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Article
Publication date: 3 October 2023

Jie Lu, Desheng Wu, Junran Dong and Alexandre Dolgui

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely…

342

Abstract

Purpose

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

Design/methodology/approach

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Findings

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

Originality/value

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

Details

Industrial Management & Data Systems, vol. 123 no. 12
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 22 May 2024

Shuang Xu, Zulnaidi Yaacob and Donghui Cao

This study aims to explore how transformational leadership influences employees' creativity by considering the role of the environment and psychology. The study aims to provide…

314

Abstract

Purpose

This study aims to explore how transformational leadership influences employees' creativity by considering the role of the environment and psychology. The study aims to provide insights into the impact of transformational leadership on team innovation climate, team reflexivity, psychological capital and employee creativity while also examining the moderating effect of environmental dynamism on these relationships.

Design/methodology/approach

This study employed a multi-source, multi-wave approach, utilizing data from 618 participants in 118 teams. It constructed a multilevel structural equation model and estimated the confidence intervals of the mediated and moderated effects using the Markov chain Monte Carlo method.

Findings

The results of the multilevel analyses indicated that transformational leadership positively influenced the team innovation climate, team reflexivity, psychological capital and employee creativity. Moreover, the study found that environmental dynamism positively moderates the relationships among transformational leadership, team reflexivity, psychological capital and employee creativity.

Originality/value

Drawing on social cognitive theory and the motivated information processing in groups model, this study offers new insights into the interplay between transformational leadership and creativity. It examines the moderating role of cross-level process linkages and environmental dynamism, thereby validating and extending relevant theories.

Details

International Journal of Organization Theory & Behavior, vol. 27 no. 2
Type: Research Article
ISSN: 1093-4537

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