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This study aims to use geographical information on social media for public opinion risk identification during a crisis.

This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.

In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.

Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

In the last 3 decades, organization-wide programs and practices based on the Total Quality Management (TQM) philosophy have become central to continuous improvement (CI) strategy in both public and private enterprises. However, there is paradoxical evidence of TQM-firm performance linkage in non-Japanese contexts. This study presents a meta-analysis of empirical research on TQM-firm performance linkage and investigates the moderating influence of national cultural (NC) values on this relationship.

Meta-analytical procedures are adopted to analyse 364 effects accumulated from 135 independent samples across 31 nations, for 30,015 firm observations. Additionally, weighted least square (WLS) meta-regression is used to test the moderation effects of four NC dimensions based on the Global Leadership and Organizational Behavior Effectiveness (GLOBE) model.

The meta-analysis results reveal that the strengths of the association varied across five soft and hard TQM dimensions and three firm performance dimensions Meta-regression indicate that the effectiveness of the TQM program is high in cultures which reward collectivist behaviours, equity of power distribution and avoidance of ambiguity in rules/structures.

The study contributes to international operational management theory on cultural influences on the effectiveness of operations strategies and decisions.

The effectiveness of the Just-in-Time (JIT) production system in non-Japanese contexts is a topic of diverse findings. This study conducts a meta-analysis of empirical research on JIT and its relationship with performance, focusing on studies published since 1995. Additionally, it examines the moderating influence of National Culture (NC) values on JIT outcomes.

A total of 59 empirical studies with 211 effects and 17,008 observations from 18 countries are meta-analyzed. A meta-regression using hierarchical linear modeling (HLM) is performed to explore how four dimensions of National Culture (NC) moderate the impact. (viz. institutional collectivism, uncertainty avoidance future orientation, and power distance,) based on the Global Leadership and Organizational Behavior Effectiveness (GLOBE) culture model.

The meta-analysis results show that improved production efficiency, product quality and reduced wastes achieved through JIT deployments translate into the overall performance of organizations. The meta-regression results shed light on how local cultures influence the effectiveness of JIT across different countries.

The findings of meta-analysis have implications for multinational manufacturers in realizing efficacy of JIT. The research adds to the international operations management literature by examining how NC values influence strategies and decisions in operations management.

This study aims to investigate the influence of corporate social responsibility (CSR) on corporate sustainable performance (CSP) of small- and medium-sized enterprises (SMEs) by looking into the significance of mediating factors, namely, brand image (BI) and brand loyalty (BL), within the context of an emerging economy.

The authors conduct an extensive literature study on the subjects of CSR, BI and BL to assess their influence on the sustainable performance of SMEs in an emerging market. The study adopts a quantitative methodology. A total of 438 answers were obtained from a sample size of 513. The data of the SMEs in Vietnam was analyzed using the smart partial least squares structural equation modeling software, specifically version 3.3.2.

The results of the authors demonstrate notable and favorable correlations between CSR and CSP, CSR and BI and CSR and BL. Importantly, the findings contribute to existing knowledge by looking into the mediating influence of BI and BL in the relationship between CSR and CSP.

According to the authors’ understanding, a number of research have investigated the correlation between CSR and CSP within the realm of SMEs. Nevertheless, there is a scarcity of scholarly research examining the mediating function of BI and BL in this association. The study’s findings have important implications for entrepreneurs and senior management in effectively guiding their enterprises and improving their business strategies with an emphasis on sustainability in emerging markets. The outcome of this study has the potential to significantly contribute to SMEs in Vietnam as well as other emerging countries.

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

The purpose of this study is to test whether the prediction of firm performance can be enhanced by incorporating nonfinancial disclosures, such as narrative disclosure tone and corporate governance indicators, into financial predictive models.

Three predictive models are developed, each with a different set of predictors. This study utilises two machine learning techniques, random forest and stochastic gradient boosting, for prediction via the three models. The data are collected from a sample of 1,250 annual reports of 125 nonfinancial firms in Pakistan for the period 2011–2020.

Our results indicate that both narrative disclosure tone and corporate governance indicators significantly add to the accuracy of financial predictive models of firm performance.

Our results offer implications for the restoration of investor confidence in the highly uncertain Pakistani market by establishing nonfinancial disclosures as reliable predictors of future firm performance. Accordingly, they encourage investors to pay more attention to these disclosures while making investment decisions. In addition, they urge regulators to promote and strengthen the reporting of such nonfinancial information.

This study addresses the neglect of nonfinancial disclosures in the prediction of firm performance and the scarcity of corporate governance literature relevant to the use of machine learning techniques.

The paper aims to investigate differences in non-professional and professional stock investors’ trust in and tendency to follow financial analysts’ buy and sell recommendations.

Online experiment conducted in Sweden in March 2022 comparing non-professional private investors (n = 80), professional investors (n = 33), and master students in finance (n = 28). Information was presented about four company stocks listed on the New York stock exchange. Two stocks were buy-recommended and two stocks sell-recommended by financial analysts. For one stock of each type, the recommendation was presented to participants. Dependent variables were predictions of the stock price after three months, ratings of confidence in the predictions and choices of holding, buying or selling the stock. Ratings were also made of the importance of presented stock-related information as well as trust in analysts’ skill and integrity.

More positive return predictions were made of buy-recommended than sell-recommended stocks. Non-professionals and to some degree finance students tended to trust financial analysts more than professional investors did and they were more influenced by the presentation of the buy recommendations. All groups made too optimistic return predictions, but the professionals were less confident in their predictions, more likely to sell the stocks and lost less on their investments.

A new finding is that non-professional stock investors are more likely than professional stock investors to trust financial analysts and follow their recommendations. It suggests that financial analysts’ recommendations influence non-professional investors to take unmotivated investment risks. Non-professionals in the stock market should hence be advised to exercise more caution in following analysts’ recommendations.

We examine the relationship between macroeconomic news and fund price jumps, using high-frequency 5-min intraday data for Exchange Traded Funds (ETFs) and Listed Open-end Funds (LOFs) from 2019 to 2020.

We utilize the non-parametric jump test known as the LM method to detect fund price jumps. In addition, we perform Logistic regression to analyze the relationship between macroeconomic news and fund price jumps. Moreover, we use multiple linear regression to explore the relationship between fund price jumps and subsequent returns.

The probability of price jumps increases by 22.56% when macroeconomic news is released. Moreover, the returns associated with news-driven price jumps display a reversal pattern, and there is an asymmetric relationship in subsequent returns following macroeconomic shocks. Specifically, funds tend to exhibit lower returns after news-driven price jumps compared to those that are not influenced by news events.

In today's digital age, investors have unprecedented access to a wealth of information through the Internet and various communication platforms. News and market data can be instantly accessed and disseminated, allowing for swift dissemination of information to investors worldwide. However, despite this enhanced accessibility, investors continue to exhibit overreactions or underreactions to new information.

Macroeconomic news release provide crucial insights into the overall health and performance of the economy. By monitoring and analyzing these indicators, investors can gain valuable information that can guide their investment decisions. Furthermore, by fostering a transparent and reliable information disclosure systems, governments can play a critical role in ensuring the stability and transparency of the funds market.

The paper utilizes 5-min high-frequency data from funds and incorporates a comprehensive macroeconomic news information database. These methodological choices enhance the precision and reliability of the analysis, allowing for a more nuanced understanding of the relationship between macroeconomic news releases and fund price jumps.