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Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

Recent disruptions have sparked concern about building a resilient and sustainable manufacturing supply chain. While artificial intelligence (AI) strengthens resilience, research is needed to understand how cloud adoption can foster integration, collaboration, adaptation and sustainable manufacturing. Therefore, this study aimed to unleash the power of cloud adoption and AI in optimizing resilience and sustainable performance through collaboration and adaptive capabilities at manufacturing firms.

This research followed a deductive approach and employed a quantitative method with a survey technique to collect data from its target population. The study used stratified random sampling with a sample size of 1,279 participants working in diverse manufacturing industries across California, Texas and New York.

This research investigated how companies can make their manufacturing supply chains more resilient and sustainable. The findings revealed that integrating the manufacturing supply chains can foster collaboration and enhance adaptability, leading to better performance (hypotheses H1-H7, except H5). Additionally, utilizing artificial intelligence helps improve adaptability, further strengthening resilience and sustainability (H8-H11). Interestingly, the study found that internal integration alone does not significantly impact collaboration (H5). This suggests that external factors are more critical in fostering collaboration within the manufacturing supply chain during disruptions.

This study dives into the complex world of interconnected factors (formative constructs in higher order) influencing manufacturing supply chains. Using advanced modeling techniques, it highlights the powerful impact of cloud-based integration. Cloud-based integration and artificial intelligence unlock significant improvements for manufacturers and decision-makers by enabling information processes and dynamic capability theory.

Drawing on the integrative perspective of the technology acceptance model (TAM) and theory of planned behaviour (TPB) and extending it further by examining the role of organisational facilitators and perceived HR effectiveness in this integrative perspective, we examine HR professionals’ AI-augmented HRM (HRM^(AI)) acceptance in this research.

The data (N=375) were collected from HR professionals working in different organisations in India. Structural equation modelling (SEM) was employed to analyse the data.

The results of the study suggest that along with organisational facilitator antecedents to the relevant components of both TAM and TPB, perceived HR effectiveness also enhanced the HRM^(AI) acceptance levels of HR professionals.

The research findings are expected to contribute to the understanding of the factors that influence the acceptance of AI-augmented HRM in organizations. The results may also help organisations to identify the facilitators that can enhance the adoption and implementation of AI-augmented HRM by HR professionals. Finally, the study provides a composite TAM-TPB theoretical framework that can guide future research on the acceptance of AI-augmented HRM.

To the best of our knowledge, this is one of the first attempts to factor in the effect of contextual factors (i.e. organisational facilitators and perceived HR effectiveness) in the TAM and TPB equations.

This study aims to examine the factors influencing Filipinos’ intention to adopt mobile wallets to pay social health insurance premiums.

The authors used an integrative model framework using the key indicators from the lenses of the technology acceptance model, the unified theory of acceptance and usage of technology model 2 and the theory of planned behavior with trust serving as a mediator. The sample size was calculated using an inverse square root ratio composed of 624 survey participants purposively identified across selected cities in the Philippines. The formulated hypotheses were examined using partial least squares structural equation modeling and deep learning–based artificial neural networks.

The results substantiate this study’s integrative model explaining the positive influence and relative importance of perceived usefulness, habit and subjective norms in developing trust in mobile wallet applications. Moreover, health insurance literacy, subjective norms and trust positively and significantly drive individuals’ intentions to adopt mobile wallets as a payment platform for social health insurance premiums. The mediation analysis also exemplified that trust positively mediates the influences of technology acceptance factors such as perceived usefulness, habit and subjective norms in the intention of individuals to adopt mobile wallet applications in social health insurance payment of premiums.

This study is a pioneering study in the Philippine context that used an integrative model to predict and explain the relative importance of predictors of Filipino intentions to adopt mobile wallets as a payment platform for social health insurance premiums.

Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also increasingly using PLS-SEM, this growing interest calls for guidance.

Based on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals.

The use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method.

This research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals.

The results of this analysis guide researchers who use the PLS-SEM method for their quality management studies.

This is the first article to systematically review the use of PLS-SEM in the quality management discipline.

The purpose of this study was to investigate the roles of employee experience and top management commitment in the relationship between human resource (HR) records management culture and HR records privacy control in organisations in Ghana.

Structural equation modelling was used in analysing the data. Following the specification of the model, three main types of analyses were carried out. They were reflective measurement model analyses to test reliability and validity; formative measurement model analyses to test redundancy, collinearity, significance and relevance of the lower-order constructs; and structural model analyses to ascertain the explanatory and predictive powers of the model, significance of the hypotheses and their effect sizes.

The study confirmed that communication, privacy awareness and training and risk assessment are dimensions of HR records management culture. Concerning the hypotheses, it was established that HR records management culture is related to HR records privacy control. Also, the study showed that employee experience positively moderated the relationship HR records management culture has with HR records privacy control. However, top management commitment negatively moderated the relationship HR records management culture has with HR records privacy control.

Organisations committed to the privacy control of HR records need to ensure the retention of their employees, as the longer they stay with the organisation, the more they embody the HR records management culture which improves the privacy control of HR records. For top management commitment, it should be restricted to providing strategic direction for HR records privacy control, as the day-to-day influence of top management commitment on the HR records management culture does not improve the privacy control of HR records.

This study demonstrates that communication, privacy awareness and training and risk assessment are dimensions of HR record management culture. Also, the extent of employee experience and top management commitment required in the relationship between HR records management culture and HR records privacy control is revealed.

Supply chain (SC) management is more challenging than ever. Significantly, the pandemic has provoked global and economic destruction that appeared in the manufacturing industry as a “black swan.” Therefore, the purpose of this study was to examine the role of information processing and digital supply chain in supply chain resilience through supply chain risk management.

This study examines SC risk management and resilience from an information processing theory perspective. The authors used data collected from 251 SC professionals in the manufacturing industry, and the authors used a quantitative method to analyze the data. The data was analyzed using partial least squares-structural equation modeling. To confirm the higher-order measurement model, the authors used SmartPLS version 4 software.

This study found that information processing capability (disruptive orientation and visibility in high-order) and digital SC significantly and positively affect SC risk management and resilience. Similarly, SC risk management positively mediates the relationship between information processing capability and digital SC. However, information processing capability was found to have a more substantial effect on SC risk management than the digital SC.

This study has both academic and practical contributions. It contributed to existing information processing theory, and manufacturing firms can improve their performance by proactively responding to SC disruptions by recognizing the pivotal role of study variables in risk management for a resilient SC.

The conceptual model of this study is based on information processing theory, which asserts that synchronizing information processing capabilities and digital SCs allows a firm to deal with unplanned events. SC disruption orientation and visibility are considered risk controllers as they allow the firms to be more proactive. An integrated model of conceptualizing the disruption orientation, visibility (higher-order) and digital SC with information processing theory makes this research novel.

Studies on social entrepreneurial intention have been increasing in recent years due to the growing concerns of young people on social and sustainability issues. Although social entrepreneurship is gaining momentum in emerging economies, only limited studies explore antecedents of social entrepreneurial intention in these countries, particularly in Southeast Asia. This paper aims to integrate the concept of entrepreneurship competence with the theory of planned behavior and examine the interrelationship between antecedents of this theory.

A survey of university students was used to validate the theory of planned behavior in explaining social entrepreneurial intention. Samples were selected using purposive sampling involving university students who had been exposed to social entrepreneurship incubators. The 372 valid responses were analyzed using partial least square structural equation modeling.

This study confirms the applicability of the theory of planned behavior to explain social entrepreneurial intention, even though not all attitudes toward social entrepreneurship orientation were significant. Out of five orientations, only attitudes toward social innovation significantly directed social entrepreneurial intention. This study validates that subjective norms and perceived behavioral control determine attitudes toward social entrepreneurship orientation. The roles of education and prior experience in providing entrepreneurship competence also positively shape individuals’ attitudes toward social entrepreneurship orientation and perceived behavioral control.

Although perceived behavioral control and entrepreneurial self-efficacy are frequently interchangeable, these concepts are independent. This study does not include social entrepreneurial self-efficacy by only examining the effect of entrepreneurship competence on perceived behavioral control. Data were limited to university students who have been exposed to social entrepreneurship incubators, which may not fully reflect the tendency of general Indonesian youths to become social entrepreneurs.

As entrepreneurship competence is essential in stimulating social entrepreneurial intention through the formation of attitudes toward social innovation and perceived behavioral control, the government and the universities can collaborate in establishing a supportive social entrepreneurship ecosystem within and outside the universities. This ecosystem may equip youths with essential knowledge and resources as well as complement subjective norms.

Studies of social entrepreneurial intention have mostly applied psychological theories; however, they rarely consider the impact of entrepreneurship education as the driver of social entrepreneurial intention. Studies of social entrepreneurial intention from psychological and educational perspectives tend to run in parallel. This study integrates entrepreneurship competence into the theory of planned behavior to explain social entrepreneurial intention. In addition, while social entrepreneurship is believed to consist of multiple orientations, not many studies have deliberated on this. This study reflects these multiple social entrepreneurship orientations in the attitudes toward social entrepreneurship.

The importance of robotics and automation (R&A) in delivering a safe built environment cannot be overemphasised. This is because R&A systems can execute a hazardous job function that the construction workforce may not execute. Based on this knowledge, this study aims to present the result of an assessment of the impediments to the deployment of R&A for a safe and healthy construction environment.

This study adopted a post-positivist philosophical stance, using a quantitative research approach and a questionnaire administered to construction professionals in South Africa. The data gathered were analysed using frequency, percentage, mean item score, Kruskal–Wallis H-test, exploratory factor analysis and partial least square structural equation modelling (SEM).

This study revealed that the impediments to the deployment of R&A could be grouped into: industry, technology, human and cost-related factors. However, SEM assessment showed that only the industry, human and cost-related factors would significantly impact attaining specific health and safety-related outcomes.

The findings offer valuable benefits to construction organisations as the careful understanding of the identified impeding factors can help lead to better deployment of R&A and the attainment of its inherent safety benefits.

This study attempts to fill the gap in the shortage of literature exploring the deployment of R&A for a safe construction environment, particularly in developing countries like South Africa, where such studies are non-existent. This paper, therefore, offers a theoretical backdrop for future works on R&A deployment, particularly in developing countries where such a study has not been explored.