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Traditional research has suggested that external environmental regulations and internal executive attitude are important factors. However, no in-depth study has yet to be conducted on the interaction between internal and external factors. This study constructs a model of the relationship between the different driving factors of eco-innovation from internal and external perspectives. By analyzing survey data covering 214 enterprises, this study explores the impact of environmental regulations on eco-innovation and further analyzes the moderating effect of the environmental awareness and commitment of top managers. Results show that, first, formal and informal environmental regulations and the environmental awareness and commitment of top managers have significant positive effects on eco-innovation. Second, the environmental awareness of top managers has no significant moderating effect on the relationship between environmental regulations and eco-innovation. Third, the environmental commitment of top managers has a significant positive moderating effect on the relationship between formal environmental regulations and eco-innovation. However, such environmental commitment has no significant moderating effect on the relationship between informal environmental regulations and eco-innovation. Finally, based on the empirical results, this study provides relevant managerial implications and policy suggestions. [ABSTRACT FROM AUTHOR]

This study investigates the interplay of internal and external factors in eco-innovation by analyzing survey data from 345 firms. This research aims to examine firm performance by employing the theoretical frameworks of resource-based theory and contingency theory. It establishes a model exploring the relationship between eco-innovation capabilities, firm performance, and the moderating influence of partnerships with alliances and partnership with universities. Findings reveal positive effects of eco-innovation capabilities on firm performance. Additionally, partnership with alliances and partnership with universities both have a significant positive moderating effect on the relationship between eco-innovation capabilities and firm performance. The study offers managerial insights and policy recommendations based on empirical results, emphasizing the importance of fostering eco-innovation capabilities and strategic partnerships for enhanced firm performance. [ABSTRACT FROM AUTHOR]

Green innovation is an essential support to realise sustainable development, while environmental regulation is an influential factor affecting green innovation, and different types of environmental regulation will have differentiated impacts on corporate green innovation. The aim of this paper is to shed light on the relationship between environmental regulation and green innovation and to explore mechanisms based on the institutional theory. Chinese manufacturing enterprises were selected as the study sample and empirically analysed using a multilayer linear regression model. The results show that command-and-control, market-incentive and voluntary environmental regulations all significantly and positively affect firms' green innovation, but voluntary environmental regulations are more effective; the level of digitalisation technology and the scope of digitalisation application are both conducive to firms' green innovation and play a partially mediating role between environmental regulations and green innovation. Based on these findings, this paper discusses the implications of regulation and provides an outlook for future research directions. [ABSTRACT FROM AUTHOR]

User-generated content (UGC) is an important data source for tourism GIScience research. However, no effective approach exists for identifying hidden spatiotemporal patterns within multi-scale unstructured UGC. Therefore, we developed an algorithm to measure the tourist destination popularity (TDP) based on a multi-spatiotemporal text granular computing model, called TDPMTGC. To accurately granulate the spatial and temporal information of tourism text, tourism text data granules are used to represent landscape objects. These granules are unified objects that possess multiple attributes, such as spatial and temporal dimensions. The multi-spatiotemporal scales are characterized by the multi-hierarchical structure of granular computing, and transformations of granular layers and data granule size are achieved by scale selection in the spatial and temporal dimensions. Therefore, all scales between the spatial and temporal dimension are related, which allows for the comparability of the data granules of all spatial-spatial, temporal-temporal and spatial-temporal layers. This approach achieves a quantitative description and comparison of the popularity value of granules between adjacent scales and cross-scales. Therefore, the TDP with multi-spatiotemporal scales can be deduced and calculated in a systematic framework. We first introduce the conceptual framework of TDPMTGC to construct a quantitative measurement model of TDP at multi-spatiotemporal scales. Then, we present a dataset construction approach to support multi-spatiotemporal scale granular reorganization. Finally, TDPMTGC is derived to describe both the TDP at a single spatial or temporal scale and the patterns and processes of the TDP at multi-spatiotemporal scales. A case study from Jiuzhaigou shows that the TDP derived using TDPMTGC is consistent with the conclusions of existing studies. More importantly, TDPMTGC provides additional detailed characteristics, such as the contributions of different scenic spots in a tourist route or scenic area, the monthly anomalies and daily contributions of TDP in a specific year, the distinct weakening of tourist route scale in tourist cognition, and the daily variations of TDP during in-season and off-season times. This is the first time that a granular computing model has been introduced to tourism GIScience that provides a feasible scheme for reorganizing large-scale unstructured text and constructing public spatiotemporal UGC tourism datasets. TDPMTGC constitutes a new approach for exploring tourist behaviors and the driving mechanisms of tourism patterns and processes.

[This corrects the article DOI: 10.1371/journal.pone.0228175.].