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Article
Publication date: 2 October 2017

Ajay Kumar Dhamija, Surendra S. Yadav and P.K. Jain

The purpose of this paper is to find out the best method for forecasting European Union Allowance (EUA) returns and determine its price determinants. The previous studies…

Abstract

Purpose

The purpose of this paper is to find out the best method for forecasting European Union Allowance (EUA) returns and determine its price determinants. The previous studies in this area have focused on a particular subset of EUA data and do not take care of the multicollinearities. The authors take EUA data from all three phases and the continuous series, adopt the principal component analysis (PCA) to eliminate multicollinearities and fit seven different homoscedastic models for a comprehensive analysis.

Design/methodology/approach

PCA is adopted to extract independent factors. Seven different linear regression and auto regressive integrated moving average (ARIMA) models are employed for forecasting EUA returns and isolating their price determinants. The seven models are then compared and the one with minimum (root mean square error is adjudged as the best model.

Findings

The best model for forecasting the EUA returns of all three phases is dynamic linear regression with lagged predictors and that for forecasting EUA continuous series is ARIMA errors. The latent factors such as switch to gas (STG) and clean spread (capturing the effects of the clean dark spread, clean spark spread, switching price and natural gas price), National Allocation Plan announcements events, energy variables, German Stock Exchange index and extreme temperature events have been isolated as the price determinants of EUA returns.

Practical implications

The current study contributes to effective carbon management by providing a quantitative framework for analyzing cap-and-trade schemes.

Originality/value

This study differs from earlier studies mainly in three aspects. First, instead of focusing on a particular subset of EUA data, it comprehensively analyses the data of all the three phases of EUA along with the EUA continuous series. Second, it expressly adopts PCA to eliminate multicollinearities, thereby reducing the error variance. Finally, it evaluates both linear and non-linear homoscedastic models incorporating lags of predictor variables to isolate the price determinants of EUA.

Details

Journal of Advances in Management Research, vol. 14 no. 4
Type: Research Article
ISSN: 0972-7981

Keywords

Abstract

Details

Applying Partial Least Squares in Tourism and Hospitality Research
Type: Book
ISBN: 978-1-78756-700-9

Book part
Publication date: 21 November 2014

Purevdorj Tuvaandorj and Victoria Zinde-Walsh

We consider conditional distribution and conditional density functionals in the space of generalized functions. The approach follows Phillips (1985, 1991, 1995) who…

Abstract

We consider conditional distribution and conditional density functionals in the space of generalized functions. The approach follows Phillips (1985, 1991, 1995) who employed generalized functions to overcome non-differentiability in order to develop expansions. We obtain the limit of the kernel estimators for weakly dependent data, even under non-differentiability of the distribution function; the limit Gaussian process is characterized as a stochastic random functional (random generalized function) on the suitable function space. An alternative simple to compute estimator based on the empirical distribution function is proposed for the generalized random functional. For test statistics based on this estimator, limit properties are established. A Monte Carlo experiment demonstrates good finite sample performance of the statistics for testing logit and probit specification in binary choice models.

Details

Essays in Honor of Peter C. B. Phillips
Type: Book
ISBN: 978-1-78441-183-1

Keywords

Article
Publication date: 26 August 2022

William Harly and Abba Suganda Girsang

With the rise of online discussion and argument mining, methods that are able to analyze arguments become increasingly important. A recent study proposed the usage of…

Abstract

Purpose

With the rise of online discussion and argument mining, methods that are able to analyze arguments become increasingly important. A recent study proposed the usage of agreement between arguments to represent both stance polarity and intensity, two important aspects in analyzing arguments. However, this study primarily focused on finetuning bidirectional encoder representations from transformer (BERT) model. The purpose of this paper is to propose convolutional neural network (CNN)-BERT architecture to improve the previous method.

Design/methodology/approach

The used CNN-BERT architecture in this paper directly uses the generated hidden representation from BERT. This allows for better use of the pretrained BERT model and makes finetuning the pretrained BERT model optional. The authors then compared the CNN-BERT architecture with the method proposed in the previous study (BERT and Siamese-BERT).

Findings

Experiment results demonstrate that the proposed CNN-BERT is able to achieve a 71.87% accuracy in measuring agreement between arguments. Compared to the previous study that achieve an accuracy of 68.58%, the CNN-BERT architecture was able to increase the accuracy by 3.29%. The CNN-BERT architecture is also able to achieve a similar result even without further pretraining the BERT model.

Originality/value

The principal originality of this paper is the proposition of using CNN-BERT to better use the pretrained BERT model for measuring agreement between arguments. The proposed method is able to improve performance and also able to achieve a similar result without further training the BERT model. This allows separation of the BERT model from the CNN classifier, which significantly reduces the model size and allows the usage of the same pretrained BERT model for other problems that also did not need to finetune their BERT model.

Details

International Journal of Web Information Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 10 April 2019

Patricio Esteban Ramírez-Correa, Elizabeth E. Grandón and Jorge Arenas-Gaitán

The purpose of this paper is to determine differences in customers’ personal disposition to online shopping.

1107

Abstract

Purpose

The purpose of this paper is to determine differences in customers’ personal disposition to online shopping.

Design/methodology/approach

The research model was proposed based on two types of purchases (hedonic vs utilitarian) and on personal traits of individuals against technology throughout the Technology Readiness Index (TRI) 2.0. Generation and gender were considered to evaluate their impact on the type of purchases. Consumers’ data were collected in Chile through 788 face-to-face surveys. The partial least squares approach was used to test the research model.

Findings

The findings show that optimism and discomfort influence online shopping. Moreover, generation and gender moderate the relationship between the dimensions of the TRI and online purchases.

Originality/value

The contributions of this study are threefold. The analysis of personal traits and the type of purchases contribute to the existing literature on consumer behavior and e-commerce, and provide some insights for marketers to identify segmentation strategies by analyzing the gender and generation of individuals. Second, this study contributes to examining the stability and invariances of the TRI 2.0 instrument, which has not been fully revised in less developed countries. Third, this study adds to the existing body of research that argues that demographic variables are not sufficient to understand technology adoption by individuals by including psychological variables.

Details

Industrial Management & Data Systems, vol. 119 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 23 December 2021

Mohammed A. Al-Sharafi, Noor Al-Qaysi, Noorminshah A. Iahad and Mostafa Al-Emran

While there is an abundant amount of literature studies on mobile payment adoption, there is a scarce of knowledge concerning the sustainable use of mobile payment…

Abstract

Purpose

While there is an abundant amount of literature studies on mobile payment adoption, there is a scarce of knowledge concerning the sustainable use of mobile payment contactless technologies. As those technologies are mainly concerned with security and users' trust, the question of how security factors and trust can influence the sustainable use of those technologies within and beyond the COVID-19 pandemic is still unanswered. This research thus develops a theoretical model based on integrating the protection motivation theory (PMT) and the expectation-confirmation model (ECM), extended with perceived trust (PT) to explore the sustainable use of mobile payment contactless technologies.

Design/methodology/approach

The developed model is evaluated based on data collected through a web-based survey from 523 users who used contactless payment technologies. Unlike the existing literature, the collected data were analyzed using a hybrid structural equation modeling-artificial neural network (SEM-ANN) technique.

Findings

The data analysis results reinforced all the proposed relationships in the developed model. The sensitivity analysis results showed that PT has the largest impact on the sustainable use of mobile payment contactless technologies with 97.2% normalized importance, followed by self-efficacy (SE) (77%), satisfaction (72.1%), perceived vulnerability (PV) (48.9%), perceived usefulness (PU) (48.2%), perceived severity (PS) (40.7%), response efficacy (RE) (28.7%) and response costs (RCs) (24.1%).

Originality/value

The originality of this research lies behind the development of an integrated model based on PMT and ECM to understand the sustainable use of mobile payment contactless technologies. The study provides several managerial implications for decision-makers, policy-makers and service providers to ensure the sustainability of those contactless technologies within and beyond the COVID-19 pandemic.

Details

International Journal of Bank Marketing, vol. 40 no. 5
Type: Research Article
ISSN: 0265-2323

Keywords

Article
Publication date: 15 May 2019

Haoqiang Shi, Shaolin Hu and Jiaxu Zhang

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value…

Abstract

Purpose

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

Design/methodology/approach

In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope.

Findings

By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds.

Practical implications

The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope.

Originality/value

In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 8 February 2021

Emrah Dokur, Cihan Karakuzu, Uğur Yüzgeç and Mehmet Kurban

This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural…

Abstract

Purpose

This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process.

Design/methodology/approach

The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (Nh) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated.

Findings

It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and Nh = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time.

Originality/value

The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 40 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 18 July 2022

Gautam Srivastava, Surajit Bag, Muhammad Sabbir Rahman, Jan Harm Christiaan Pretorius and Mohammad Osman Gani

The negative influence of gamification on online communities has received little attention in the available literature. The study examines the adverse effects of…

Abstract

Purpose

The negative influence of gamification on online communities has received little attention in the available literature. The study examines the adverse effects of gamification during engaging in online communities.

Design/methodology/approach

Gap-spotting methods were used to develop the research questions, followed by model development using the social exchange and social-network theories. Data were collected from 429 samples. The study applied partial least squares structural equation modeling to test the research hypotheses followed by ANN application.

Findings

The study identified five factors related to gamification that have a significant adverse effect on the mental and emotional well-being of the users. Furthermore, the results of PLS-SEM were then compared through an artificial neural network (ANN) analytic process, revealing consistency for the model. This research presents a theoretical contribution by providing critical insights into online gamers' mental and emotional health. It implies that gamification can even bring mental and emotional disturbance. The resulting situation might lead to undesirable social consequences.

Practical implications

The result highlights the managerial and social relevance from the perspective of a developing country. As respondents are becoming more engrossed in online gaming, managers and decision-makers need to take preventive measures to overcome the dark side of online gaming.

Originality/value

The present study shows that the dark side of gamification has some adverse effects on human mental and emotional health. The study's findings can be used to improve gamification strategies while engaging online communities.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 25 June 2021

Ai-Fen Lim, Voon-Hsien Lee, Pik-Yin Foo, Keng-Boon Ooi and Garry Wei–Han Tan

In today’s globalized and heavily industrialized economy, sustainability issues that negatively affect the human population and external environment are on the rise. This…

Abstract

Purpose

In today’s globalized and heavily industrialized economy, sustainability issues that negatively affect the human population and external environment are on the rise. This study aims to investigate a synergistic combination of supply chain management and quality management practices in strengthening the sustainability performance of Malaysian manufacturing firms.

Design/methodology/approach

A total sample of 177 usable surveys was collected. Given the contributions and acceptability of the artificial neural network (ANN) approach in evaluating the findings of this study, this study uses ANN to measure the relationship between each predictor (i.e. supply chain integration [SCI], quality leadership [QL], supplier focus [SF], customer focus (CF) and information sharing [IS]) and the dependent variable (i.e. sustainability performance). Via sensitivity analysis, the relative significance of each predictor variable is ranked based on the normalized importance value.

Findings

The sensitivity analysis indicates that CF has the greatest effect on sustainability performance (SP) with 100% normalized relative importance, followed by QL (75%), IS (61.5%), SF (57.3%) and SCI (46.7%).

Originality/value

The findings of this study have the potential to provide valuable guidance and insights that can help all manufacturing firms enhance their SP from the optimum combination of the selected SCQM practices with a focus on sustainability.

Details

Supply Chain Management: An International Journal, vol. 27 no. 5
Type: Research Article
ISSN: 1359-8546

Keywords

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