Analysing the behavioural, psychological, and demographic determinants of financial decision making of household investors

Kumar, Parul; Islam, Md. Aminul; Pillai, Rekha; Sharif, Taimur

doi:10.1016/j.heliyon.2023.e13085

Cited by 19 publications

(11 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The fit indices of the four-factor model were compared with those of other models, and the significant difference was used as the criterion to evaluate the discriminant validity of the model (Δχ 2 ; >3.84, >6.64, or >10.83) [ 14 ]. We applied the AVE and CR to evaluate convergent validity, where the values of AVE above 0.50 and CR above 0.70 indicated good convergent validity [ 59 ].…”

Section: Methodsmentioning

confidence: 99%

Perceived organizational support and occupational commitment: A study among male kindergarten teachers

Zhang,

Huang,

2023

Heliyon

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Perceived organizational support and occupational commitment: A study among male kindergarten teachers

Zhang,

Huang,

2023

Heliyon

View full text Add to dashboard Cite

“…Today's investment climate is blending financial and digital literacy. Additionally, P. Kumar, Islam, Pillai, and Sharif (2023) study digital literacy and financial information processing. They observed that technologically literate people can understand complex financial data and insights.…”

Section: Digital Literacy As a Mediatormentioning

confidence: 99%

Big Data, Artificial Intelligence, and Financial Literacy: Exploring their Combined Influence on Investment Behavior among Chinese Household

Zhang,

Sidik

2024

J INFORM SYSTEMS ENG

View full text Add to dashboard Cite

The investing behavior of Chinese families is undergoing a dramatic transition in the context of the digital financial era, impacted by factors such as big data use, AI adoption, financial literacy, digital literacy, and risk aversion. Although prior research has offered useful insights into certain components, a thorough examination of their linked dynamics has been lacking. The purpose of this research was to look into how big data usage, AI adoption, financial literacy, digital literacy, and risk aversion influence investment behavior among Chinese households. Additionally, it aimed to learn more about how risk aversion and digital literacy function as mediators in these relationships. A questionnaire-based survey of 370 Chinese families was employed as part of the quantitative research methodology. The study employed AMOS to find the relationship between variables. The research found that big data usage, AI adoption, financial literacy, and digital literacy significantly and favorably influenced Chinese households' investment behavior. It was discovered that digital literacy mediated the linkages between the adoption of technology and investment decisions. Furthermore, risk aversion reduced the effects of financial literacy and big data usage on investment behavior. This study added to the body of knowledge by providing a comprehensive framework that incorporates several aspects impacting investment behavior. It shed insight into the complicated dynamics of technology uptake and literacy, as well as their impact on investment decisions. The study went beyond individual components to investigate their interactions, resulting in a more complex view of modern investment behavior. This study has broad-ranging effects that will help investors, financial institutions, governments, educators, and researchers. The focus on a particular setting and self-reported data are two important constraints that must be acknowledged. Future studies can investigate longitudinal dynamics and cross-cultural variances to further our understanding of investment behavior in the digital age.

show abstract

“…Path coefficients(β) ≥ 0 indicate a direct and positive relationship whereas β ≤ 0 indicates a direct and adverse relationship, zero indicates that there is no direct relationship between the variables (Attia et al, 2023). Moreover, β-value between 0.1-0.3 show weak impact, between 0.3-0.5 moderate impact and 0.5-1.0 strong influence (Kumar et al, 2023), while the p-value for 95% confidence level should be < 0.05. The results of hypotheses testing in tables 6 and 7 provide a useful insight into the decision-making process for high-rise fast-track projects.…”

Section: Structural Model (Path Analysis)mentioning

confidence: 99%

A Decision-Making Model for Fast-Track High-Rise Buildings Using Structural Equation Modelling

Mustafa Sultan, Ishtiaq Hassan, Syed Shujaa Safdar Gardezi

2024

pst

View full text Add to dashboard Cite

Fast-track is a project management technique used to address time constraints and schedule overrun on construction projects. One of the major impediments in the application of fast-track technique in Pakistan’s construction industry is the lack of a comprehensive decision-making model. The aim of this research is to develop a comprehensive decision-making model for fast-track high-rise buildings which can highlight the impact of each decision-making aspect on its relevant project success indicator i.e., time variance(TV), cost variance(CV), quality variance(QV) or scope variance(SV) and the impact of cost, quality, and scope variances on time variance. Structural equation modelling (SEM) was used to develop this model. Questionnaire based on 5-point Likert’s scale was used to collect data from 159 construction industry professionals. Data was analysed using smart PLS-4 software. Results showed that on fast-track projects, scope variance has the greatest impact on time variance whereas quality variance has the least impact. Early scope freeze, early involvement of the contractor in design phase and early procurement of long-lead-time items are the most important decisions on fast-track projects whereas quality optimization decisions have an adverse impact on project duration. The proposed model has high out-of-sample predictive ability making it a universally applicable decision-making model. DOI: https://doi.org/10.52783/pst.434

show abstract

Analysing the behavioural, psychological, and demographic determinants of financial decision making of household investors

Cited by 19 publications

References 81 publications

Perceived organizational support and occupational commitment: A study among male kindergarten teachers

Perceived organizational support and occupational commitment: A study among male kindergarten teachers

Big Data, Artificial Intelligence, and Financial Literacy: Exploring their Combined Influence on Investment Behavior among Chinese Household

A Decision-Making Model for Fast-Track High-Rise Buildings Using Structural Equation Modelling

Contact Info

Product

Resources

About