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This study aims to (1) investigate the influence of firms’ social media utilization on performance through supply chain agility, (2) examine the mediating role of supply chain agility and (3) explore the indirect effect of social media utilization on operational performance via supply chain agility as knowledge transfer increases.

A survey of 298 Chinese manufacturing firms was conducted to assess the proposed relationships, employing moderated mediation analysis with Andrew Hayes (2017) PROCESS macro.

Social media utilization indirectly enhances operational performance through supply chain agility, supporting our mediation hypothesis (H1). Additionally, knowledge transfer moderates the positive impact of social media utilization on supply chain agility (H2). The moderated mediation analysis reveals that the mediating effect of supply chain agility on operational performance is stronger at higher levels of knowledge transfer (H3), shedding light on the intricate relationships between these variables and providing insights for businesses seeking to leverage social media and knowledge transfer to enhance supply chain resilience and operational performance.

This study empirically investigates the role of social media utilization in supply chains within the digital age. We explore how social media enhances supply chain agility and knowledge transfer, highlighting its transformative potential for real-time communication, responsiveness and collaboration across networks. By integrating dynamic capability theory with contemporary digital practices, we demonstrate how leveraging digital platforms alongside traditional supply chain processes can significantly improve manufacturing efficiency. This research bridges existing gaps in the literature and provides valuable insights for businesses navigating complex, rapidly changing environments in the era of digital transformation.

This paper aims to identify the variables responsible for classifying the investors into risk takers (RT) and risk avoiders (RA) across their economic perspectives.

The research offers a novel and unobtrusive measure of classifying investors into RT and RA based on a set of financial risk tolerance (FRT) questions. The authors have investigated the causes of discrimination across economic perspectives over a sample of 552 investors exposed to market risk.

The authors identify that out of the total of 11 risk assessment variables, only three are responsible for classifying investors into RA and RT. The variables are risk return trade-off, comfort level dealing with risk, and understanding short-term volatility. Financial literacy is considered as an emerging cause of discrimination. Further, the authors highlight the most striking finding to be the discriminating factors across wealth and source of income of the investors.

Existing research on FRT can be loosely segregated into three groups: the relationship between an individual's financial and non-FRT, estimation of FRT score (FRTS), and perceived self-assessed FRTS. The current research roughly falls into the third category of study where the authors have not only studied the self-assessed risk tolerance but also evaluated the predictors. Most of the studies have focussed on estimating self-assessed FRT with the help of one direct question to the respondent. However, the uniqueness of this study is that the researchers have used an instrument comprising a series of direct and indirect questions that can easily estimate the self-assessed risk perception and also discriminate the role of the economic factors that have any impact on self-assessed FRTS.

This study uses data from the National Financial Capability Study to examine the financial vulnerability of Asian American and Pacific Islander (AAPI) adults relative to that of other major racial/ethnic groups in the United States across the past decade and within the AAPI population, examining how vulnerability varied across AAPI adults of East Asian, South Asian, Southeast Asian, and Pacific Islander heritage.

The study uses four waves (2012, 2015, 2018 and 2021) of the State-by-State National Financial Capability Study (NFCS) and the 2021 NFCS AAPI Oversample dataset. Financial vulnerability was estimated using five binary indicators: (1) An inability to come up with $2,000, (2) An experience of overdraw, (3) A lack of emergency fund savings, (4) Difficulty paying bills and expenses, and (5) Credit card revolving. A financial vulnerability index was also created using the binary indicators. Logistic regression analyses were conducted on binary indicators and an OLS regression was additionally conducted on the aggregated financial vulnerability index.

Results show that, overall, AAPI respondents reported the lowest levels of financial vulnerability relative to White respondents, Black respondents, Hispanic respondents, and those of another race or ethnicity. However, using the 2021 datasets, we found that within the AAPI population, financial vulnerability varied widely by heritage, with those of East Asian heritage reporting less vulnerability than AAPI adults of other studied heritage groups.

These results provide insights into the financial well-being of AAPI households, particularly amidst the COVID-19 pandemic, and present initial evidence of the significant disparities that exist within this heterogenous community. This study provides valuable insights for researchers, educators, policymakers, and financial practitioners.

Around the globe, low retirement savings threaten the economic well-being of large portions of the population. To better understand what promotes retirement sufficiency, we investigate variables that correlate with retirement savings behaviors.

Using the 2021 National Financial Capability Study data, we examine factors correlated with having a retirement plan, contributing to a retirement plan and avoiding the depletion of retirement savings.

While strong financial behavior and actual financial literacy are each connected to retirement plan participation, the link attributed to strong financial behavior is nearly twice as strong as that for actual financial literacy. Strong financial behavior correlates strongly with leaving retirement savings in place. Having a financial literacy blind spot (i.e. not knowing that one does not know about financial literacy) correlates strongly with retirement savings depletion. Financial anxiety does not correlate with retirement plan participation or depletion.

Our measure of strong financial behavior explains much more variation in retirement savings than variables commonly explored in the retirement literature. Individuals facing income constraints without a financial literacy blind spot are less likely to deplete their retirement savings. Conversely, those with a financial literacy blind spot tend to deplete their retirement savings regardless of their financial vulnerability or strength. Our findings hold even when restricting the sample to households with incomes below the median ($75,000), as well as above the median, indicating that policies targeting non-income variables could enhance retirement outcomes.

This study examines the relationship between changes in retirement status and social support and their associations with the life satisfaction of older adults, with a focus on potential differences across income levels.

We analyzed various work-retirement pathways using retirement status data from two waves (2016 and 2018) of the biennial Health and Retirement Study. We examined the relationship between these pathways and life satisfaction, incorporating social support from close relationships, including those with a spouse or partner, children, immediate family members and friends. A subgroup analysis was performed based on household income levels.

The study found that completely or partially retired individuals reported higher life satisfaction than those who continued working. Those who had returned to work also experienced higher life satisfaction, particularly among the low-income group. Among the middle-income group, individuals transitioning toward retirement reported greater life satisfaction than those still working. Across all subgroups, closeness with a spouse or partner and having close friends were positively related to life satisfaction. However, a lack of close relationships with immediate family members was linked to lower life satisfaction in the low- and middle-income groups, though this was not found in the high-income group.

These findings have theoretical, policy, and practical implications for older populations, mainly retirees or those nearing retirement. The study suggests strategies to assist older adults in navigating diverse retirement pathways, such as fostering strong social connections and offering flexible or phased retirement programs to ease the transition.

Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they harbour risks like schedule delays and increased project costs, underlining the importance of strategic RFI management in construction projects. Despite this, a lack of literature dissecting RFI processes impedes a full understanding of their intricacies and impacts. This study aims to bridge the gap through a comprehensive literature review, delving into RFI intricacies and implications, while emphasising the necessity for strategic RFI management to prevent project risks.

This research study systematically reviews RFI-related papers published between 2000 and 2023. Accordingly, the review discusses key themes related to RFI management, yielding best practices for industry stakeholders and highlighting research directions and gaps in the body of knowledge.

Present RFI management platforms exhibit deficiencies and lack analytics essential for streamlined RFI processing. Complications arise in building information modelling (BIM)-enabled projects due to software disparities and interoperability hurdles. The existing body of knowledge heavily relies on manual content analysis, an impractical approach for the construction industry. The proposed research direction involves automated comprehension of unstructured RFI content using advanced text mining and natural language processing techniques, with the potential to greatly elevate the efficiency of RFI processing.

The study extends the RFI literature by providing novel insights into the problemetisation with the RFI process, offering a holistic understanding and best practices to minimise adverse effects. Additionally, the paper synthesises RFI processes in traditional and BIM-enabled project settings, maps a causal-loop diagram to identify associated issues and summarises approaches for extracting knowledge from the unstructured content of RFIs. The outcomes of this review stand to offer invaluable insights to both industry practitioners and researchers, enabling and promoting the refinement of RFI processes within the construction domain.

Most existing research has focused on the outcomes of advice-giving network centrality for focal employees, neglecting to address what drives employee centrality in advice-giving networks. Our study aims to explore how and when employees attain a central position in an advice-giving network.

Through a multi-wave, multi-source survey design, we collected data from 148 full-time employees on 34 newly established self-managed teams. We used Mplus 8 for data analysis and hypothesis testing.

Interpersonal trust and leadership emergence sequentially mediate the relationship between warmth/competence perceptions and advice-giving network centrality. Employee narcissism weakens the positive relationship between leadership emergence and advice-giving network centrality and further weakens the mediation effects.

Drawing on the social capital theory of career success and the approach-inhibition theory of power, we outline the driving mechanism behind and the boundary condition for advice-giving network centrality. Our findings not only uncover effective career development strategies for employees but also offer practical insights for managers to improve team effectiveness.

This study aims to systematically review the literature on how various factors influence investor sentiment and affect financial markets. This study also sought to present an overview of explored contexts and research foci, identifying gaps in the literature and setting an agenda for future research.

The systematic literature investigation yielded 555 journal articles, with few other exceptional inclusions. The data have been extracted from the two databases, i.e. Scopus and Web of Science. For bibliometric analysis, VOSviewer and Biblioshiny by R have been used. The period of investigation is from 1985 to July 2023.

This systematic literature review helped us identify factors influencing investor sentiment and financial markets. This study has broadly classified these factors into two categories: rational and irrational. Rational factors include – economics and monetary policy, exchange rate, interest rates, inflation, government mandatory regulations, earning announcements, stock-split, dividend decisions, audit quality, environmental, social and governance aspects and ratings. Irrational factors include – behavioural and psychological factors, social media and online talk, news and entertainment, geopolitical and war events, calendar anomalies, environmental, natural disasters, religious events and festivals, irrationality caused due to government/supervisory body regulations, and corporate events. Using these factors, this study has developed an investor sentiment model. In addition, this review identified research trends, methodology, data and techniques used by researchers.

This review comprehensively explains how various factors affect investor sentiment and the stock market using the investor sentiment model. It further proposes an extensive future research agenda. This study has implications for stock market participants.

In recent years, studies have shown that machine learning significantly improves student performance and retention and reduces the risk of student dropout and withdrawal. However, there is a lack of empirical research reviews focusing on the application of machine learning to predict student performance in terms of learning engagement and self-efficacy and exploring their relationships. Hence, this paper conducts a systematic research review on the application of machine learning in higher education from an empirical research perspective.

This systematic review examines the application of machine learning (ML) in higher education, focusing on predicting student performance, engagement and self-efficacy. The review covers empirical studies from 2016 to 2024, utilizing a PRISMA framework to select 67 relevant articles from major databases.

The findings show that ML applications are widely researched and published in high-impact journals. The primary functions of ML in these studies include performance prediction, engagement analysis and self-efficacy assessment, employing various ML algorithms such as decision trees, random forests, support vector machines and neural networks. Ensemble learning algorithms generally outperform single algorithms regarding accuracy and other evaluation metrics. Common model evaluation metrics include accuracy, F1 score, recall and precision, with newer methods also being explored.

First, empirical research literature was selected from only four renowned electronic journal databases, and the literature was limited to journal articles, with the latest review literature and conference papers published in the form of conference papers also excluded, which led to empirical research not obtaining the latest views of researchers in interdisciplinary fields. Second, this review focused mainly on the analysis of student grade prediction, learning engagement and self-efficacy and did not study students’ risk, dropout rates, retention rates or learning behaviors, which limited the scope of the literature review and the application field of machine learning algorithms. Finally, this article only conducted a systematic review of the application of machine learning algorithms in higher education and did not establish a metadata list or carry out metadata analysis.

The review highlights ML’s potential to enhance personalized education, early intervention and identifying at-risk students. Future research should improve prediction accuracy, explore new algorithms and address current study limitations, particularly the narrow focus on specific outcomes and lack of interdisciplinary perspectives.

This research aims to investigate the interplay between individuals’ health status and their level of trust in both smart health-care systems and health-care providers and how these factors influence the decision to use such systems.

Drawing upon institution-based trust and affordance theories, the authors developed and empirically examined a research model using a sample from a prominent US university.

The findings reveal that both types of trust, specifically trust in smart health-care systems and trust in health-care providers, positively influence the intention to use these systems. Additionally, the authors identified that health status plays a dual moderating role in this context. It positively moderates the relationship between trust in health-care system providers and the intention to use, suggesting that individuals with better health are more inclined to use smart health-care systems when trust in providers is high. Conversely, health status negatively moderates the relationship between trust in the system and the intention to use it. This implies that trust in the system exerts a more pronounced influence on the intention to use the system among individuals with lower health status. This heightened impact can be attributed to the increased necessity for the system’s benefits among this group.

While the power analyses suggest our sample size is sufficient, caution is warranted when interpreting the study’s conclusions. These results have substantial implications for researchers and providers of smart health-care systems. They underscore the intricate dynamics between trust, health status and technology use, offering valuable insights for future investigations in this domain. Furthermore, they guide the design and implementation of smart health-care systems, emphasizing the need to consider the nuanced influence of health status on trust and use intentions.

Past research has focused on individuals’ trust in understanding the adoption of smart health-care systems; however, it did not consider how individuals’ health status can moderate their trust and intention to adopt such systems. In this study, the authors close this gap by investigating the moderating role of health status in the relationships between two types of trust and intention to use smart health-care systems through the lens of institution-based trust theory and affordance theory.