Gestalt theory, this research introduces a holistic framework for defining artificial intelligence (AI) anthropomorphism, which guides the development of a parsimonious Scale of AI Anthropomorphism (SAIA). Through a systematic literature review, seven focus group discussions, and rigorous six-step scale development procedures, the research conceptualizes and validates a multidimensional framework that captures both external and internal human-like traits, including virtues and vices of anthropomorphic AI. Based on 2944 valid responses from five studies, a 40-item, six-dimensional parsimonious SAIA was developed, namely, human-like appearance, cognitive competency, adaptive capacity, social intelligence, morality, and fallibility. The scale demonstrated strong psychometric properties through comprehensive qualitative and quantitative validation. The SAIA serves as a robust tool for assessing the anthropomorphism of both tangible (e.g., robots) and intangible (e.g., chatbots) AI across normal service and service failure contexts.

在格式塔理论的基础上，本研究引入了一个定义人工智能（AI）拟人化的整体框架，该框架指导了人工智能拟人化简约量表（SAIA）的开发。通过系统的文献综述、七个焦点小组讨论和严格的六步规模开发程序，该研究概念化并验证了一个多维框架，该框架捕捉了外在和内在的类人特征，包括拟人化人工智能的优点和缺点。基于5项研究的2944份有效问卷，构建了一个包含40个项目的六维精简SAIA，即类人外貌、认知能力、适应能力、社会智力、道德和易错性。通过全面的定性和定量验证，该量表显示出较强的心理测量特性。SAIA是一个强大的工具，用于评估在正常服务和服务故障环境中有形（例如，机器人）和无形（例如，聊天机器人）人工智能的拟人化。

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引用次数: 0

Paradoxical adoption of consumer-facing service technologies: Investigating the role of mindset, learning paradox, and technological context 面向消费者的服务技术的矛盾采用：调查心态、学习悖论和技术背景的作用

IF 12.5 1区社会学 Q1 SOCIAL ISSUES

Technology in Society

Pub Date : 2025-12-11 DOI: 10.1016/j.techsoc.2025.103196

Xiaodi Liu , Kum Fai Yuen , Miao Su , Xueqin Wang

Human–technology interactions are often infused with paradoxes. This study adopts a paradox perspective to examine the adoption of consumer-facing service technologies (CFSTs). Specifically, we explore how experienced tension and paradoxical mindset influence users' adoption intention through two paradoxical perceptual pathways: functional perceptions (efficiency versus inefficiency) and emotional perceptions (enjoyment versus exhaustion), with learning paradox acting as a mediating mechanism (Study 1). Contextual variations between human- and technology-dominant settings are also examined (Study 2). Data were collected through an online survey of 350 participants and analysed using structural equation modelling. Study 1 shows that both experienced tension and paradoxical mindset shape the paradoxical perceptions. Efficiency and enjoyment positively affect adoption, whereas inefficiency and exhaustion exert non-significant or negative effects. Nevertheless, all perceptions influence adoption positively through the learning paradox, which shows that users transform contradictory experiences into adaptive engagement. Study 2 provides further evidence of contextual differences: paradoxical perceptions are more pronounced in technology-dominant service contexts (e.g., autonomous delivery robot) than in human-dominant contexts (e.g., self-service locker).

人类与技术的互动往往充满了悖论。本研究采用悖论视角考察面向消费者服务技术的采用情况。具体而言，我们通过功能感知（效率与低效率）和情感感知（享受与疲惫）两种矛盾的感知途径，探讨了体验性紧张和矛盾心态如何影响用户的采用意愿，其中学习悖论作为中介机制（研究1）。研究还检查了人类和技术主导环境之间的上下文差异（研究2）。数据是通过350名参与者的在线调查收集的，并使用结构方程模型进行分析。研究1表明，经历过的紧张和矛盾的心态共同塑造了矛盾的感知。效率和享受对收养产生积极影响，而效率低下和疲惫对收养产生不显著或负向影响。然而，所有的感知都通过学习悖论积极地影响采用，这表明用户将矛盾的体验转化为适应性参与。研究2提供了上下文差异的进一步证据：在以技术为主导的服务环境（例如，自动送货机器人）中，矛盾感知比在以人类为主导的环境（例如，自助储物柜）中更为明显。

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引用次数: 0