One size doesn’t fit all: A project designing small-scale economic development projects

The authors describe an innovative active learning strategy for a course on the economics of developing countries—a project designing a small-scale economic development project. Student teams research issues faced by developing countries and identify a specific problem in a specific locale. Students then create a detailed, feasible plan to alleviate the problem. Student plans include five key components: justification, implementation, budget, funding and evaluation. After having implemented this project for six years, the authors believe it has the potential to enhance learning and improve analytical, creative problem-solving and research skills. Results from a spring 2017 survey, where students respond favorably to the project, are discussed.     

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.     

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.     

Antecedents of the propensity to learn management practices and their impacts on firm outcomes in emerging markets: A Bayesian Model Averaging approach

This article explores the factors that motivate firms to learn new management practices. The hypotheses are empirically tested using a representative sample of 3676 small, medium and large firms from four South Asian countries and across all main sectors of economic activity. Given that we know little about the antecedents of the propensity to learn management practices in emerging markets, the study employs Bayesian Model Averaging approach to overcome the potential issue of model uncertainty. The results reveal that market competition, resource allocation towards internal and external R&D, good quality mobile network coverage and the use of external certified financial auditors have all positive and significant effects on the propensity to learn management practices. The results also suggest that private intellectual property rights protection in the context of inefficient legal systems can deter firms from learning, perhaps in fear of legal ramifications. Finally, the study shows that firms with a higher propensity of learning management practices are more likely to become profitable while exhibiting higher levels of both potential and actual innovation.     

A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior

Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours.     

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.     

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.     

Price gap anomaly in the US stock market: The whole story

This paper analyses the price gap anomaly in the US stock market (comprised of the DJI, S&P 500 and NASDAQ) covering the period 1928 to 2018. This paper aims to investigate whether or not price gaps create market inefficiencies. Price gaps occur when the current day’s opening price is different from the previous day’s closing price due orders placed before the opening of the market. Several hypotheses are tested using various statistical tests (Student’s t-test, ANOVA, Mann-Whitney test), regression analysis, and special methods, that is, the modified cumulative returns and the trading simulation approaches. We find strong evidence in favour of abnormal price movements after price gaps. We observe that during a gap day prices tend to change in the direction of the gap. A trading strategy based on this anomaly was efficient in that its results were not random, indicating that this market was not efficient. The momentum effect was found to be temporary and no evidence of seasonality in price gaps was found. Lastly, our results were also contrary to the myth that price gaps tend to get filled.