Past,present, and future of green product innovation

Firms are under constant pressure from various governmental and nongovernmental agencies to switch from conventional environmentally polluting products to green product innovations (GPIs). However, the relevant research pertaining to GPI has been published in a diverse set of journals that vary in their scope and readership and, therefore, the scholarly contribution to the topic remains largely fragmented. This study has utilised a systematic literature review approach to examine the literary corpus on GPI to paint a holistic picture of its different aspects. The content and thematic analysis of 85 studies resulted in the extraction of seven key research themes: organisational capabilities, organisational learning, institutional pressures, barriers, structural changes, benefits of GPI, and methodological choices. This study's findings further highlight the various gaps in the GPI literature and raise some research questions that warrant scholarly investigation in the future. Likewise, our study has important implications for practitioners who are likely to benefit from a holistic understanding of the different aspects of GPI. Similarly, policymakers can use this study's findings to introduce policy interventions, especially in countries where GPI adoption is low.     

Co-location as a catalyst for service innovation: a study of Scottish health and social care

Academic literature and policy on co-location of local public services focus on the cost benefits. Other benefits and outcomes of co-location, including service innovations benefiting users, are under-conceptualized. This paper suggests a framework for evaluating co-location as a learning environment for innovation, drawing on new case studies of five Community Health Partnerships in Scotland charged with more closely coordinating health and social care. We conclude that partnerships using co-location are benefiting from additional service innovations.     

Analysing enterprise architecture maturity models: a learning perspective

In order to aid organisations in the adoption of enterprise architecture (EA) best practices, maturity models have been proposed in the literature. These models offer organisational roadmaps and assessment frameworks for increasing EA maturity. However, key questions concerning the implied meaning of the term maturity in the context of these models have been left unexplored by previous research. This research, aided by the field of organisational learning, offers new insights into the implied assumptions of current EA maturity models and offers initial concepts and constructs to guide the conceptualisation, construction and refinement of enterprise maturity models.     

以开放的理念推动团队学习和创新——上海市电力公司市区供电公司团队学习新模式

企业要实现可持续发展,如何激发广大员工主动学习、团队合作以及管理创新的意识,使员工积极自觉参与公司发展,是企业必须解决的一大课题。基于上述考虑,上海市电力公司市区供电公司以"思维前线·智力风暴"人力资源开发解决方案青年设计大赛为基础,提出了供电公司团队学习新模式。文中首先对新模式内容进行了介绍,包括本次大赛的形式、内容等;接着结合相关理论,就新模式对企业团队学习、创新与可持续发展、创建和谐企业等方面产生的积极影响进行了论述。     

大学英语自主学习探析

回顾了国内外自主学习的教学理论,指出了大学英语自主学习的必要性,探讨了在自主学习模式下如何进行大学英语教学。     

PBL教学结合多媒体技术在物流专业课堂教学改革中的应用

文章分析了传统理论教学所存在的问题,结合《配送中心运营管理》课程的特点,将PBL教学模式与多媒体技术进行结合改革课堂教学模式,从而达到提高学生积极性和创造性的目的,对实施前准备工作、课堂教学中的组织和课堂后质量考核评价进行了设计和应用,从而达到培养学生职业能力的目的。     

基于PC A的江西省区域物流能力评价研究

区域物流能力是区域物流系统为社会提供支撑和服务的能力,其能力的高低会直接影响到区域经济的发展。根据江西省2002～2011年的相关数据,运用主成分分析法（PCA）对江西省近10年的区域物流能力进行了评价和实证研究,分析结果表明,江西省10年来的区域物流能力逐年提高,但也存在着基础设施落后、人才缺失等问题。最后,针对其存在的问题提出了吸收物流先进技术、积极培养物流经营及管理人才和完善物流业基础设施建设等对策与建议。     

汽修专业课程开发的几点思路

职业教育的课程开发比教材编写丰富、复杂,值得广大职业教育工作者研究和探讨。然而,现在的职业教育对课程开发重视不够,用教材编写代替课程开发,导致教师在教学活动中对教材的依赖性越来越强,甚至脱离实际,不适合职业教育的健康发展。     

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well-performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well-established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes’ interactions with exogenous factors. To assist related work, we made the code available in a dedicated repository.     

Understanding how heterogeneous agents affect Principal's returns: Perspectives from short-termism and Bayesian learning

We consider a general framework of optimal contract design under the heterogeneity and short-termism of agents. Our research shows that the optimal contract must weigh the agent's information rent, incentive cost, and benefit to overcome the contract's adverse selection and moral hazards. Agents with higher moral levels were more likely to choose higher effort and lower manipulation. Simultaneously, the principal offers lower incentives and receives more significant payoff. We also extend our model to investigate the benefits of Bayesian learning. Furthermore, we compare the principal's returns in general and learning models and find that the learning contract can bring more profit to the principal.