Pains and gains of environmental management system certification for the sustainable development of manufacturing companies: Heterogeneous effects of industry peer learning

Environmental sustainability is a growing global concern. Environmental management systems (EMS) could be an effective strategic tool to help firms deal with their sustainable development. However, whether EMS certification pays off financially and how it takes effect can be debated. Thus far, these questions remain largely under‐researched. In particular, the effects of EMS certification on financial performance are inconclusive, and the reasons explaining the effects are underdeveloped. This study aims to enrich the current research by exploring the mediating and moderating roles from the perspective of cost‐efficiency trade‐offs to reveal how EMS certification affects financial performance. Applying a PROCESS procedure analysis and causal mediation analysis to a sample of 1,751 Chinese listed manufacturing firms from 2008 to 2016, this study shows that the effect of EMS certification on firms' financial performance is insignificant because their operating costs burden increases while their marketing efficiency and managerial efficiency improve. For the first time, this study demonstrates the moderating role of industry peer learning, as the mediating effects decrease with the growth of industry peer learning.     

Market drivers of sustainability and sustainability learning capabilities: The moderating role of sustainability control systems

This study examines (i) the impact of market drivers of sustainability on the adoption of sustainability learning capabilities and (ii) the moderating role of sustainability control systems (SCS) on the relationship between market drivers of sustainability and sustainability learning capabilities. Drawing on the levers of control framework, stakeholder theory and organisational learning literature, survey data were collected from 175 large scale local and multinational companies operating in Sri Lanka. Findings reveal that market drivers of sustainability have a significant positive impact on sustainability learning capabilities. Whereas the interactive use of SCS shows a positive moderating impact, the diagnostic use of SCS shows a negative impact. The study enhances our understanding of (i) the influence of market drivers of sustainability on the adoption of sustainability learning capabilities and (ii) the use of SCS in enabling sustainability learning capabilities. The study reveals novel insights for managers responding to changing market drivers of sustainability, on how to (re)align different uses of SCS to enable sustainability learning capabilities.     

The Moderating Role of Strategic Integration in Performance Improvement Through Purchasing Social Responsibility in Thailand

This research aims to understand the performance of purchasing social responsibility (PSR) through moderating effect of purchasing strategic integration. The results show that PSR directly influences purchasing performance, while the relationship between PSR and purchasing performance is partially mediated by organizational learning. Moreover, strategic integration negatively moderates the relationship between PSR and purchasing performance. This study suggests that the adoption of PSR affects the operations of both buyers and suppliers in a supply chain that further encourage organizational learning and increases purchasing efficiency. The results also show that organizations may realize this effect of PSR practices but may focus on other purchasing practices, which affects purchasing performance. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

Learning about the real-world in MICE education: The case of exhibition learning from Thailand

Abstract

The use of experiential learning in tourism and hospitality education is well-documented in literature. Experiential learning studies in this field may include, for example, internship experiences, field trip perceptions, conferences, and social events. However, there is still insufficient literature to understand students’ learning and their real-world experience in MICE education, especially in the exhibition sector. This study, therefore, addresses this gap by reporting the experiential learning of graduate students in an event course with the objectives to investigate student perceptions on academic learning experiences and the development of work-related skills by carrying out the exhibition project. Students are challenged to perform a complicated task as a real exhibition organizer, and to deal with other stakeholders of the exhibition industry (e.g., exhibition venue, exhibitors, contractors, and visitors). The experiential learning method is discussed through the Plan-Do-Check-Act (PDCA) process. The results indicate that students not only gained in-depth learning about the exhibition industry, but also developed important work skills (e.g., teamwork, planning, and coordinating skills). Moreover, classroom learning, industry visits, and real-world experience are found to be the important factors contributing to exhibition learning. The current study contributes to the limited exhibition learning literature and provides event educators new insights into the teaching and learning of exhibition-based projects in regard to how students plan, learn and carry out the exhibition event through the case of Thailand. Other similar courses may apply the learning processes and results of this study to develop effective experiential learning in MICE education.     

Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.     

Using deep learning and visual analytics to explore hotel reviews and responses

This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

大数据、人工智能与厂商竞争路径

经济学对市场竞争路径的学理性分析，主要集中在价格确定、产量确定、规模经济、产业组织等方面，而对科技进步引发市场竞争路径的变化并没有足够的关注。其实，市场竞争路径变化的底蕴是科技进步，只是经济学家在分析市场竞争路径时偏好于将科技因素作为外生变量处理。大数据和人工智能等的发展可谓是一场史无前例的科技革命，它对人类经济活动产生广泛而深刻的影响主要表现为：大数据及其运用怎样影响厂商投资经营，大数据与机器学习等人工智能手段相融合会在哪些方面改变厂商竞争路径，厂商如何提高数据智能化和实现网络协同化，在什么样的条件下会出现行业垄断，等等。文章的基本分析观点是：厂商竞争路径变化是贯穿于大数据、互联网和人工智能等相互融合过程的一种现象，这种现象对应于新科技进步和运用的不同层级；微观经济分析需要将新科技因素作为内生变量，通过分析大数据、机器学习与厂商竞争路径之间的关联，揭示厂商竞争路径变化机理以及由此引致的产业组织等问题。     

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。