Does online observational learning matter? Empirical evidence from panel data

The information presented on a product sales page plays an important role in consumers' purchase decisions. This study examines the persuasive effect, whether a customer's choice is heavily driven by information inferred from others' behavior, and how these impacts are moderated by market age and product type. Results show that online customer choice was significantly affected by historical cumulative sales and times saved. Positive cumulative sales and shop service quality have a significant positive impact on product sales. The times saved have no direct impact on product sales. For different types of products, the times saved of experiential products has a significant impact on product sales, while the shop service quality information of search products has a greater impact on product sales. The influence of online observation learning on product sales will be significantly moderated by a combination of product type and market age. These findings not only offer important theoretical contributions to e-commerce research but also provide practical implications for online sellers and managers of social commerce platforms.     

基于建构主义理论的外语语音教学策略简

扼要介绍了建构主义学习理论的基本内涵，指出当前外语语音教学存在的主要问题及其根源，并有针对性地提出了基于建构主义学习理论的外语语音教学策略。     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.     

Escape dynamics: A continuous-time approximation

We extend a continuous-time approximation approach to the analysis of escape dynamics in economic models with constant gain adaptive learning. This approach is based on the application of the results of continuous-time version of large deviations theory to the linear diffusion approximation of the original discrete-time dynamics under learning. We characterize escape dynamics by analytically deriving the most probable escape point and mean escape time. The approximation is tested on the Phelps problem of a government controlling inflation while adaptively learning a misspecified Phillips curve, studied previously by Sargent (1999) and Cho et al. (2002) (henceforth, CWS), among others. We compare our results with simulations extended to very low values of the constant gain and show that, for the lowest gains, our approach approximates simulations relatively well. We express reservations regarding the applicability of any approach based on large deviations theory to characterizing escape dynamics for economically plausible values of constant gain in the model of CWS when escapes are not rare. We show that for these values of the gain it is possible to derive first passage times for learning dynamics reduced to one dimension without resort to large deviations theory. This procedure delivers mean escape time results that fit the simulations closely. We explain inapplicability of large deviations theory by insufficient averaging near the point of self-confirming equilibrium for relatively large gains which makes escapes relatively frequent, suggest the changes which might help approaches based on the theory to work better in this gain interval, and describe a simple heuristic method for determining the range of constant gain values for which large deviations theory could be applicable.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

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 construction of a composite index for general satisfaction in Turkey and the investigation of its determinants

The goal of public enterprises is to increase the wealth and happiness of individuals. This target lets them make the investment to fulfill these expectations. However, not all citizens in a country might have the same opportunities. This case requires comparing citizens who live in cities with regard to satisfaction levels. This study consists of two stages. In the first stage, a composite index called the General Satisfaction Index (GSI), which aims to explain the satisfaction levels of citizens living in cities, is obtained by using the Benefit of the Doubt method. Contrary to the previous studies, the satisfaction indicators based on the emotions and senses of citizens are used to construct a composite index. Satisfaction indicators’ contribution to GSI is ensured using the minimum weights. In the second stage of this study, socioeconomic determinants of GSI are investigated in both countrywide and regions by using regularized regression methods. In the result of the regression analyze, it is confirmed that there are different socioeconomic determinants of satisfaction levels for countrywide and each region. For this reason, it is proposed that policymakers should follow different politicizes in each region to increase the satisfaction levels of citizens in the country.     

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.