Journal of Open Innovation: Technology, Market, and Complexity最新文献_第2页

Impact of climate risk on clean water investments: Does crude oil act as a hedge? 气候风险对清洁水投资的影响：原油是否起到对冲作用？

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-23 DOI: 10.1016/j.joitmc.2025.100708

Mohammad Rakib Uddin Bhuiyan , Anupam Dutta , Ali Ahmed , Gazi Salah Uddin

Water investments play an increasingly important role in sustainable finance, yet their response to climate policy uncertainty (CPU) under different market conditions remains poorly understood. This study examines the regime-dependent influence of CPU on water equity performance using monthly data for the First Trust Water ETF (FIW) and the Invesco Global Water ETF (PIO) from 2007 to 2024. A Markov regime-switching VAR framework is employed to capture nonlinear dynamics that conventional linear models may overlook. The results reveal two distinct volatility regimes with contrasting CPU effects. In low-volatility periods, CPU is associated with higher returns, indicating that climate-policy developments can signal investment opportunities when markets are stable. During high-volatility periods, CPU exerts a negative influence, consistent with rising discount rates applied to long-term water-infrastructure cash flows. Regime persistence differs across ETFs: FIW exhibits frequent, short-lived transitions, whereas PIO displays more persistent states. A complementary DCC-GARCH analysis shows that crude oil provides a relatively cost-effective hedge for water portfolios, while technology ETFs offer substantially weaker hedging performance. Overall, the findings highlight the importance of regime-sensitive portfolio strategies for investors and emphasize that policymakers should consider prevailing market conditions when communicating climate initiatives. The study demonstrates that nonlinear models are essential for uncovering climate-finance linkages that linear approaches fail to detect.

水资源投资在可持续金融中发挥着越来越重要的作用，但人们对其在不同市场条件下对气候政策不确定性（CPU）的反应知之甚少。本研究利用2007年至2024年First Trust water ETF （FIW）和景顺全球水ETF （PIO）的月度数据，考察了CPU对水股权绩效的制度依赖影响。采用马尔可夫状态切换VAR框架捕捉传统线性模型可能忽略的非线性动力学。结果揭示了两种不同的波动机制与对比的CPU效应。在低波动性时期，CPU与较高的回报相关，这表明气候政策的发展可以在市场稳定时发出投资机会的信号。在高波动性时期，中央集权率会产生负面影响，这与长期水基础设施现金流的贴现率不断上升相一致。状态持久性在不同的etf之间是不同的：FIW显示频繁的、短暂的转换，而PIO显示更持久的状态。一项补充性的DCC-GARCH分析表明，原油为水投资组合提供了相对具有成本效益的对冲，而技术etf的对冲表现则弱得多。总体而言，研究结果强调了对制度敏感的投资组合策略对投资者的重要性，并强调政策制定者在沟通气候倡议时应考虑当前的市场状况。该研究表明，非线性模型对于揭示线性方法无法发现的气候融资联系至关重要。

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引用次数: 0

Technological capabilities for value creation in tech startups under turbulence 动荡下科技创业公司价值创造的技术能力

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-18 DOI: 10.1016/j.joitmc.2025.100704

Raditya Ardianwiliandri , Sandra Hasanefendic , Bart Bossink

This study examines how technological capabilities drive value creation in technology startups under turbulence. Using market and technological turbulence as proxies and survey data from 404 tech startups in the United Kingdom, Germany, and the Netherlands, the findings reveal that human capital development through expert talent acquisition and continuous technical training is more impactful than traditional R&D investment in fostering value creation. This study also highlights the strategic value of incremental innovation over radical innovation. Furthermore, market turbulence weakens the positive effect of technological capabilities on value creation, whereas technological turbulence has no significant moderating effect. This study advances theory by unpacking which specific technological capabilities matter for value creation and offer insights into how tech startups should prioritize capabilities under turbulence, contributing to the literature on strategic agility and innovation management in turbulent environments.

本研究探讨技术能力如何在动荡环境下驱动科技创业公司的价值创造。以市场和技术动荡为代理，并以英国、德国和荷兰的404家科技初创企业的调查数据为基础，研究结果表明，在促进价值创造方面，通过专业人才获取和持续技术培训进行人力资本开发比传统的研发投资更有效。本研究还强调了渐进式创新相对于突破性创新的战略价值。此外，市场动荡削弱了技术能力对价值创造的正向作用，而技术动荡没有显著的调节作用。本研究通过揭示哪些特定的技术能力对价值创造至关重要来推进理论，并为科技初创公司在动荡环境下如何优先考虑能力提供了见解，为动荡环境下的战略敏捷性和创新管理方面的文献做出了贡献。

引用次数: 0

Technological innovation system applicability in developing countries: Insights from Malaysia's gallium nitride technology development 技术创新体系在发展中国家的适用性：来自马来西亚氮化镓技术发展的启示

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-17 DOI: 10.1016/j.joitmc.2025.100700

Hoo Kooi Lim , Seuk Wai Phoong , Ahmad Shuhaimi , Ainin Sulaiman

This study examines the applicability and effectiveness of the Technological Innovation System (TIS) framework in developing countries, with a specific focus on Malaysia's semiconductor and electronics sector and its Gallium Nitride (GaN) technology development. Despite global recognition of GaN technology's potential, its integration within Malaysia's technological landscape poses challenges emblematic of broader issues faced by developing nations, including disparities in technological capabilities, limited access to resources, and weak commercialization pathways. The primary objective of this research is to explore the knowledge transfer mechanisms within Malaysia's GaN technology ecosystem, analyzing how knowledge is generated, shared, and applied among diverse actors through the lens of open innovation dynamics. Through semi-structured interviews with key stakeholders and document reviews, the study identifies critical enablers and barriers to effective knowledge transfer. Findings highlight the pivotal role of technology transfer, robust knowledge networks, open innovation partnerships, and learning and experimentation in driving GaN technology development and diffusion. The research reveals both significant strengths (strong government commitment, strategic international partnerships, dedicated research infrastructure) and notable weaknesses (limited private sector engagement, heavy dependence on foreign expertise, weak commercialization pathways) within Malaysia's GaN ecosystem. The study contributes to the broader discourse on TIS by offering insights into how developing countries can harness technological innovations to bridge the gap with developed counterparts, while providing a three-stage framework for closing the TIS gap in developing countries. The insights gained from Malaysia's GaN technology ecosystem provide a model for other developing nations seeking to leverage technological innovations for economic and social development.

本研究考察了技术创新系统（TIS）框架在发展中国家的适用性和有效性，特别关注马来西亚的半导体和电子行业及其氮化镓（GaN）技术的发展。尽管全球都认识到氮化镓技术的潜力，但它在马来西亚技术领域的整合带来了挑战，这是发展中国家面临的更广泛问题的象征，包括技术能力的差距、资源的有限获取和薄弱的商业化途径。本研究的主要目的是探索马来西亚氮化镓技术生态系统中的知识转移机制，通过开放创新动态的视角，分析知识如何在不同参与者之间产生、共享和应用。通过与主要利益相关者的半结构化访谈和文件审查，本研究确定了有效知识转移的关键推动因素和障碍。研究结果强调了技术转让、强大的知识网络、开放式创新伙伴关系以及学习和实验在推动氮化镓技术发展和传播方面的关键作用。该研究揭示了马来西亚氮化镓生态系统的显著优势（强有力的政府承诺、战略国际伙伴关系、专门的研究基础设施）和显著劣势（私营部门参与有限、严重依赖外国专业知识、商业化途径薄弱）。该研究为发展中国家如何利用技术创新弥合与发达国家的差距提供了见解，同时为缩小发展中国家的交通运输差距提供了一个三阶段框架，从而有助于更广泛地讨论交通运输问题。从马来西亚GaN技术生态系统中获得的见解为其他寻求利用技术创新促进经济和社会发展的发展中国家提供了一个模式。

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引用次数: 0

The role of innovation dynamics on sustainable development goals: Interaction patterns based on the Triple Helix model 创新动态对可持续发展目标的作用：基于三螺旋模型的交互模式

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-17 DOI: 10.1016/j.joitmc.2025.100705

Mahshid G. Zadegan, Sepehr Ghazinoory

Since the United Nations introduced the Sustainable Development Goals (SDGs) in 2015, nations worldwide have launched a range of initiatives aimed at achieving these targets. However, the uneven SDGs progress across countries have raised fundamental questions regarding the underlying factors influencing these disparities. This study aims to examine the role of innovation dynamics in explaining the variation in national performance concerning the SDGs. To this end, it operationalizes the conceptual framework of the Triple Helix model of innovation, which identifies the dynamic interaction among three key institutions (universities, government, and industry)as the driving force behind innovation processes. After reviewing the theoretical literature on innovation dynamics and addressing conceptual challenges in existing studies, this research provides a clear and practical definition of the concept, making it suitable for statistical analysis. Building on the theoretical underpinnings of the Triple Helix model, this study evaluates how institutional interaction influences countries’ SDG performance during 2019 and 2024. To test the main hypothesis, four hierarchical regression models were developed. The dependent variable was the variance in sustainable development performance across countries during the specified period, while the independent variables included standardized performance scores of governments, industry, and academia, as well as indicators of bilateral and trilateral institutional interactions. These were analyzed while controlling for economic, political, cultural, and natural conditions. The results reveal that trilateral collaboration among universities, industry, and government has a highly significant and positive impact on sustainable development performance, whereas the isolated effects of individual institutions were limited or statistically insignificant. In addition, bilateral government–industry collaboration showed an independently significant contribution. The findings underscore that innovation dynamics and the structure of institutional interaction play a critical role in determining national success in achieving SDGs. This study offers a structured framework for evaluating institutional collaboration at the national level, and highlights the Triple Helix model as a key framework for sustainable development policymaking.

自2015年联合国提出可持续发展目标（sdg）以来，世界各国发起了一系列旨在实现这些目标的举措。然而，各国在可持续发展目标方面的进展参差不齐，对影响这些差异的根本因素提出了根本性问题。本研究旨在探讨创新动态在解释可持续发展目标相关国家绩效差异中的作用。为此，本文运用了创新三重螺旋模型的概念框架，该模型将三个关键机构（大学、政府和行业）之间的动态互动确定为创新过程背后的驱动力。在回顾了有关创新动力学的理论文献并解决了现有研究中的概念挑战后，本研究提供了一个清晰实用的概念定义，使其适合于统计分析。在三螺旋模型的理论基础上，本研究评估了机构互动如何影响各国在2019年和2024年期间的可持续发展目标绩效。为了检验主要假设，我们建立了四个层次回归模型。因变量是各国在特定时期内可持续发展绩效的差异，而自变量包括政府、行业和学术界的标准化绩效得分，以及双边和三边机构互动的指标。这些都是在控制经济、政治、文化和自然条件的情况下进行分析的。结果表明，大学、产业和政府三方合作对可持续发展绩效具有显著的正向影响，而个别机构的孤立效应有限或统计不显著。此外，双边政府-产业合作也显示出独立的显著贡献。研究结果强调，创新动态和机构互动结构在决定国家能否成功实现可持续发展目标方面发挥着关键作用。本研究为评估国家层面的机构合作提供了一个结构化框架，并强调了三螺旋模型作为可持续发展政策制定的关键框架。

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引用次数: 0

Resistance intention towards mobile payment among Gen X: Evidence from an emerging country X世代对移动支付的抗拒意向：来自一个新兴国家的证据

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-12 DOI: 10.1016/j.joitmc.2025.100703

Saiful Islam , Muhaiminul Islam , Farjana Rahman , Md. Aftab Uddin

This study explores users' intentions to resist mobile payment (m-payment) adoption in the emerging economy, combining functional and psychological factors from the innovation resistance theory (IRT) and push, pull, and mooring (PPM) theory. Using a mixed research approach, data is collected from 400 respondents through a survey questionnaire, and from 10 participants in a focus group discussion (FGD). The partial least squares structural equation modeling (PLS-SEM) is used to test the hypotheses, and the results in FGD are summarized to analyze and cross-validate the empirical findings. Results indicate that, while psychological factors substantially affect users' intention to resist m-payment, functional factors have no significant impact. Perceived monetary value as a pull factor and switching costs and inertia as mooring factors also significantly influence users' intention to resist m-payment. Users’ intention to resist m-payment can lead to two possible outcomes: a complete rejection of m-payment and postponement of adoption, where personal innovativeness significantly moderates the second outcome. These findings have several theoretical and managerial implications for m-payment developers, policymakers, and other stakeholders in reducing resistance to m-payment adoption.

本研究结合创新阻力理论（IRT）和推拉系泊（PPM）理论的功能因素和心理因素，探讨了新兴经济体中用户抵制移动支付（m-payment）采用的意愿。采用混合研究方法，通过调查问卷从400名受访者中收集数据，并从10名焦点小组讨论（FGD）参与者中收集数据。采用偏最小二乘结构方程模型（PLS-SEM）对假设进行检验，并对烟气脱硫的结果进行总结分析，对实证结果进行交叉验证。结果表明，心理因素对用户抵制移动支付意愿有显著影响，功能因素对用户抵制移动支付意愿的影响不显著。感知货币价值作为拉动因素，转换成本和惯性作为停泊因素也显著影响用户抵制移动支付的意愿。用户抵制移动支付的意图可能导致两种可能的结果：完全拒绝移动支付和推迟采用移动支付，其中个人创新显著地缓和了第二种结果。这些发现对移动支付开发者、政策制定者和其他利益相关者在减少对移动支付采用的阻力方面具有一些理论和管理意义。

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引用次数: 0

Consumer intensity drivers and adaptive marketing agility: Empirical evidence of mediating spillovers and co-evolution with moderating algorithmic amplification 消费者强度驱动因素与适应性营销敏捷性：中介溢出和协同进化与调节算法放大的实证证据

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-09 DOI: 10.1016/j.joitmc.2025.100701

Rohit Bansal , Y. Suryanarayana Murthy , Nishita Pruthi , Aulia Luqman Aziz , Arfendo Propheto

The consumer intensity drivers, such as co-creation, digital brand passion, zealotry orientation, micro-community evangelism, gamification appeal, narrative identity alignment, and sustainability-related brand love are crucial drivers of marketing in the rapidly changing digital ecosystems. This paper empirically investigates the role of these antecedents in increasing the adaptive marketing agility via the two mediation mechanisms of open innovation spillovers and consumer-brand co-evolution, and further hypothesizes the moderating effect of algorithmic social amplification. It used a survey-based design where the responses were collected among the consumers who were participating in online brand communities. Through structural equation modelling (SEM), the analysis established that consumer intensity drivers have a significant impact on the mediators which in turn have a positive impact on adaptive marketing agility. In addition, the mediated pathways were found to be moderated by algorithmic amplification, which enhanced the process of translation of spillovers and co-evolution into agility outcomes. Theoretically, Service-Dominant Logic and Dynamic Capabilities Theory are extended in the study by showing how the intensity of consumers can be translated into organizational adaptability in the context of algorithmically mediated environments. In practice, the results will give managers a strategy to take advantage of consumer intensity drivers, nurture innovation spill-overs, build co-evolutionary relationships, and be platform-congruent to maintain agility in competitive digital markets.

消费者强度驱动因素，如共同创造、数字品牌激情、狂热导向、微社区布道、游戏化吸引力、叙事身份一致性和与可持续性相关的品牌热爱，是快速变化的数字生态系统中营销的关键驱动因素。本文通过开放式创新溢出和消费者-品牌协同进化两种中介机制实证考察了这些前因变量在提高适应性营销敏捷性中的作用，并进一步假设了算法社会放大的调节作用。它采用了基于调查的设计，从参与在线品牌社区的消费者中收集反馈。通过结构方程模型（SEM），分析确定消费者强度驱动因素对中介因素有显著影响，中介因素反过来对适应性营销敏捷性有积极影响。此外，研究还发现，这些中介途径被算法放大所调节，从而增强了溢出效应和协同进化转化为敏捷性结果的过程。从理论上讲，服务主导逻辑和动态能力理论在研究中得到扩展，展示了消费者的强度如何在算法介导的环境中转化为组织适应性。在实践中，研究结果将为管理者提供一种策略，以利用消费者强度驱动因素，培育创新溢出效应，建立共同进化关系，并与平台一致，以在竞争激烈的数字市场中保持敏捷性。

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引用次数: 0

Factors influencing circular economy business model adoption: Evidence from NCA and PLS-SEM 影响循环经济商业模式采用的因素：来自NCA和PLS-SEM的证据

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-04 DOI: 10.1016/j.joitmc.2025.100697

Noor Ul Hadi , M. Imran Khan

Several studies have examined the factors influencing circular economy business model adoption. However, understanding remains limited from the perspective of Necessary Condition Analysis, leaving a notable gap in circular economy research that explicitly applies Necessary Condition Analysis to uncover the hidden conditions necessary for the successful implementation of circular strategies in business organizations. Drawing on the Resource-Based View and Stakeholder Theory, this study combines Partial Least Squares Structural Equation Modeling (PLS-SEM) with Necessary Condition Analysis (NCA) to distinguish “should-have” associations from “must-have” thresholds. Using survey data from 209 marble manufacturing firms, we examine eight factors: circular human resource management, circular entrepreneurship, circular marketing, circular financing, circular manufacturing technologies, circular-oriented workplace culture, circular open innovation, and government support. The integrated results show that several internal capabilities and one institutional factor are both necessary and sufficient (circular human resource management, circular entrepreneurship, circular manufacturing technologies, circular-oriented culture, government support), circular open innovation is necessary-only, circular financing is sufficient-only, and circular marketing is neither in this context. NCA bottleneck thresholds provide actionable benchmarks indicating minimum levels each necessary condition must meet at different target levels of CEBM adoption. Methodologically, the study demonstrates how a dual-logic approach reveals hidden constraints that sufficiency models alone overlook. Theoretically, it reframes selected capabilities and institutional enablers as foundational preconditions rather than merely supportive resources. Practically, it offers a sequenced roadmap: meet the lowest unmet threshold first, then deploy sufficiency levers to scale circular outcomes supporting managers and policymakers seeking credible pathways toward a regenerative, circular economy.

一些研究考察了影响采用循环经济商业模式的因素。然而，从必要条件分析的角度来看，对循环经济的理解仍然有限，在循环经济研究中，明确运用必要条件分析来揭示企业组织成功实施循环战略所必需的隐藏条件的研究仍然存在明显的空白。基于资源基础观点和利益相关者理论，本研究将偏最小二乘结构方程模型（PLS-SEM）与必要条件分析（NCA）相结合，以区分“应该拥有”与“必须拥有”的关联阈值。利用209家大理石制造企业的调查数据，我们考察了8个因素：循环人力资源管理、循环创业、循环营销、循环融资、循环制造技术、循环导向的工作场所文化、循环开放式创新和政府支持。综合研究结果表明，在此背景下，几个内部能力和一个制度因素（循环人力资源管理、循环创业、循环制造技术、循环导向文化、政府支持）是必要和充分的，循环开放式创新是必要的，循环融资是充分的，而循环营销两者都不是。NCA瓶颈阈值提供了可操作的基准，表明在CEBM采用的不同目标级别上，每个必要条件必须满足的最低水平。在方法上，该研究展示了双重逻辑方法如何揭示了单独的充分性模型所忽略的隐藏约束。从理论上讲，它将选定的能力和制度促成因素重新定义为基础先决条件，而不仅仅是支持性资源。实际上，它提供了一个有序的路线图：首先满足最低的未满足阈值，然后部署充足的杠杆来扩大循环成果，支持管理者和政策制定者寻求通向再生循环经济的可靠途径。

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引用次数: 0

Economic policy uncertainty and artificial intelligence (AI) innovation: A cross-country analysis 经济政策不确定性与人工智能（AI）创新：一个跨国分析

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-04 DOI: 10.1016/j.joitmc.2025.100698

Tuan-Hock Ng , Chun-Teck Lye , Ying-San Lim , Ying-Zhee Lim

As nations vie for a strategic edge in artificial intelligence (AI), sustaining innovation has shifted from priority to imperative. Yet, economic policy uncertainty (EPU) presents a particularly salient threat, capable of derailing the investment and research and development (R&D) activities that underpin AI breakthroughs. Against this backdrop, this study examines the relationship between EPU and AI innovation in 34 countries from 2017 to 2023, using quantile regression to delineate heterogeneous effects across the innovation distribution. The results indicate that EPU exerts a significantly negative effect on AI innovation, with disproportionately stronger adverse effects observed in countries at lower innovation quantiles. These results remain robust across pooled ordinary least squares (OLS), fixed and random effects models, generalised method of moments (GMM), and placebo tests. As one of the earliest empirical investigations into the asymmetric effects of EPU on AI innovation, this study contributes novel insights to the open innovation literature and ongoing policy dialogue. Collectively, the findings highlight the importance of stable and predictable policy environments in bolstering national innovation ecosystems and narrowing the global AI innovation gap.

随着各国争夺人工智能（AI）的战略优势，持续创新已经从优先事项转变为当务之急。然而，经济政策的不确定性（EPU）提出了一个特别突出的威胁，能够破坏支撑人工智能突破的投资和研发（R&；D）活动。在此背景下，本研究考察了2017年至2023年34个国家的EPU与人工智能创新之间的关系，使用分位数回归来描述创新分布中的异质性效应。结果表明，EPU对人工智能创新具有显著的负面影响，在创新分位数较低的国家中，这种负面影响尤为明显。这些结果在合并普通最小二乘（OLS）、固定和随机效应模型、广义矩量法（GMM）和安慰剂测试中都保持稳健。作为最早研究EPU对人工智能创新不对称效应的实证研究之一，本研究为开放式创新文献和持续的政策对话提供了新的见解。总的来说，研究结果强调了稳定和可预测的政策环境对于加强国家创新生态系统和缩小全球人工智能创新差距的重要性。

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引用次数: 0

Artificial intelligence shock in manufacturing: A threat or an opportunity for South Africa’s Labour Market? 制造业的人工智能冲击：对南非劳动力市场是威胁还是机遇？

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-02 DOI: 10.1016/j.joitmc.2025.100696

Fiyinfoluwa Giwa, Sin-Yu Ho

Existing research on artificial intelligence (AI) has focused predominantly on its productivity effects in advanced economies, leaving a critical gap in understanding its labour market consequences in developing nations. This study addresses this gap by examining how AI shocks shape employment dynamics in South Africa’s manufacturing sector—a pivotal industry characterised by structural unemployment and uneven technological readiness. Using quarterly data (2014–2024) and a Structural Vector Autoregression (SVAR) model, we find that a positive AI shock triggers an immediate and statistically significant contraction in employment. This negative effect, consistent with task-based theory, persists over the short to medium term, with variance decomposition showing that AI shocks account for nearly 30 % of forecast-error variance in manufacturing employment over a ten-quarter horizon. These findings demonstrate the distinct risks of automation in developing economies and underscore the urgent need for policies that foster human-AI complementarity, such as reskilling and incentives for labour-augmenting technologies. Beyond its employment effects, the study contributes to open-innovation research by demonstrating how AI diffusion shapes technological learning and adaptive industrial capacity in South Africa’s manufacturing sector. The results provide policy guidance for aligning AI adoption with labour market priorities to support inclusive and innovation-oriented industrial growth.

关于人工智能（AI）的现有研究主要集中在其对发达经济体的生产率影响上，在理解其对发展中国家劳动力市场的影响方面存在重大空白。本研究通过研究人工智能冲击如何影响南非制造业的就业动态来解决这一差距，制造业是一个以结构性失业和技术准备不均衡为特征的关键行业。使用季度数据（2014-2024年）和结构向量自回归（SVAR）模型，我们发现积极的人工智能冲击触发了就业的立即和统计上显着的收缩。这种负面影响与基于任务的理论一致，在中短期内持续存在，方差分解显示，人工智能冲击占制造业就业预测误差方差的近30% %，超过10个季度。这些发现表明，自动化在发展中经济体中存在明显的风险，并强调迫切需要制定促进人类与人工智能互补的政策，例如再培训和激励劳动力增加技术。除了对就业的影响外，该研究还通过展示人工智能扩散如何影响南非制造业的技术学习和适应性工业能力，为开放式创新研究做出了贡献。研究结果为将人工智能应用与劳动力市场重点结合起来，支持包容性和创新型工业增长提供了政策指导。

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引用次数: 0

Hybrid early warning system: Integration of Z-score and machine learning for predicting financial performance of IRB in Indonesia 混合预警系统：整合Z-score和机器学习来预测印尼IRB的财务表现

Q1 Economics, Econometrics and Finance

Journal of Open Innovation: Technology, Market, and Complexity

Pub Date : 2025-12-02 DOI: 10.1016/j.joitmc.2025.100694

Imron Mawardi , Andi Estetiono , Tika Widiastuti , Anidah Robani , Muhammad Ubaidillah Al Mustofa , Faris Kurnia Hakim , Qothrotunnidha Almaulidiyah , Eka Puspa Dewi

Purpose

This study aims to develop a hybrid Early Warning System (EWS) that integrates the traditional Z-score method with machine learning algorithms to accurately predict the financial performance and potential failure of IRB in Indonesia.

Design/methodology/approach

The study employs a data science–driven approach using monthly financial and macroeconomic data from 2011 to 2024. A total of 21 independent variables were tested against the Z-score as the dependent variable. Various machine learning models were compared using AutoML (TPOT and LazyRegressor), and model validation was conducted through statistical testing, cross-validation, robustness checks, and feature importance analysis.

Findings

The Extra Trees algorithm emerged as the most accurate and robust predictive model, achieving an R² of 0.95 and the lowest Mean Absolute Error (MAE) of 0.0676. Key predictive variables identified include Non-Performing Financing (NPF), inflation (INF), and istishna'-based Financing. The model successfully anticipates financial distress up to two years in advance and enables the categorisation of IRB conditions into Stable, Vulnerable, and Unstable using Z-score thresholds.

Originality/value

This is the first study to integrate Z-score with advanced machine learning techniques within a hybrid EWS framework tailored to IRB. It offers a novel predictive model that enhances early risk detection and supports regulatory decision-making.

Limitation

The study focuses exclusively on IRB in Indonesia and may require recalibration for broader application in different financial systems or geographies. Reporting accuracy and completeness may also limit the use of secondary data.

Practical implications

The hybrid model provides actionable insights for regulators (OJK, LPS) and financial institutions to monitor IRB's health proactively. It strengthens risk mitigation strategies and contributes to the long-term resilience of the Islamic microfinance sector.

本研究旨在开发一种混合预警系统（EWS），该系统将传统的Z-score方法与机器学习算法相结合，以准确预测印度尼西亚IRB的财务绩效和潜在失败。设计/方法/方法本研究采用数据科学驱动的方法，使用2011年至2024年的月度金融和宏观经济数据。以z分数为因变量，共检验21个自变量。使用AutoML （TPOT和LazyRegressor）对各种机器学习模型进行比较，并通过统计检验、交叉验证、鲁棒性检查和特征重要性分析对模型进行验证。Extra Trees算法是最准确、最稳健的预测模型，R²为0.95，平均绝对误差（MAE）最低，为0.0676。确定的关键预测变量包括不良融资（NPF）、通货膨胀（INF）和基于银行的融资。该模型成功地预测了长达两年的财务困境，并使用z分数阈值将IRB条件分为稳定、脆弱和不稳定。这是第一个将Z-score与先进的机器学习技术集成到为IRB量身定制的混合EWS框架中的研究。它提供了一种新的预测模型，可以增强早期风险检测并支持监管决策。局限性：本研究仅关注印度尼西亚的IRB，可能需要重新调整以更广泛地应用于不同的金融体系或地区。报告的准确性和完整性也可能限制二手数据的使用。实际意义混合模型为监管机构（OJK， LPS）和金融机构主动监测IRB的健康状况提供了可操作的见解。它加强了风险缓解战略，并有助于伊斯兰小额信贷部门的长期复原力。

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