Hongyu Lv, Ning Ding, Yiming Zhai, Yingjie Du, Fengxi Xie
Heritage crimes can result in the significant loss of cultural relics and predicting them is crucial. To address the issues of inconsistent textual information format and the challenge of preventing and combating heritage crimes, this paper develops a system that extracts crime elements and predict heritage crime occurrences. The system comprises two deep-learning models. The first model, Bi-LSTM + CRF, is constructed to automatically extract crime elements and perform spatio-temporal analysis of crimes based on them. By integrating routine activity theory, social disorder theory, and practical field experience, the research reveals that holidays and other special days (SD) perform a critical role as influential factors in heritage crimes. Building upon these findings, the second model, LSTM + SD, is constructed to predict excavation-type heritage crimes. The results demonstrate that the model with the introduction of the holiday factor improves the RMSE and MAE by 6.4% and 47.8%, respectively, when compared to the original LSTM model. This paper presents research aimed at extracting crime elements and predicting excavation-type heritage crimes. With the ongoing expansion of data volume, the practical significance of the proposed system is poised to escalate. The results of this study are expected to provide decision-making support for heritage protection departments and public security authorities in preventing and combating crimes.
{"title":"The System for Extracting Crime Elements and Predicting Excavation-Type Heritage Crimes Based on Deep Learning Models","authors":"Hongyu Lv, Ning Ding, Yiming Zhai, Yingjie Du, Fengxi Xie","doi":"10.3390/systems11060289","DOIUrl":"https://doi.org/10.3390/systems11060289","url":null,"abstract":"Heritage crimes can result in the significant loss of cultural relics and predicting them is crucial. To address the issues of inconsistent textual information format and the challenge of preventing and combating heritage crimes, this paper develops a system that extracts crime elements and predict heritage crime occurrences. The system comprises two deep-learning models. The first model, Bi-LSTM + CRF, is constructed to automatically extract crime elements and perform spatio-temporal analysis of crimes based on them. By integrating routine activity theory, social disorder theory, and practical field experience, the research reveals that holidays and other special days (SD) perform a critical role as influential factors in heritage crimes. Building upon these findings, the second model, LSTM + SD, is constructed to predict excavation-type heritage crimes. The results demonstrate that the model with the introduction of the holiday factor improves the RMSE and MAE by 6.4% and 47.8%, respectively, when compared to the original LSTM model. This paper presents research aimed at extracting crime elements and predicting excavation-type heritage crimes. With the ongoing expansion of data volume, the practical significance of the proposed system is poised to escalate. The results of this study are expected to provide decision-making support for heritage protection departments and public security authorities in preventing and combating crimes.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80999875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhiyong Fu, Anna Barbara, P. Scupelli, Yanru Lyu, Y. Cheng, S. Sul
Exploring future scenarios can consider future generations and society from a long-term perspective. A Futures Triangle is an approach used for mapping future scenarios. In general, the Futures Triangle collects weak signals using qualitative research methods. However, collecting weak signals qualitatively is limited by its small data size and manual data analysis errors. To overcome those limitations, this study proposes the data-driven futuristic scenario approach. This approach analyzes a large number of social perceptions existing in social networks as weak signals via semantic network analysis. Using our proposed data-driven approach, researchers can quantitatively collect weak signals for a Futures Triangle. To verify the applicability of the proposed method, we conducted a case study on the Chinese smart home service experience. The dataset consists of 2421 posts containing the keyword “smart home experience” on the Chinese social media platform Weibo. Three future scenarios were constructed using the proposed method. The results demonstrate the feasibility of the proposed methodology. The data-driven futuristic scenario approach has the advantage of quantitatively analyzing a large amount of stakeholder data to provide weak signals for the Futures Triangle. We suggest that the data-driven futuristic scenario approach serves as a supplementary method, combined with the traditional Futures Triangle approach, to comprehensively explore future scenarios.
{"title":"Data-Driven Futuristic Scenarios: Smart Home Service Experience Foresight Based on Social Media Data","authors":"Zhiyong Fu, Anna Barbara, P. Scupelli, Yanru Lyu, Y. Cheng, S. Sul","doi":"10.3390/systems11060287","DOIUrl":"https://doi.org/10.3390/systems11060287","url":null,"abstract":"Exploring future scenarios can consider future generations and society from a long-term perspective. A Futures Triangle is an approach used for mapping future scenarios. In general, the Futures Triangle collects weak signals using qualitative research methods. However, collecting weak signals qualitatively is limited by its small data size and manual data analysis errors. To overcome those limitations, this study proposes the data-driven futuristic scenario approach. This approach analyzes a large number of social perceptions existing in social networks as weak signals via semantic network analysis. Using our proposed data-driven approach, researchers can quantitatively collect weak signals for a Futures Triangle. To verify the applicability of the proposed method, we conducted a case study on the Chinese smart home service experience. The dataset consists of 2421 posts containing the keyword “smart home experience” on the Chinese social media platform Weibo. Three future scenarios were constructed using the proposed method. The results demonstrate the feasibility of the proposed methodology. The data-driven futuristic scenario approach has the advantage of quantitatively analyzing a large amount of stakeholder data to provide weak signals for the Futures Triangle. We suggest that the data-driven futuristic scenario approach serves as a supplementary method, combined with the traditional Futures Triangle approach, to comprehensively explore future scenarios.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80209793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To overcome the limitations of the present grey models in spatial data analysis, a spatial weight matrix is incorporated into the grey discrete model to create the SDGM(1,1,m) model, and the L1-SDGM(1,1,m) model is proposed, considering the time lag effect to realize the simultaneous forecasting of spatial data. The validation of the SDGM(1,1,m) and L1-SDGM(1,1,m) models is achieved, and finally, the per capita energy consumption levels (PCECs) of 30 provinces in China from 2020 to 2025 is predicted using SDGM(1,1,m) with a metabolic mechanism. We draw the following conclusions. First, the SDGM(1,1,m) and L1-SDGM(1,1,m) models established in this paper are reasonable and improve forecasting accuracy while supporting interactive regional forecasting. Second, although SDGM(1,1,m) resembles the DGM(1,n) model, their modeling conditions and targets are different. Third, the SDGM(1,1,m) and L1-SDGM(1,1,m) models can be used to effectively analyze the spatial spillover effects within the selected modeling interval while achieving accurate predictions; notably, from 2010 to 2017, the PCECs of Inner Mongolia and Qinghai were most affected by spatial factors, while the PCECs of Jilin, Jiangxi, and other provinces were influenced little by spatial factors. Fourth, predictions indicate that the PCECs of most Chinese provinces will increase under the current grey conditions, while the PCECs of provinces such as Beijing are expected to decrease.
{"title":"Forecasting per Capita Energy Consumption in China Using a Spatial Discrete Grey Prediction Model","authors":"Huiping Wang, Zhun Zhang","doi":"10.3390/systems11060285","DOIUrl":"https://doi.org/10.3390/systems11060285","url":null,"abstract":"To overcome the limitations of the present grey models in spatial data analysis, a spatial weight matrix is incorporated into the grey discrete model to create the SDGM(1,1,m) model, and the L1-SDGM(1,1,m) model is proposed, considering the time lag effect to realize the simultaneous forecasting of spatial data. The validation of the SDGM(1,1,m) and L1-SDGM(1,1,m) models is achieved, and finally, the per capita energy consumption levels (PCECs) of 30 provinces in China from 2020 to 2025 is predicted using SDGM(1,1,m) with a metabolic mechanism. We draw the following conclusions. First, the SDGM(1,1,m) and L1-SDGM(1,1,m) models established in this paper are reasonable and improve forecasting accuracy while supporting interactive regional forecasting. Second, although SDGM(1,1,m) resembles the DGM(1,n) model, their modeling conditions and targets are different. Third, the SDGM(1,1,m) and L1-SDGM(1,1,m) models can be used to effectively analyze the spatial spillover effects within the selected modeling interval while achieving accurate predictions; notably, from 2010 to 2017, the PCECs of Inner Mongolia and Qinghai were most affected by spatial factors, while the PCECs of Jilin, Jiangxi, and other provinces were influenced little by spatial factors. Fourth, predictions indicate that the PCECs of most Chinese provinces will increase under the current grey conditions, while the PCECs of provinces such as Beijing are expected to decrease.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75008791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Long-term exposure to air pollution will pose a serious threat to human health. Accurate prediction can help people reduce exposure risks and promote environmental pollution control. However, most previous studies have ignored the spatial spillover of air pollution, i.e., that the current region’s air quality is also correlated with that of geographically adjacent areas. Therefore, this paper proposes an innovative spatiotemporal hybrid early warning system based on adaptive feature extraction and improved fuzzy cognition maps. Firstly, a spatial spillover analysis model based on the Moran index and local gravitational clustering was proposed to capture the diffusion and concentration characteristics of air pollution between regions. Then, an adaptive feature extraction model based on an optimized Hampel filter was put forward to process and correct the outliers in the original series. Finally, a hesitant fuzzy information optimized fuzzy cognitive maps model was proposed to forecast the air quality of urban agglomeration. The experimental results show that the air quality forecasting accuracy of urban agglomerations can be significantly improved when the geographical conditions and other interactions among cities are comprehensively considered, and the proposed model outperformed other benchmarks and can be used as a powerful analytical tool during urban agglomeration air quality management.
{"title":"Spatiotemporal Hybrid Air Pollution Early Warning System of Urban Agglomeration Based on Adaptive Feature Extraction and Hesitant Fuzzy Cognitive Maps","authors":"Xiaoyang Gu, Hongmin Li, Henghao Fan","doi":"10.3390/systems11060286","DOIUrl":"https://doi.org/10.3390/systems11060286","url":null,"abstract":"Long-term exposure to air pollution will pose a serious threat to human health. Accurate prediction can help people reduce exposure risks and promote environmental pollution control. However, most previous studies have ignored the spatial spillover of air pollution, i.e., that the current region’s air quality is also correlated with that of geographically adjacent areas. Therefore, this paper proposes an innovative spatiotemporal hybrid early warning system based on adaptive feature extraction and improved fuzzy cognition maps. Firstly, a spatial spillover analysis model based on the Moran index and local gravitational clustering was proposed to capture the diffusion and concentration characteristics of air pollution between regions. Then, an adaptive feature extraction model based on an optimized Hampel filter was put forward to process and correct the outliers in the original series. Finally, a hesitant fuzzy information optimized fuzzy cognitive maps model was proposed to forecast the air quality of urban agglomeration. The experimental results show that the air quality forecasting accuracy of urban agglomerations can be significantly improved when the geographical conditions and other interactions among cities are comprehensively considered, and the proposed model outperformed other benchmarks and can be used as a powerful analytical tool during urban agglomeration air quality management.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84790150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tao Wang, Bingsheng Liu, Shimeng Liu, Kuan Zhang, Mingyue Ma
Climate change is the main cause of frequent extreme weather and natural disasters. Therefore, effective climate adaptation strategies for urban rail transit (URT) should be adopted to cope with extreme precipitation events (EPEs). This study proposes a decision-making model based on climate change for drainage renovation, which consists of an optimal renovation sequence model and an optimal investment timing model. This study analyzes the inundation risk of each station and its node importance in the URT network and then uses a multi-attribute decision analysis (MADA) to determine the optimal renovation sequence. This study also uses a real options pricing approach to calculate the value of an option in order to defer the renovation project and determine the optimal investment timing. Then, the Beijing Urban Rail Transit (BURT) is taken as an example to conduct an empirical analysis of the proposed model. Considering the uncertainty of climate change and the complexity of the URT network, the model can obtain the optimal renovation sequence and the investment timing of each station, which is expected to provide a decision-making tool for urban governments to formulate an optimal plan that strengthens the prevention of flooding disasters.
{"title":"A Two-Stage Investment Decision-Making Model for Urban Rail Transit Drainage Renovation","authors":"Tao Wang, Bingsheng Liu, Shimeng Liu, Kuan Zhang, Mingyue Ma","doi":"10.3390/systems11060280","DOIUrl":"https://doi.org/10.3390/systems11060280","url":null,"abstract":"Climate change is the main cause of frequent extreme weather and natural disasters. Therefore, effective climate adaptation strategies for urban rail transit (URT) should be adopted to cope with extreme precipitation events (EPEs). This study proposes a decision-making model based on climate change for drainage renovation, which consists of an optimal renovation sequence model and an optimal investment timing model. This study analyzes the inundation risk of each station and its node importance in the URT network and then uses a multi-attribute decision analysis (MADA) to determine the optimal renovation sequence. This study also uses a real options pricing approach to calculate the value of an option in order to defer the renovation project and determine the optimal investment timing. Then, the Beijing Urban Rail Transit (BURT) is taken as an example to conduct an empirical analysis of the proposed model. Considering the uncertainty of climate change and the complexity of the URT network, the model can obtain the optimal renovation sequence and the investment timing of each station, which is expected to provide a decision-making tool for urban governments to formulate an optimal plan that strengthens the prevention of flooding disasters.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83038521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SCF (Supply Chain Finance), as an emerging technology-driven financial optimization approach, has grown quickly worldwide with the development of information technology. China is one noteworthy country influenced by SCF development; its traditional financial market structure dominated by banks is undergoing change. The E-commerce-centered SCF has evolved into the representative capital provider in the new financial “Blue Ocean” market. For capturing competitiveness, Chinese JD (Jingdong) SCF practice backed to JD E-commerce is a typical example involving business model innovations, but with few previous related studies on this aspect. Therefore, to fill the research gap, this paper introduces a hybrid theoretical analysis of the BMC (Business Model Canvas) considering a three-layer strategic innovation structure and financial analysis regarding a modified competitive advantages-gaining model to comprehensively explore the recent innovative development and transformation of JD SCF business based on the perspective of competitive advantages. This study identifies JD SCF’s two times of business model innovations that benefit from its sustainable development; verifies that “cost”, “differentiation”, and “focused strategy” are three means for JD SCF practice gaining competitiveness at different development stages and simultaneously emphasizes that the latter two are influenced by business model innovation. The mixed analysis work in this paper may contribute both theoretical and practical implications.
供应链金融作为一种新兴的技术驱动的金融优化方式，随着信息技术的发展在全球范围内迅速发展起来。中国是一个值得关注的受气候变化发展影响的国家;以银行为主导的传统金融市场格局正在发生变化。以电子商务为核心的SCF已发展成为金融新“蓝海”市场中具有代表性的资本提供者。在获取竞争力方面，以京东电子商务为背景的中国京东SCF实践是商业模式创新的典型案例，但在这方面的相关研究较少。因此，为了填补研究空白，本文引入了基于三层战略创新结构的BMC (Business Model Canvas)理论分析和基于改进的竞争优势获取模型的财务分析相结合的方法，以竞争优势视角全面探讨京东供应链业务近年来的创新发展与转型。本研究发现京东SCF的两次商业模式创新受益于其可持续发展;验证了“成本”、“差异化”和“聚焦战略”是京东供应链物流实践在不同发展阶段获得竞争力的三种手段，同时强调了后两者受到商业模式创新的影响。本文的混合分析工作具有理论和实践意义。
{"title":"Supply Chain Finance Business Model Innovation: Case Study on a Chinese E-Commerce-Centered SCF Adopter","authors":"Lele Zhou, Hyangsook Lee","doi":"10.3390/systems11060278","DOIUrl":"https://doi.org/10.3390/systems11060278","url":null,"abstract":"SCF (Supply Chain Finance), as an emerging technology-driven financial optimization approach, has grown quickly worldwide with the development of information technology. China is one noteworthy country influenced by SCF development; its traditional financial market structure dominated by banks is undergoing change. The E-commerce-centered SCF has evolved into the representative capital provider in the new financial “Blue Ocean” market. For capturing competitiveness, Chinese JD (Jingdong) SCF practice backed to JD E-commerce is a typical example involving business model innovations, but with few previous related studies on this aspect. Therefore, to fill the research gap, this paper introduces a hybrid theoretical analysis of the BMC (Business Model Canvas) considering a three-layer strategic innovation structure and financial analysis regarding a modified competitive advantages-gaining model to comprehensively explore the recent innovative development and transformation of JD SCF business based on the perspective of competitive advantages. This study identifies JD SCF’s two times of business model innovations that benefit from its sustainable development; verifies that “cost”, “differentiation”, and “focused strategy” are three means for JD SCF practice gaining competitiveness at different development stages and simultaneously emphasizes that the latter two are influenced by business model innovation. The mixed analysis work in this paper may contribute both theoretical and practical implications.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83737726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Two emerging trends for designing a complex, cyber-physical systems are the component-based and mixed-criticality (MC) approaches. A component-based approach independently develops individual components and subsequently integrates them to reduce system complexity. This approach provides strong isolation among components but incurs resource inefficiency. Alternatively, an MC approach integrates components of different criticality with different levels of guarantee for resource efficiency, while components are not isolated. To leverage MC and component-based approaches, we investigate how to balance component isolation and resource efficiency under component-based MC systems. We introduce the concept of component-MC schedulability, where isolated tasks are protected from external events outside the component, and shared tasks may be suspended for the critical events of other components. Under component-MC schedulability, we propose a component-based mixed-criticality scheduling framework with dynamic resource allocation (CMC-DRA), which suspends low-criticality tasks differently depending on internal or external component behavior. We also develop scheduling semantics and analyze the schedulability for CMC-DRA. Through simulation on synthetic workloads, we demonstrate that CMC-DRA has up to 88.3% higher schedulability than existing approaches and reduces the deadline miss ratio by up to 47.7%.
{"title":"Scheduling Complex Cyber-Physical Systems with Mixed-Criticality Components","authors":"Jaewoo Lee, Keumseok Koh","doi":"10.3390/systems11060281","DOIUrl":"https://doi.org/10.3390/systems11060281","url":null,"abstract":"Two emerging trends for designing a complex, cyber-physical systems are the component-based and mixed-criticality (MC) approaches. A component-based approach independently develops individual components and subsequently integrates them to reduce system complexity. This approach provides strong isolation among components but incurs resource inefficiency. Alternatively, an MC approach integrates components of different criticality with different levels of guarantee for resource efficiency, while components are not isolated. To leverage MC and component-based approaches, we investigate how to balance component isolation and resource efficiency under component-based MC systems. We introduce the concept of component-MC schedulability, where isolated tasks are protected from external events outside the component, and shared tasks may be suspended for the critical events of other components. Under component-MC schedulability, we propose a component-based mixed-criticality scheduling framework with dynamic resource allocation (CMC-DRA), which suspends low-criticality tasks differently depending on internal or external component behavior. We also develop scheduling semantics and analyze the schedulability for CMC-DRA. Through simulation on synthetic workloads, we demonstrate that CMC-DRA has up to 88.3% higher schedulability than existing approaches and reduces the deadline miss ratio by up to 47.7%.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88120811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rima H. Binsaeed, A. Grigorescu, Zahid Yousaf, E. Condrea, A. Nassani
The advancement of technology offers various opportunities for business organizations to achieve sustainable growth. Through emerging technologies, business organizations are able to collect and analyze essential information vital for the acceleration of innovation. Therefore, this study investigated how big data contribute to the innovation activities of manufacturing entrepreneurs in terms of big data analytic capability (BDAC). The aim of this study was to relate BDAC to organizational readiness and innovation performance (IP). Moreover, we examined the mediating role of organizational readiness between BDAC and IP. We also examined the strengthening role of digital orientation. To collect the study data, we approached 494 frontline managers of the manufacturing sector of Saudi Arabia. The collected data were analyzed using statistical techniques such as descriptive, correlation, and hierarchical regression techniques. We found that BDAC plays a vital role in developing organizational readiness and IP. The findings also proved that organizational readiness has a significant effect on IP. The results revealed that organizational readiness mediates between BDAC and IP.
{"title":"Leading Role of Big Data Analytic Capability in Innovation Performance: Role of Organizational Readiness and Digital Orientation","authors":"Rima H. Binsaeed, A. Grigorescu, Zahid Yousaf, E. Condrea, A. Nassani","doi":"10.3390/systems11060284","DOIUrl":"https://doi.org/10.3390/systems11060284","url":null,"abstract":"The advancement of technology offers various opportunities for business organizations to achieve sustainable growth. Through emerging technologies, business organizations are able to collect and analyze essential information vital for the acceleration of innovation. Therefore, this study investigated how big data contribute to the innovation activities of manufacturing entrepreneurs in terms of big data analytic capability (BDAC). The aim of this study was to relate BDAC to organizational readiness and innovation performance (IP). Moreover, we examined the mediating role of organizational readiness between BDAC and IP. We also examined the strengthening role of digital orientation. To collect the study data, we approached 494 frontline managers of the manufacturing sector of Saudi Arabia. The collected data were analyzed using statistical techniques such as descriptive, correlation, and hierarchical regression techniques. We found that BDAC plays a vital role in developing organizational readiness and IP. The findings also proved that organizational readiness has a significant effect on IP. The results revealed that organizational readiness mediates between BDAC and IP.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89808889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article aims to analyze whether a larger diffusion of institutional–private co-operation in farming systems, such as Italian food districts (FDs), is helpful in pursuing goals of sustainable land use in agriculture. The paper focuses on the case of Lombardia in Italy, a region where this form of public–private partnership is widespread throughout the regional territory. Combining differences-in-differences (DiD) and propensity score matching (PSM) methods to reduce the estimation bias, we assessed and quantified a “district effect” on the sustainable management of lands. Specifically, using several land-use and land-use change proxies as outcome measures, we verified whether there are significant differences in such outcomes between two different groups of municipalities: those involved in FDs and those not. Our analysis shows that there is an “FD effect” on the persistence of agricultural activity and, although this does not necessarily translate into more landscape diversity, it can at least counteract detrimental tendencies such as the loss of natural elements, the loss of landscape diversity due to intensive farming, and land abandonment.
{"title":"An Assessment of the Effects of Food Districts on Sustainable Management of Land: The Case of Lombardia, Italy","authors":"F. Carillo, R. Henke, Alberto Sturla","doi":"10.3390/systems11060283","DOIUrl":"https://doi.org/10.3390/systems11060283","url":null,"abstract":"The article aims to analyze whether a larger diffusion of institutional–private co-operation in farming systems, such as Italian food districts (FDs), is helpful in pursuing goals of sustainable land use in agriculture. The paper focuses on the case of Lombardia in Italy, a region where this form of public–private partnership is widespread throughout the regional territory. Combining differences-in-differences (DiD) and propensity score matching (PSM) methods to reduce the estimation bias, we assessed and quantified a “district effect” on the sustainable management of lands. Specifically, using several land-use and land-use change proxies as outcome measures, we verified whether there are significant differences in such outcomes between two different groups of municipalities: those involved in FDs and those not. Our analysis shows that there is an “FD effect” on the persistence of agricultural activity and, although this does not necessarily translate into more landscape diversity, it can at least counteract detrimental tendencies such as the loss of natural elements, the loss of landscape diversity due to intensive farming, and land abandonment.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73347103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Regional transportation integration is a key aspect in promoting regional integration and high-quality economic development, as it can improve inter-regional connectivity, decrease transportation costs, and facilitate the flow of labor, capital, technology, and data. However, regional transportation integration is also difficult to quantitatively evaluate, causing difficulties in comprehensively understanding the specific transportation–economic relationships for different regions that are planned for integration. This article studied 41 cities in the Yangtze River Delta, which is known as the largest regional integration project in China. Two sets of index systems were created to quantitatively evaluate regional transportation integration and high-quality economic development. Coupling coordination degree analysis was then performed to investigate the relationship between the two systems. It was found that areas with a high degree of coupling coordination are located in the Z-shaped belt with the Shanghai–Nanjing–Hefei and Shanghai–Hangzhou–Ningbo urban agglomerations. Furthermore, specific developmental gaps between regional transportation integration and high-quality economic development were identified and mapped, showing areas with transportation development falling behind economic development and vice versa. Based on these findings, a number of policy suggestions are provided from the perspective of province and regional development. It is recommended to continue to invest in transportation development and integration in the well-coordinated Z-shaped region and areas with transportation development falling behind economic development, while it is not recommended to use transportation investment to solve economic problems for those under-developed regions that already have relatively advanced transportation than economic development.
{"title":"Regional Transportation Integration and High-Quality Economic Development, Coupling Coordination Analysis, in the Yangtze River Delta, China","authors":"Na Li, Yun Song, Wen Xia, Shucai Fu","doi":"10.3390/systems11060279","DOIUrl":"https://doi.org/10.3390/systems11060279","url":null,"abstract":"Regional transportation integration is a key aspect in promoting regional integration and high-quality economic development, as it can improve inter-regional connectivity, decrease transportation costs, and facilitate the flow of labor, capital, technology, and data. However, regional transportation integration is also difficult to quantitatively evaluate, causing difficulties in comprehensively understanding the specific transportation–economic relationships for different regions that are planned for integration. This article studied 41 cities in the Yangtze River Delta, which is known as the largest regional integration project in China. Two sets of index systems were created to quantitatively evaluate regional transportation integration and high-quality economic development. Coupling coordination degree analysis was then performed to investigate the relationship between the two systems. It was found that areas with a high degree of coupling coordination are located in the Z-shaped belt with the Shanghai–Nanjing–Hefei and Shanghai–Hangzhou–Ningbo urban agglomerations. Furthermore, specific developmental gaps between regional transportation integration and high-quality economic development were identified and mapped, showing areas with transportation development falling behind economic development and vice versa. Based on these findings, a number of policy suggestions are provided from the perspective of province and regional development. It is recommended to continue to invest in transportation development and integration in the well-coordinated Z-shaped region and areas with transportation development falling behind economic development, while it is not recommended to use transportation investment to solve economic problems for those under-developed regions that already have relatively advanced transportation than economic development.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87240716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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