Gabriele Santoro’s research while affiliated with University of Nicosia and other places

Purpose This study investigates the variations in consumers’ affective responses to AI-generated versus human-generated content in a firm’s advertising copywriting, analysing emotional reactions to short-form promotional text across three affective dimensions—Pleasure, Arousal, and Dominance—from the PAD emotional model. Design/methodology/approach A Mann-Whitney analysis on SPSS 29 is conducted to assess group differences on a sample of 222 participants randomly assigned to one of two experimental conditions. The analysis evaluates group variances across emotional constructs alongside three control variables: AI Encounter, AI recognition and AI trust. Findings The results indicate no significant differences among groups regarding the main emotional dimensions, suggesting relevant implications for AI integration in business process automations. However, trust and familiarity with AI technology influence consumers’ responses differently across conditions. Originality/value This study sheds light on new business opportunities for enhancing creative communication processes with AI implementation, providing practitioners with relevant insights to improve advertising and copywriting by collaboratively using AI in crafting emotionally engaging advertisements for effective communication strategies.

Managerial Experimental Approaches (MEAs) such as Lean Startup (LS), Agile, Design Thinking (DT), and Growth Hacking (GH) have gained prominence in business and management for their potential to shape Business Model Dynamics (BMDs) through innovation, validation, pivoting, and scaling. The lack of research on the influence and implications of MEAs in BMDs and their interconnections motivates the present study. This study aims to provide a comprehensive understanding of the interplay between MEAs and BMDs by systematically reviewing the current state of research. Key findings reveal both shared and unique contributions of each MEA to BMDs, such as LS’s focus on rapid validation, Agile’s emphasis on adaptability, DT’s role in fostering creativity and reducing biases, and GH’s alignment with measurable, data-intensive scaling. The study also uncovers significant synergies among the approaches and gaps in the literature, highlighting the potential for a more integrative view of leveraging MEAs. As a key contribution, we propose an organizing framework synthesizing fragmented insights, guiding scholars and practitioners in tailoring MEAs to dynamic business environments. By bridging theoretical gaps and providing actionable insights, this research advances the understanding of MEAs’ strategic value in shaping BMDs and lays the groundwork for future exploration.

Purpose The rapid expansion of technology has facilitated the widespread adoption of data-driven approaches and strategies for business growth. One such approach is growth hacking (GH), which seeks to optimize growth through experimental methods. Despite increasing use in organizations of all sizes, the academic literature has not fully explored the implementation and potential benefits and challenges associated with GH. This paper aims to address this research gap by providing new insights into GH and categorizing its main benefits and challenges. Design/methodology/approach A multiple-case study approach was employed to investigate the growth strategies of micro, small and medium enterprises. Semi-structured interviews were conducted with founders, managers, consultants and professionals in the field. Findings The findings shed light on the economic, technological, organizational and managerial benefits derived from GH implementation, which include the scalability of strategies and a data-driven culture, learning from failures, leaner and more efficient processes and improved readiness to respond to change. Several challenges associated with GH implementation were also identified, including entry barriers; limited availability of time, budget and resources and a higher propensity for risk, failure and patience. Originality/value This article contributes to the existing literature by providing new evidence on the opportunities and risks associated with GH for better and more effective implementation of this strategy while suggesting future research directions.

Purpose-This research examines the influence of big data analytics (BDA) on the competitive performance of firms in the United Arab Emirates (UAE). Focused on the linkages of IT-enabled dynamic capabilities, managerial support, data driven culture, environmental uncertainty and supply chain resilience, the study aims to evaluate the mechanisms through which BDA contributes to competitive advantage. Design/methodology/approach-This research employs an empirical investigation to address questions regarding the influence of BDA on the competitive performance of the supply chain industry in the UAE. The research involved the distribution of a structured questionnaire to the employees (n 5 400) across diverse supply chain units in the UAE. The proposed framework was evaluated through SPSS and AMOS. Additionally, the researchers utilized the Process Macro to reveal the mediating and moderating dynamics. Findings-The study finding emphasizes the impact of BDA on both supply chain resilience and competitive performance with IT-enabled capabilities playing a mediating role. Furthermore, managerial support was found to positively moderate the relationship between BDA and IT-enabled capabilities. Research limitations/implications-This study contributes to the improvisation of existing literature in the field providing an understanding of how study variables collectively influence competitive performance within the specific context of UAE firms. Practical implications-The findings provide insights for industry practitioners, highlighting the strategic importance of integrating BDA into supply chain management to boost operational efficiency and sustain competitive performance. Originality/value-The study findings provide opportunities for scholars as well as managers for optimizing their strategic developments to build sustainable competitive performance by processing data analytic processes, resilient activities and efficient managerial support.

This study examines entrepreneurial dynamics in the electric vehicle (EV) sector, focusing on motivations, personality traits, and external influences such as government policies, innovation, and competition. The study developed a research model based on the creation and discovery theory. The proposed model was evaluated with data obtained from 389 participants through a deep learning-based artificial neural network (ANN) and the partial least squares structural equation modeling (PLS-SEM) approach. The research identifies key predictors of entrepreneurial behavior, including innovativeness, technological advancement, and entrepreneurial personality traits, which significantly impact motivation and enthusiasm for EV ventures. The findings underscore the crucial role of government policies in fostering entrepreneurial enthusiasm while competitive pressures and innovation drive the development of strong business attributes. Entrepreneurial motivation emerges as a critical mediator, bridging various predictors and enthusiasm for EV adoption. By integrating discovery and creation theories, this research offers a comprehensive theoretical framework, providing actionable insights for policymakers and industry stakeholders to promote sustainable entrepreneurship. Despite limitations related to generalizability and the study’s cross-sectional nature, the findings lay a robust foundation for future research on entrepreneurial ecosystems in emerging industries. This study advances the understanding of entrepreneurial dynamics in the EV sector and highlights pathways for fostering innovation and sustainability in entrepreneurship.