Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?

This research contributes to the debate about the relevance of Industry 4.0 technologies in improving environmental performance in the manufacturing industry. We employed a qualitative–quantitative approach in which 19 Italian operations managers were interviewed and 260 managers responded to an online questionnaire. The effects of various technologies were ranked using ordinal regression. Comments and suggestions gave context to the quantitative results. Sensors, radio-frequency identification, artificial intelligence and analytics were found to be most relevant in improving environmental performance, whereas simulation software contributed moderately. Additive manufacturing, cobots, robots, automated mobile robots and automated guided vehicles had a negative effect, augmented reality had no effect and other technologies indirectly affected environmental performance. We also found a lack of knowledge and application as well as scepticism about technologies such as artificial intelligence and augmented reality. Finally, there was concern about the disposal of electrical and electronic waste produced by these technologies.     

桥梁路面防水施工技术关键点研究

桥梁工程具有周期长以及范围大等特点。因此,在桥梁施工中任何一个环节出现偏差,都可能诱发裂缝或剥离等问题,最终影响整个桥梁的质量。路面防水工程是桥梁施工项目中的重要环节,防水工程的施工质量将直接影响桥梁结构的整体质量以及使用寿命。因此,在桥梁防水施工过程中,要选择合格的施工材料以及最优的施工方法,从而提高桥梁路面防水工程的施工质量。     

Self-service technology in supermarkets – Do frontline staff still matter?

This paper uses the concept of psychological distance under construal level theory to explore the differences in the customers’ evaluations of overall store quality, satisfaction and loyalty, based on their experiences with the traditional staff-checkout method and the relatively new self-checkout machines. Two empirical studies, a field survey with retail shoppers in UK (N1 = 313) and an online survey with members of a consumer panel in Australia (N2 = 474), show that the perceived quality of staff-checkout has a stronger positive impact on the overall store quality, satisfaction and loyalty, than the quality of self-checkout. Similarly, satisfaction with staff-checkout has a stronger positive effect on store satisfaction and loyalty, than the satisfaction with self-checkout. Finally, loyalty to staff-checkout also has a stronger positive influence on store loyalty, than the loyalty towards self-checkout. These results show that despite growing use of self-service technology, frontline staff continue to be important for overall store evaluations.     

农业企业采纳大数据技术影响因素研究——基于ISM模型的实证分析

为提升大数据技术在农业领域的应用深度与广度,进一步挖掘农业数据资源,推动农业供给侧改革。基于189份农业企业调研数据,运用ISM模型,分析了农业企业采纳大数据技术影响因素及其关联关系与递阶层次结构。提出加大技术的研发力度,提高核心设备和技术的自主知识产权水平,加强大数据技术产业协作,培养企业数据管理意识与大数据技术型人才,加强大数据技术公共示范工程的宣传作用等对策建议。     

海洋科技创新驱动区域经济增长影响效应分析

海洋经济被视为引领世界经济发展的新引擎.基于结构方程模型在经济学领域分析复杂关联因素中的优势,采用偏最小二乘结构方程模型研究海洋科技创新能力、蓝色经济发展和区域经济发展之间的关联效应,分析海洋科技与经济发展之间的内在作用机理.实证研究表明:海洋科技创新能力对蓝色经济发展具有显著的直接促进作用;引入蓝色经济作为中介变量形成传导路径,科技创新能力对驱动区域经济发展具有间接效应及明显的总体作用.     

高质量发展视角下区域创新能力的提升路径 ——以南京市江宁区为例

我国经济已由高速增长阶段转变为高质量发展阶段,实现高质量发展是新时代我国经济发展的根本要求。高质量发展与创新驱动紧密关联,必须多举措协同提升区域创新能力。区域创新能力是指一个地区将知识转化为新产品、新工艺、新服务的能力,代表了创新要素在一定区域内聚集、整合以及推动区域可持续发展的基本能力。为了有效提升区域创新能力,必须强化区域创新的综合保障、构建区域创新综合生态体系、促进创新创业项目孵化与成果转化、强化区域制造业发展相关的创新活动等。以南京市江宁区为例对此进行了分析,该区积极实施战略科技引领计划,不断强化创新工作,通过促进区域创新平台建设、区域创新的服务支撑体系构建、高端创新资源在区域的聚集、区域制造业创新发展及合理布局等,不断增强区域创新能力,有效支撑了区域高质量发展。     

高校新农村研究院：科技支撑乡村振兴的有效载体

[目的]较之前开展的新农村建设,乡村振兴战略是一种更高层次的发展手段和目标。文章旨在对高校新农村研究院推动乡村振兴的实践经验和困难问题进行梳理,分析背后的成因与制约因素,以期为以高校新农村研究院为抓手、从科技引领和支撑的角度推动乡村振兴战略的落地实施提供有针对性的建议。[方法]该文从协同创新理论出发,基于对沈阳农业大学新农村发展研究院的实地调研,采用文献梳理、归纳总结和定性分析等方法对高校新农村研究院为乡村振兴战略提供科技支撑的机理、模式、作用和困难问题进行了梳理。[结果]高校新农村发展研究院通过整合政府、高校、企业等多方资源,从技术协同、人才协同与理念协同3方面为乡村振兴提供了有效支撑。但目前各级政府对高校新农村研究院的作用地位还缺乏充分认识,存在财政支持不到位、技术推广激励与考评机制不完善等问题。[结论]整合各方科技创新资源,构建起各类主体跨界协同、不同技术集成应用、创新资源全链配置、关联产业融合发展的新型农业科技协同创新体系;进一步强化高校新农村发展研究院在农业科技服务中的主体地位,增强财政对新农村发展研究院的持续投入,改革高校农技推广人员职称评定与考核激励机制。     

Evaluation of the green technology innovation efficiency of China's manufacturing industries: DEA window analysis with ideal window width

Data Envelopment Analysis (DEA) window analysis with ideal window width is applied to evaluate the green technology innovation efficiency of 28 manufacturing industries in China during 2006–2014. The obtained results are compared with those calculated using the traditional DEA model, and convergence analysis of the efficiency is conducted. Five years is the obtained ideal window width and DEA window analysis with ideal window produces results closer to reality for China's manufacturing industry. The overall efficiency of the green technology innovation in the manufacturing sector is low following a wave-shaped curve – first decreasing, then increasing and decreasing again, with large inter-industrial differences. There are 8 high-, 14 medium- and 6 low-efficiency industries. A convergence trend in the green technology innovation efficiency within the 28 manufacturing industries exists, implying a catch-up effect between them.