Kuo-An Tseng, Yu-Wen Lan, Hao-Chun Lu and Pin-Yu Chen
The purpose of this paper is to explore the mediating effect of business strategy (BS) on intellectual capital (IC) and financial performance (FP). The impact of financial crisis…
Abstract
Purpose
The purpose of this paper is to explore the mediating effect of business strategy (BS) on intellectual capital (IC) and financial performance (FP). The impact of financial crisis is also a major topic of this research.
Design/methodology/approach
This study applies BS as mediator to explore the relationships between IC, BS, and FP. Partial least squares is employed to test the reliability and validity of measurements and the significance of path coefficients, and therefore to examine the hypotheses.
Findings
IC has significant impacts on BS and FP in all samples, as well as in those years before and after the financial crisis. BS has a partial significant mediating effect between IC and FP. BS has significant effects on FP in all samples and pre-financial crisis, but has not in post-financial crisis.
Research limitations/implications
IC has significant impacts on BS and FP. Moreover, the relationships of IC, BS, and FP are different during pre- and post-financial crisis. The direct effect of IC on FP is confirmed and consistent, and the indirect effect of IC on FP by BS is dependent upon the environment status.
Practical implications
Enterprises should pay attention to IC, BS, and the related changes in environment status. These help enterprises develop appropriate strategies, maintain competitive advantage, and upgrade FP.
Originality/value
This study applies BS as mediator, and explores the relationships between IC, BS, and FP. The impact of financial crisis is also discussed. The results may serve as the criteria for strategic performance management.
Details
Keywords
Anum Paracha and Junaid Arshad
Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems…
Abstract
Purpose
Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems against cyberattacks. This paper aims to report findings from a quantitative analysis of literature within ML security to assess current research trends in ML security.
Design/methodology/approach
The study focuses on statistical analysis of literature published between 2000 and 2023, providing quantitative research contributions targeting authors, countries and interdisciplinary studies of organizations. This paper reports existing surveys and a comparison of publications of attacks on ML and its in-demand security. Furthermore, an in-depth study of keywords, citations and collaboration is presented to facilitate deeper analysis of this literature.
Findings
Trends identified between 2021 and 2022 highlight an increase in focus on adversarial ML – 40\% more publications compared to 2020–2022 with more than 90\% publications in journals. This paper has also identified trends with respect to citations, keywords analysis, annual publications, co-author citations and geographical collaboration highlighting China and the USA as the countries with highest publications count and Biggio B. as the researcher with collaborative strength of 143 co-authors which highlight significant pollination of ideas and knowledge. Keyword analysis highlighted deep learning and computer vision as the most common domains for adversarial attacks due to the potential to perturb images whilst being challenging to identify issues in deep learning because of complex architecture.
Originality/value
The study presented in this paper identifies research trends, author contributions and open research challenges that can facilitate further research in this domain.
Details
Keywords
- Adversarial machine learning
- Cyber threats
- Privacy preservation
- Secure machine learning
- Bibliometrics
- Quantitative analysis
- Analytical study
- Adversarial attack vectors
- Poisoning machine learning
- Evasion attacks
- Test-time attacks
- Differential privacy
- Data sanitization
- Adversarial re-training
- Data perturbation