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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.

The link between interlocking directors and mergers and acquisitions (M&A) efficiency has been analyzed in an information asymmetry environment. Despite an abundance of evidence highlighting that interlocking directors do contribute to M&A efficiency in an acquirer-target binary relationship, the target is embedded in a complex network of supplier-customer relationships, which implies that the acquirer needs to consider the value of suppliers, distributors and retailers in the target’s supply chain in improving M&A efficiency. Through the lenses of acquirer-target multivariate relationships, this paper aims to examine how directors with supply chain experience (DSCs) act as heterogeneous network pipes to affect M&A efficiency.

Using a sample of 311 A-share listed firms on the Shanghai and Shenzhen stock exchanges in China during 2011–2020, this paper investigates the relationship between DSCs and M&A efficiency by using ordinary least squares (OLS) regression.

Through empirical research, we verify a negative relationship between DSCs and M&A duration and an inverted U-shaped relationship between both DSCs and M&A performance, revealing the complexity of the relationship between experience and efficiency. Furthermore, drawing on upper echelon theory, the information value of DSCs will be greatly reduced when executives have overconfident psychological characteristics, which are mainly shown to negatively moderate the relationship between DSCs and M&A performance. We also conduct multiple robustness tests and supplemental analyses to illustrate the robustness and boundaries of our findings. Finally, DSCs are likely more important in environments among growth and mature firms as well as high-growth industries.

We break through the assumption that interlocking directors contribute to M&A efficiency in an acquirer-target binary relationship and examine the impact of DSCs on M&A efficiency based on micro-empirical evidence from the value of target-related upstream or downstream industries, which extends the connotation of interlocking directors and enriches the study related to factors influencing M&A efficiency.

Tourism value chain (TVC) analysis has been widely used as an alternative approach for designing sustainable and inclusive programs for tourism development in Africa, Asia, and the Pacific. This chapter looks at the experiences of three sustainable tourism initiatives in the Philippines to assess the strengths and limitations of the value chain as a framework for destination development. Short case studies describe the experiences of stakeholders in two popular destinations in the Philippines: the surfing town of San Juan, La Union, and the resort-island of Panglao, Bohol. The third case study shares the perspective of the Transforming the Tourism Value Chain (TTVC) project, a national campaign for more sustainable hotels, resort as well as meetings, conventions, incentives, and event (MICE) facilities in various destinations around the country. Stakeholders deeply involved in implementing these projects share their insights on the successes, challenges, and limitations of TVC-based approaches to resilient and sustainable destination development. The destinations' experiences suggest that the advocacy for sustainable management and operations may be comparable to a process of technology adoption or acceptance along the TVC. Practical and theoretical recommendations for leveraging the TVC toward more holistic and sustainable visitor economies are given at the end of this chapter.

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.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

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.

This study aims to examine the language choices of outdoor signs and menus in addition to the functions of outdoor signs in restaurants in a Saudi tourist city, Abha. The primary focus is on identifying the extent to which outdoor signs accurately represent the language choices of restaurant menus.

The study developed a conceptual framework for the linguistic landscape (LL) of restaurants. It employed a quantitative approach to collect outdoor signs and menus of 75 sampled restaurants in Abha using online photos and a smartphone camera. Then it analyzed the frequency and percentage of language choices on outdoor signs and menus as well as the extent to which language choices of outdoor signs represent menus.

The findings indicate that more than half (58.66%) of the restaurants employ bilingual signage in both Arabic and English. Other languages like Spanish, French, Chinese and Turkish are sporadically used, with multilingualism observed only in isolated instances. The study also reveals that bi/multilingualism on outdoor signs primarily serves informational purposes, where more than one-third (36%) of the outdoor signs use languages other than Arabic to serve a symbolic function. Regarding menus, Arabic and English dominate, while Turkish appears on one menu. Spanish, French, and Chinese are absent from restaurant menus, indicating linguistic mismatch in terms of language choices.

This study contributes to LL studies of restaurants in tourist cities by showing language choices and functions of outdoor signs and their alignment with menus.

Building information modelling (BIM) has transformed the traditional practices of the Architecture, Engineering and Construction (AEC) industry. BIM creates a collaborative digital representation of built environment data. Competitive advantage can be achieved with collaborative project delivery and rich information modelling. Despite the abundant benefits, BIM’s adoption in the AEC is susceptible to confrontation. A substantial impediment to BIM adoption often cited is data interoperability. Other facets of interoperability got limited attention. Other academic areas, including information systems, discuss the interoperability construct ahead of data interoperability. These interoperability factors have yet to be surveyed in the AEC industry. This study aims to investigate the effect of interoperability factors on BIM adoption and develop a comprehensive BIM adoption model.

The theoretical foundations of the proposed model are based on the European interoperability framework (EIF) and technology, organization, environment framework (TOE). Quantitative data collection from construction firms is gathered. The model has been thoroughly examined and validated using partial least squares structural equation modelling in SmartPLS software.

The study’s findings indicate that relative advantage, top management support, government support, organizational readiness and regulation support are determinants of BIM adoption. Financial constraints, complexity, lack of technical interoperability, semantic interoperability, organizational interoperability and uncertainty are barriers to BIM adoption. However, compatibility, competitive pressure and legal interoperability do not affect BIM adoption.

Finally, this study provides recommendations containing the essential technological, organizational, environmental and interoperability factors that AEC stakeholders can address to enhance BIM adoption.

To the best of the authors’ knowledge, this paper is one of the first studies to combine TOE and EIF in a single research model. This research provides empirical evidence for using the proposed model as a guide to promoting BIM adoption. As a result, the highlighted determinants can assist organizations in developing and executing successful policies that support BIM adoption in the AEC industry.

The purpose of this research is to determine key behaviours to be efficient in identifying and developing employees' talent. The article aims to address the relationship between learning agility and job crafting, the influence between them, and how this relationship is built to improve performance and adaptability. For this purpose, the research has analysed which behaviours obtain the highest scores in both scales (job crafting and learning agility), designing the tool which allows Human Resources (HR) professionals an efficient identification and development behaviours to get the versatile talent that companies and professionals of the future need.

Using the questionnaire that has integrated the learning agility scale and the Spanish job crafting scale. Data were collected from a sample of business professionals in Spain. Factor analysis and hierarchical cluster analysis were used, using a classificatory variable with the 126 valid responses obtained.

In an ever-changing environment, continuous employee adaptation to his/her role within a company is a critical factor for its survival. However, there is a paucity of large-scale empirical research on which behaviours employees have to develop to increase their adaptative skills. Drawing on the outcome of extant literature, the authors identify learning agility as the construct that firms have to encourage in their employees to impact job crafting. The contribution of the paper is twofold: (1) the authors empirically explored the association and the effects of learning agility and its factor on the development of job crafting. Results demonstrated the association between the two constructs; further, higher scores in both learning agility and job crafting predict increased employability, and higher scores in job crafting are associated with higher scores in change agility; (2) this study provides a multidimensional instrument that provides HR departments with the key behaviours to recruit in order to develop talent to prepare employees to face future challenges, ensuring the right performance and sustainable impact in the environment.

A limitation of this study is that it is done exclusively within Spanish companies, even though from different industries and with different characteristics. Therefore, future research is necessary and should be conducted in other countries in similar industries to explore the empirical findings from this study in additional contexts.

This research has found a tool that might allow HR departments to measure what level of job crafting and learning agility their employees have and to identify what key behaviours they need to focus on in the recruitment or in their internal strategic HR action plan to overcome any future challenges in their organization.

In a scenario where artificial intelligence is modifying the professional landscape, generating uncertainty about which skills are best to develop, the results are a guide for enterprises as to where to focus plans for learning and training, as well as for business schools regarding the content provided in training programs.

The authors advance the literature by providing a theoretical base for understanding the relationship between job crafting and learning agility. This article offers some practical managerial recommendations that help the human resources department focus on behaviours that allow talent to be identified and recruited to ensure an effective organization.

The purpose of this study is to investigate the influence of ambidextrous strategy on supply chain resilience and its impact on firm performance, employing the Dynamic Capabilities View.

Through a survey of 215 supply chain professionals, the research employs a structural equation modeling analysis to examine the relationships between ambidexterity, agile operations, resilience, and performance.

The findings demonstrate that the ambidextrous strategy significantly enhances both agile operations and supply chain resilience. In turn, agile operations and resilience positively impact firm performance. The study also reveals that agile operations and supply chain resilience partially mediate the relationship between ambidextrous strategy and firm performance.

This research contributes to the supply chain management literature by highlighting the importance of an ambidextrous approach in fostering agile operations and resilience, thereby improving firm performance. It extends the dynamic capabilities view framework by elucidating how ambidexterity acts as a pivotal mechanism for adapting to disruptions and securing competitive advantage in volatile markets. Finally, measurements of ambidextrous strategy and resilience are provided to further enhance practitioners’ understanding of building these important components in networks.