... depth, and network density. Based on the algorithm-selected key variables, a CART decision tree model incorporates firms' knowledge diversity to uncover configurational patterns driving persistent innovation. The machine learning-based framework enables both variable prioritization...

... strategies, and stage-specific transformation roadmaps. Design/methodology/approach This study employs machine learning ( ML) algorithms (Random Forest, LightGBM, XGBoost) and SHAP analysis to examine how corporate culture and executive traits influence digital transformation. Using 2017–2023 data from...

... research on ESG investing preferences, specifically related to social media content, sentiment, and other factors. The search used the Scopus database with broad keywords, including social media content, netnography, machine learning, big data analytics, and ESG investing. These terms were chosen to ensure...

... Artificial intelligence readiness Machine learning Consumer acceptance model Technology acceptance model Consumer acceptance of AI-enabled products and services is critical for businesses aspiring to use AI through Industry 4.0 (Jan et al., 2023 ; Rampersad, 2020). AI has been considered...

... Intelligent systems Micro, small and medium enterprises Machine learning Fuzzy cognitive maps Innovation is a phenomenon studied from different perspectives, considered both a process and a result; encompasses different elements and stages of development. The interest in the subject from...

... Artificial intelligence Machine learning Entrepreneurial finance Communication The verbal and non-verbal skills of individuals in real-life communication situations can have a crucial impact on audience members’ behavior. Through a dramaturgical and interactive approach, we can imagine situations...

... The primary dataset for this study includes 1,268 worldwide firm-year data for 2021. The sample is subjected to missing data examination as a component of the filtration process. Data preprocessing is performed before machine learning analysis, including verifying missing data. Our research resulted...

...Melike Artar; Yavuz Selim Balcioglu; Oya Erdil Purpose Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications...

... for specialized digital proficiencies. Leveraging this imperative, firms seek to attract and retain top-tier talent through generous compensation packages. This study introduces a holistic, integrated theoretical framework integrating machine learning models to develop a compensation model, interrogating...

.... Hence, it was concluded that EEG measures were better at recognizing brain activity associated with ROL than TOL. EEG signals were also strongest when individuals were presented with stimuli containing positive (specifically, happy) emotional content. A subsequent machine learning model developed using...

... and machine learning to healthcare engineering systems. Design/methodology/approach A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed...