Determinants of investment-related FinTech services among retail investors of India: a multi-group analysis using PLS-SEM

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-04-19 DOI:10.1108/jm2-01-2024-0025

Shweta Jha, Ramesh Chandra Dangwal

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引用次数: 0

Abstract

Purpose

The purpose of this study is to investigate the factors affecting behaviour intention (BI) to use and actual usages of investment-related FinTech services among the zoomers (Gen Z) and millennials (Gen M) retail investors of India.

Design/methodology/approach

The study explores the predictive relevance of actual adoption behaviour among the two different age categories of Indian retail investors. It uses the Unified Theory of Acceptance and Use of Technology-2 and the prospect theory framework as guiding frameworks. Data has been collected from 294 retail investors, actively engaged in the investment-related FinTech services. The multi-group analysis using variance-based partial least square structured equation modelling has been used to compare the two groups. The invariance between the two groups was achieved through measurement invariance assessment.

Findings

The study reveals distinct factors significantly affecting BI to use investment-related FinTech services among Gen Z and Gen M retail investors are performance expectancy (PE) to BI, perceived risk (PR) to BI, price value (PV) to BI and PR to service trust (ST).

Research limitations/implications

This study provides insights for financial providers and policymakers, emphasizing different factors influencing BI to use investment-related FinTech services in both age groups. Notably, habit emerges as a common factor influencing the actual usage of investment-related FinTech services across Gen M and Gen Z retail investors in India.

Originality/value

This study explores the heterogeneous behaviour of the heterogenous population in the domain of technological adoption of investment-related FinTech services in India.

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印度散户投资者使用与投资相关的金融科技服务的决定因素：利用 PLS-SEM 进行的多组分析

本研究的目的是调查影响印度 Z 世代和 M 世代散户投资者使用投资相关金融科技服务的行为意向（BI）和实际使用情况的因素。研究采用技术接受和使用统一理论-2 和前景理论框架作为指导框架。数据收集自 294 名积极参与投资相关金融科技服务的散户投资者。使用基于方差的偏最小二乘法结构方程模型进行多组分析，对两组进行比较。研究结果本研究揭示了显著影响 Z 世代和 M 世代散户投资者使用投资相关金融科技服务的 BI 的不同因素，即业绩预期（PE）与 BI、感知风险（PR）与 BI、价格价值（PV）与 BI 和 PR 与服务信任（ST）。值得注意的是，习惯是影响印度 M 世代和 Z 世代零售投资者实际使用投资相关金融科技服务的共同因素。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.