Purpose Given the disparity of results in the literature on female business leadership, this study aims to deepen the existing debate by offering an enriching proposal on this concept. We focus both on previous contributions to the literature and on the inclusion of contextual factors, such as the family business environment, to develop our proposal. Design/methodology/approach The article adopts a quantitative approach using panel data to analyse data collected from 162 large companies between 2007 and 2019. Findings The effectiveness of female business leadership increases when women participate in more than one of the company's corporate governance actors, with the family context being more conducive to achieving this female business leadership. Originality/value Our work takes a step further by delving deeper into female business leadership, arguing that it should not only involve the possibility of participating in and influencing business decision-making, but also generate the capacity for this participation to influence others and, therefore, the orientation of the company.
Purpose The aim of this paper is to analyze and typify the strategic consequences of M&A withdrawal for abandoned firms once the private negotiation phase is over. Design/methodology/approach Due to the exploratory nature of the research question, this research follows a qualitative methodological approach using a multiple case study of six announced acquisitions that ended in withdrawal in the Spanish banking industry using 503 media news and the firms' annual reports. Findings M&A withdrawal is a stressful and disturbing event for the abandoned firm. After withdrawal some abandoned firms survive and recover while others do not. Strategic behaviors that allow stability and flexibility help to explain why some firms recover while others collapse. Originality/value The paper improves knowledge about strategic consequences for abandoned targets of being jilted at the altar through the lens of resilience research.
Purpose This study examines whether B Corp certified firms present lower investment-cash flow sensitivity than non-certified firms, and whether this differential varies with B Impact Assessment scores or firm size. Design/methodology/approach A fixed-effects panel data analysis is conducted on 2,945 unlisted firms from five European countries over 2014–2022, of which 407 were certified during the period. Control firms were matched by size, sector, and country. Findings Certified firms exhibit significantly lower investment-cash flow sensitivity than non-certified firms, a pattern consistent with lower information asymmetries and agency conflicts. No significant differences are detected between firms scoring above and below the country-year mean on the B Impact Assessment, and size-related differences in sensitivity are of a similar magnitude in both groups. This lower sensitivity is already observable before certification. Research limitations/implications Certification is not random, so the findings should be interpreted as associations consistent with the proposed mechanisms rather than as strictly causal effects. Practical implications B Corp status identifies firms whose investment depends less on internally generated funds, which is informative for investors and lenders that assess certified firms. Social implications The findings support policy efforts to promote social enterprise models as instruments of sustainable and inclusive economic development. Originality/value This is the first study to relate B Corp certification to investment-cash flow sensitivity, in a sample of unlisted European firms, separating certification status from performance within the process.
Purpose Grounded in the Attention Capture and Transfer by Elements of Advertisements (AC-TEA) model, this study examines atmospheric variables, image brightness and design complexity, in furniture e-retail by integrating visual attention with perceptions of pleasantness and order. Design/methodology/approach An eye-tracking experiment measured consumers' visual attention in images simulating furniture e-retail storefronts or catalogues. Metrics included pupil dilation as an index of emotional arousal and cognitive effort, and fixation-based measures to capture visual attention and information processing. The study used home-interior images varying in brightness and design complexity, integrating physiological data with post-test perceptions of pleasantness and order. Findings Results show greater pupil dilation when consumers view dark-filtered or highly complex images, indicating higher emotional arousal and cognitive effort. Brighter lighting conditions are associated with reduced pupil dilation. Scene complexity also affects attentional metrics, leading to longer fixation durations but fewer fixations in high-complexity images, suggesting a more focused visual information processing. Furthermore, these visual attention patterns translate into perceptual judgements, with attention to dark and moderately structured scenes positively predicting pleasantness and order, respectively, while sustained attention to highly complex scenes is associated with lower perceived order. Originality/value This study provides novel evidence, integrating eye-tracking with subjective measures, on the effects of image brightness and design complexity on consumer attention and perceptions in furniture e-retail, and develops a model of Attention Capture and Transfer within an E-Retailing (AC-TER), extending previous research within this sector.
Purpose This paper aims to provide a comprehensive bibliometric mapping of research on artificial intelligence in algorithmic trading by analyzing 417 publications from 1993 to 2025. It seeks to identify publication trends, influential journals and authors, collaboration patterns, and the field's main thematic structures, in order to clarify the intellectual foundations, emerging research fronts, and future directions of this rapidly evolving interdisciplinary domain. Design/methodology/approach The study employs a bibliometric research design based on 417 publications retrieved from the Web of Science Core Collection (1993–2025). Descriptive indicators are used to analyze publication trends and source productivity. Network-based techniques, implemented through VOSviewer, are applied to conduct citation analysis, co-authorship mapping, and term co-occurrence analysis. These methods enable the identification of influential contributions, collaboration structures, thematic clusters, and emerging research fronts within artificial intelligence–driven algorithmic trading. Findings The results show a sharp growth in publications since 2018, indicating the rapid consolidation of AI-driven algorithmic trading as a research field. A small number of highly cited studies act as intellectual anchors, while most authors and institutions remain weakly connected, revealing fragmented collaboration networks. Thematic analysis identifies coherent clusters cantered on methodological innovation, trading strategy applications, time-series forecasting, and ethical and governance issues. Emerging research fronts, particularly transformer-based models and explainable AI, signal a shift toward more interpretable and robust trading systems. Originality/value This study provides the first large-scale, longitudinal bibliometric mapping of artificial intelligence research in algorithmic trading, covering more than three decades of scientific production. It uniquely integrates publication trends, citation impact, collaboration networks, and thematic clustering within a single analytical framework. By revealing both the intellectual consolidation of dominant AI paradigms and the fragmentation of collaboration structures, the paper offers novel insights into the field's evolution. It delivers strategic value for scholars and practitioners by identifying emerging research fronts and underexplored areas that shape future theoretical development and practical innovation.
