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Some aspects of technology behaviour remain unclear, such as the generation of technology use and engagement. So, this study aims to address the following question: does engagement with technology drive the use of technology?or does the use of technology create the engagement with technology?

Based on the uses and gratifications theory, this study compares three alternative competing models that explain technology behaviour on a sample of 715 individuals, using the selection criteria proposed by Mathieson. A comprehensive analysis and comparison of three structural competing models on technology behaviour, namely, “use-and-engagement”, “use-to-engagement” and “engagement-to-use”, are presented.

Findings show that the “use-and-engagement” model provides a better explanation of technology behaviour and is superior to predict technology behaviour, suggesting that both technology engagement and use could be considered as consequences.

This study’s major contribution is the empirical examination of three structural competing models and the selection of the best explaining model of technology behaviour.

Algunos aspectos del comportamiento tecnológico permanecen sin aclarar, como la creación del uso e implicación hacia la tecnología. Así que abordamos la siguiente pregunta: ¿La implicación con la tecnología impulse su uso?, o ¿es el uso de la tecnología el que impulse la implicación?.

Basándonos en la Teoría de los Usos y Gratificaciones se han comparado tres modelos alternativos que compiten entre sí para explicar el comportamiento tecnológico, en una muestra de 715 individuos utilizando el criterio de selección propuesto por Mathieson. Se presenta un análisis y una comparación exhaustive de tres modelos estructurales competitivos sobre el comportamiento tecnológico, que son “uso-e-implicación”, “uso-para-la implicación” e “implicación-para-el uso”.

Los resultados muestran que el modelo “uso-e-implicación” proporciona la mejor explicación del comportamiento tecnológico y es superior para predecir el comportamiento tecnológico, lo que sugiere que tanto la implicación como el uso de la tecnología podrían considerarse como consecuencias.

Nuestra principal contribución es el análisis empírico de tres modelos estructurales competitivos y la selección del mejor de ellos para explicar el comportamiento tecnológico.

技术行为的某些方面仍然不清楚, 例如技术使用和参与的产生。因此, 我们意在解决以下问题：对技术的参与是否推动了技术的使用,还是技术的使用创造了技术的参与？

基于 “使用与满足 “理论, 我们使用马蒂森提出的选择标准, 在715人的样本上比较了三种解释技术行为的替代竞争模型。即我们对三个关于技术行为的结构性竞争模型, “使用和参与”、“使用到参与 ”和 “参与到使用 ”进行了综合的分析和比较。

研究结果显示, “使用和参与 “模型更好的解释了技术行为, 并且其优于预测技术行为, 这表明技术参与和使用都可以被认为是后果。

我们的主要贡献是对三个结构性竞争模型进行了实证检验, 并选择了对技术行为的最佳解释模型。

This study proposes a Bayesian approach to analyze structural breaks and examines whether structural changes have occurred, at the onset of civil war, with respect to economic development and population during the period from 1945 to 1999.

In the Bayesian logit regression changepoint model, parameters of covariates are allowed to shift individually, regime transitions can move back and forth, and the model is applicable to cross-sectional, time-series data.

Contrary to popular belief that the causal process of civil war changed with the end of the Cold War, the empirical analysis shows that the regression relationships between civil war and economic development, as well as between civil war and population, remain quite stable during the study period.

This is the first to develop a Bayesian logit regression changepoint model and to apply it to studies of economic development and civil war.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns and volatility.

The competing models are the autoregressive moving average (ARMA) model and autoregressive fractional integrated moving average (ARFIMA) model for house price returns. For house price volatility, the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model is competing with the fractional integrated GARCH (FIGARCH) and component GARCH (CGARCH) models.

Results reveal that, for modelling Finnish house price returns, the data set under study drives the performance of ARMA or ARFIMA model. The EGARCH model stands as the leading model for Finnish house price volatility modelling. The long memory models (ARFIMA, CGARCH and FIGARCH) provide superior out-of-sample forecasts for house price returns and volatility; they outperform their short memory counterparts in most regions. Additionally, the models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances.

The research results have vital implications, namely, portfolio allocation, investment risk assessment and decision-making.

To the best of the author’s knowledge, for Finland, there has yet to be empirical forecasting of either house price returns or/and volatility. Therefore, this study aims to bridge that gap by comparing different models’ performance in modelling, as well as forecasting the house price returns and volatility of the studied market.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.

This study aims to validate a model for estimating platoon delay due to pedestrian crossing for use in Kuwait City.

The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically. Using video footage to observe the site, several variables were collected, and a model was used to calculate the delays suffered by the vehicles because of pedestrian crossing. The model was validated using the actual footage and manual observation to measure the delays.

The model showed a good match fit to the observed data, as the average delays differed by 22.5% between the two methods. Following the comparison, a sensitivity analysis was made on three variables: the acceleration rate, deceleration rate, as well as the pedestrian walking time. The analysis has shown that deceleration rate has approximately twice the effect on the model than the acceleration rate has. It has also shown that the pedestrian walking time has a major effect on the model, in an almost one-to-one correlation. A 50% change of the pedestrian walking time is associated with approximately 50% change in the model’s output delay.

A model for estimating platoon delay because of pedestrian crossing was validated for use in Kuwait City. The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically.

The purpose of the study is to highlight the relevance of reactance theory for modeling consumer responses to COVID restrictions. The study also aims to critically evaluate the appropriateness of the most established reactance model (the intertwined model) for studying reactance specifically in relation to freedom threats arising from measures aimed at combatting the spread of the pandemic.

Following a conceptual analysis of the intertwined model of reactance, structural equation modeling is applied to Rain's (2013) meta-analytic data to compare the model to alternative model specifications.

The analysis reveals both conceptual and statistical shortcomings of the intertwined model of reactance in its current/traditional form. It also draws attention to other model specifications that provide just as good statistical fit and offer promising alternative ways of modeling reactance in a COVID context.

The study is the first attempt to explicitly discuss conceptual and statistical problems associated with the most widely accepted model of reactance, illustrate these issues with specific reference to consumer reactions to COVID restrictions, identify alternative promising model specifications and suggest a respecification of the intertwined model.

To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.