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It is found that previous studies only focus on how digital transformation contributes to individual firms’ green innovation performance while ignoring the important role that it plays in the spillover and diffusion of green innovations among peer firms. Therefore, this study aims to investigate the influence of focal firms’ digital transformation on the spillover of green innovation among peer firms in heavily polluting industries mediated by environmental, social and governance (ESG) performance and agency conflict. Further, this study is also expected to explore the effects of digital transformation’s green innovation spillover.

This study chooses 6,438 A-share heavily polluting listed firms in the stock exchanges based in Shanghai and Shenzhen in China during 2010–2020 as samples and tests the hypothesis with ordinary least squares (OLS) regression. Results prove to be robust to a battery of robustness analyses the authors performed to take care of endogeneity.

The results show that the focal firm’s digital transformation may trigger their peer firms’ green innovation spillover and prompt them to engage in green innovation activities actively. The mechanism test shows that peer firms’ ESG performance and agency conflict mediate the influence path between digital transformation and peer firms’ green innovation spillover. Finally, among heavily polluting firms with high industry competition and large scale, digital transformation’s green innovation spillover effects are more significant in conventional energy-based source control, end-of-pipe treatment and substantive green innovation.

This study is possible to provide a potential driving mechanism of green innovation spillovers. The findings lay a sound foundation for future research, providing important theoretical support and practical insights for digital transformation to empower heavily polluting industries to achieve green transformation and low-carbon development.

With the rapid development of society, major emergencies occur frequently, posing a serious threat to people’s lives and property. This study focuses on the chain reaction of major emergencies, aiming to improve the overall situational awareness and capabilities for managing major emergencies in complex scenarios.

We proposed an information support framework for the chain reaction of major emergencies based on a causality eventic graph (CEG). The framework consists of three modules: the data layer, the analysis layer and the service layer. The data layer focuses on the perception and collection of major emergency information. The analysis layer includes key components such as causality recognition, causality extraction, event fusion and generalization. In this layer, we developed several deep learning (DL)-based models using a joint extraction approach to obtain causal pairs. The service layer depicts the event evolution logic from both industry and public perspectives.

The empirical study has demonstrated the feasibility and effectiveness of the proposed information support framework. First, the BERT-BiLSTM-CRF model achieved the best performance in the causality extraction task. Second, the SBERT model was found to be more suitable for event fusion. Third, the analysis results of CEG revealed that the impact of pandemics on industries in turn affects other industries as well as people’s livelihoods and vice versa. The framework shows a better information support, discovery and reasoning effect. This study, however, still has several limitations. We focused primarily on causal logic and did not fully explore other logical relationships. In future work, we will incorporate more logical relations to further refine the eventic graph (EG).

This study proposes a novel information support framework for the chain reaction of major emergencies, leveraging CEG to provide targeted and hierarchical information to industry and public stakeholders. It solves several problems in the emergency management of major emergencies.

The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors.

This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction.

The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms.

Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

The objective of this article is to analyze the mediating role of innovation capability—both radical and incremental—between technological turbulence and digital innovation ecosystem performance, considering the impact of cross-organizational knowledge sabotage. Despite the enthusiasm surrounding digitization, the high failure rate (80%) of digital transformation projects has received limited attention. This alarming statistic indicates a potential rise in opportunistic behaviors within organizations. We hypothesize that employees seeking to reduce the risk of being displaced by digital technologies, may not only hide knowledge, as previously observed, but also engage in knowledge sabotage by disseminating inaccurate information during the co-creation of digital innovations within the digital innovation ecosystem.

The study employed structural equation modeling to examine moderated mediation using survey data collected from 148 firms, mainly from sectors of high to medium levels of digital intensity.

The most significant finding indicates that cross-organizational knowledge sabotage considerably reduces the only mediating effect, namely that of incremental innovation capability.

Our study presents a novel perspective by investigating the phenomenon of cross-organizational knowledge sabotage. Unlike prior research, which primarily identified the existence of knowledge hiding, our findings suggest that employees are not only willing to withhold information but also to disseminate inaccurate information to external partners. Consequently, our research extends the boundaries of the existing knowledge field by demonstrating that cross-organizational knowledge sabotage has repercussions that extend beyond intra-organizational impacts, as previously recognized. It also adversely affects the outcomes of collaborative work within the digital innovation ecosystem.

Despite the evident link between digitalisation and sustainability, many organisations have these two strategies operating on a parallel basis and not in a coordinated manner. Hence the objective of this work, which consists of proposing a model to analyse the connection existing between both strategies within the business environment, additionally relating them to innovation and Corporate Social Responsibility (CSR).

With that aim, the results of a survey answered by 98 managers of Spanish enterprises are examined using the PLS software, especially suited for the study of structural equations like the one put forward in this paper.

The conclusions drawn suggest that the most innovative firms are indeed the ones that invest to a greater extent in digitalisation and sustainability. It was also possible for us to verify that digitalisation exerts a positive influence on sustainability and that both the latter and digitalisation directly relate to CSR strategies. However, innovation as such does not constitute a requirement for CSR; instead, these Social Responsibility actions will take place when innovations rely on digitalisation and sustainability strategies.

Until now, most studies have addressed digitalisation and sustainability independently, with contradictory theoretical approaches in the literature about these two topics and a paucity of empirical results about the link between digitalisation and sustainability. The present study clarifies the relationships between sustainability and digitalisation, also relating them to innovation and CSR in the business environment.

This paper expects to analyze the connection between occupational stigma and job meaningfulness among Chinese takeaway riders, the mediating role of occupational identity and relative deprivation, and the moderating effect of job stress based on resource conservation theory.

The sample was derived from 371 takeaway riders across China. PLS-SEM was mainly utilized for the data analysis.

The findings of the study indicated a significant negative correlation between occupational stigma and job meaningfulness. Furthermore, it is worth noting that relative deprivation and occupational identity served as mediators and masks, respectively, in the relationship between occupational stigma and job meaningfulness. Furthermore, job stress amplifies the association between occupational stigma and occupational identity. Additionally, job stress diminishes the connection between occupational stigma and relative deprivation.

This study proposes a positive correlation between occupational stigma and occupational identity in the Chinese context. It also enriches the empirical research based on resource conservation theory. Furthermore, it holds practical implications for takeaway riders in China, offering insights to bolster their job meaningfulness.

The research aims to explore the dynamic relationship between digital service innovation (DSI), artificial intelligence (AI) and business performance (BPer) in service-based models with a focus on how AI-enhanced insights from service use and customer feedback can strengthen business strategies. The aims are to show that DSI and AI are key to driving growth and efficiency in the digital economy and to underscore AI’s role in utilizing contextual data to improve decision-making and business outcomes.

The study uses general structural equation modeling to analyze Spanish manufacturing firms, focusing on medium-sized enterprises and including both business-to-business and business-to-consumer orientations. Data are drawn from the Iberian Balance Analysis System [Sistema de Análisis de Balances Ibéricos (SABI)] database, complemented by a Qualtrics survey to assess the integration of AI in decision-making processes. The methodology is designed to evaluate the interplay between DSI, AI and BPer, with the aim of identifying actionable insights for service-based business orientations.

The study clarifies the relationships between DSI, AI and BPer, providing new theoretical and empirical insights. The findings confirm DSI's direct positive impact on performance and suggest AI’s nuanced mediating role, emphasizing the need for strategic DSI-AI integration in manufacturing firms for enhanced performance.

The research explains the synergistic bond between DSI and AI in boosting BPer and discovering how by-product data can be transformed into strategic insights.

This study advises manufacturing sector leaders to integrate DSI and AI for enhanced performance and competitive advantage, emphasizing the value of high-quality, contextual data for AI learning and decision-making.

Researchers will observe that the study confirms the positive impact of DSI on BPer, while also highlighting the significant role of AI in enhancing this effect.

This study aims to examine how dynamic pricing impacts customer perceptions of restaurants and sentiment toward prices via online reputation metrics. In addition, to deepen the debate on dynamic pricing, a novel definition is drawn by exploring the specific forms of discrimination that can manifest in different industries.

Leveraging a comprehensive data set of restaurant reviews sourced from TripAdvisor, the study focuses on restaurants affiliated with one of the largest groups of restaurants in Spain. We used a quasi-experimental method (difference-in-differences), to study how dynamic pricing strategies influence customers’ perceptions of value based on numerical ratings. Meanwhile, we used a Bidirectional Encoder Representations from Transformers model on the textual component of reviews to dissect the emotional nuances of dynamic pricing.

Results did not reveal a causal impact of dynamic pricing strategies on customers’ perceptions. Moreover, the sentiment analysis shows no heightened negative view after introducing dynamic pricing in restaurants compared to the control group. Contrary to what previous literature suggests, our findings indicate that implementing dynamic pricing does not adversely affect customers’ perceptions or sentiments regarding prices in restaurants.

The quasi-experimental setting of the study presents inherent challenges in establishing causality that require further investigation using controlled experimental settings. Nevertheless, our study reveals that restaurant customers do not perceive dynamic pricing as unfair. This finding is critical for restaurant managers when considering the implementation of dynamic pricing and revenue management strategies. In addition, our study highlights the importance of considering not only numerical ratings but customer sentiment analysis as well. This more holistic approach to assessing the impact of pricing strategies can give restaurant managers a deeper understanding of customer reactions. In addition, a more rigorous definition of dynamic pricing is provided, clarifying its nature and its distinction in using different price discrimination.

This study contributes to the evolving understanding of dynamic pricing strategies’ impact on customers’ perceptions and sentiments in the restaurant industry. It aims to fill the gap in understanding customer reactions to algorithmically determined prices (via revenue management systems such as DynamEat) in this industry. The combination of causal inference and sentiment analysis offers a novel perspective, shedding light on the nuanced connections between dynamic pricing implementation and customers’ emotions.

本研究考察动态定价如何通过在线声誉指标影响顾客对餐厅的感知和对价格的情绪。此外, 为了深化对动态定价的讨论, 通过探索不同行业中可能表现出的具体歧视形式, 提出了一个新的定义。

利用从TripAdvisor获取的餐厅评论的全面数据集, 研究聚焦于与西班牙最大的餐厅集团之一相关联的餐厅。我们采用了准实验方法（差异中的差异）, 研究动态定价策略如何根据数值评分影响顾客对价值的感知。同时, 我们运用BERT模型对评论的文本成分进行分析, 以解析动态定价的情感细微差别。

结果没有揭示动态定价策略对顾客感知产生因果影响。此外, 情绪分析显示, 在餐厅引入动态定价后, 与对照组相比, 没有增加消极观点。与以往文献所述相反, 我们的发现表明, 实施动态定价并不会对顾客对价格的感知或情绪产生负面影响。

研究的准实验设置存在确立因果关系的固有挑战, 需要通过控制实验设置进一步调查。尽管如此, 我们的研究揭示了餐厅顾客不认为动态定价不公平。这一发现对餐厅经理在考虑实施动态定价和收入管理策略时至关重要。此外, 我们的研究强调, 考虑顾客情绪分析和数值评分的重要性。这种更全面的方法评估定价策略的影响, 可以让餐厅经理更深入地理解顾客反应。此外, 提供了一个更严格的动态定价定义, 澄清了其性质及其在使用不同价格歧视中的区别。

本研究对于理解动态定价策略对餐厅行业顾客感知和情绪影响的不断发展有所贡献。它旨在填补对客户对算法确定的价格（通过收入管理系统（RMS）例如DynamEat）在此行业中反应的理解空白。因果推断与情绪分析的结合提供了新的视角, 揭示了动态定价实施与顾客情绪之间微妙的联系。

Este estudio examina cómo la fijación dinámica de precios impacta en las percepciones de los clientes de los restaurantes y en el sentimiento hacia los precios a través de métricas de reputación en línea. Además, para profundizar en el debate sobre la fijación dinámica de precios, se propone una definición novedosa explorando las formas específicas de discriminación que pueden manifestarse en diferentes industrias.

Utilizando un conjunto de datos exhaustivo de reseñas de restaurantes obtenidas de TripAdvisor, el estudio se centra en los restaurantes afiliados a uno de los mayores grupos de restaurantes en España. Empleamos un método cuasiexperimental (diferencias en diferencias) para estudiar cómo las estrategias de precios dinámicos influyen en las percepciones de valor de los clientes basándonos en las calificaciones numéricas. Mientras tanto, empleamos un modelo BERT en el componente textual de las reseñas para desentrañar los matices emocionales de la fijación dinámica de precios.

Los resultados no revelaron un impacto causal de las estrategias de precios dinámicos en las percepciones de los clientes. Además, el análisis de sentimiento no muestra una visión negativa aumentada después de introducir la fijación dinámica de precios en los restaurantes en comparación con el grupo de control. Contrariamente a lo que sugiere la literatura previa, nuestros hallazgos indican que la implementación de precios dinámicos no afecta negativamente las percepciones o los sentimientos de los clientes respecto a los precios en los restaurantes.

La configuración cuasiexperimental del estudio presenta desafíos inherentes para establecer la causalidad que requieren una investigación más profunda utilizando entornos experimentales controlados. Sin embargo, nuestro estudio revela que los clientes de restaurantes no perciben la fijación de precios dinámica como injusta. Este hallazgo es crítico para los gerentes de restaurantes al considerar la implementación de la fijación de precios dinámica y estrategias de gestión de ingresos. Además, nuestro estudio resalta la importancia de considerar no solo las calificaciones numéricas sino también el análisis del sentimiento del cliente. Este enfoque más holístico para evaluar el impacto de las estrategias de precios puede dar a los gerentes de restaurantes una comprensión más profunda de las reacciones de los clientes. Además, se proporciona una definición de fijación de precios dinámica más rigurosa, aclarando su naturaleza y su distinción en el uso de diferentes discriminaciones de precios.

Este estudio contribuye a la comprensión en evolución del impacto de las estrategias de fijación de precios dinámicos en las percepciones y sentimientos de los clientes en la industria restaurantera. Su objetivo es llenar el vacío en la comprensión de las reacciones de los clientes a los precios determinados algorítmicamente (a través de sistemas de gestión de ingresos (RMS) como DynamEat) en esta industria. La combinación de inferencia causal y análisis de sentimientos ofrece una perspectiva novedosa, arrojando luz sobre las conexiones matizadas entre la implementación de la fijación de precios dinámicos y las emociones de los clientes.

Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study aims to establish a Greenness of Construction Technologies (GCT) evaluation model using the method of analytic network process (ANP).

Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study establishes a GCT evaluation model using the method of ANP.

Among the four formwork technologies of plastic, steel, plywood and wooden formwork, the plastic formwork exhibits the best performance in terms of environmental friendliness, economic and social effects, while facing issues like material wastage and low static strength. The results align with practical observations which validates the model’s applicability.

This research contributes to the field by introducing the concept of greenness into construction technology evaluation for the first time. The establishment of the GCT evaluation model promotes the adoption of green construction technologies and advances sustainable practices in the construction industry.

The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina.

Using Python, the models were assessed on a data set of 1,512 property transactions with mean squared error, coefficient of determination, mean absolute error and root mean squared error as metrics. The study also conducts variable importance test.

Upon preprocessing and standardization of the data, the models were trained and tested, with the decision tree model producing the best performance. The variable importance test found the distance from central business district and distance to the road leading to central business district as the most relevant drivers of housing prices across all models, with the exception of support vector machine model, which showed minimal importance for all variables.

To the best of the author’s knowledge, the originality of this research rests in its methodological approach and emphasis on Prishtina's real estate market, which has never been studied in this context, and its findings may be generalizable to comparable transitional economies with booming real estate sector like Kosovo.