The increased penetration of inverter-based resources is reducing system inertia and challenging traditional frequency modeling approaches based on a single global frequency. Accurate estimation of local bus frequencies has therefore become essential for dynamic analysis and control. This paper proposes a near real-time bus frequency estimation framework that combines the analytical structure of the frequency divider (FD) formulation with the distributed inference capability of the Gaussian belief propagation (GBP) algorithm. By representing the power system as a factor graph, the proposed method enables iterative and fully distributed computation of bus frequencies without matrix inversion. The proposed algorithm enables estimation on a much smaller time scale than classical approaches, allowing faster detection of frequency deviations. Simulation studies on standard test systems demonstrate that the GBP framework achieves the same accuracy as the classical FD, while offering improved scalability and suitability for distributed implementation.
Although the formation and behavioral consequences of brand stereotypes have been the focus of recent research, little is known about whether and how social media influencers shape the content of brand stereotypes in terms of warmth and competence. Building on associative learning theory, and drawing on stereotyping and influencer marketing literature, we develop and test a multilevel model of (a) the transfer of consumer stereotypical perceptions of influencers to their stereotypical perceptions of brands, and (b) the moderating impact of influencer–brand fit on the aforementioned stereotype content transfer. In a large empirical study of 40 brands, 80 influencers, and 815 consumers, we find that influencer stereotype content indeed transfers to the brand, both in terms of warmth and competence perceptions. Furthermore, the degree of the influencer–brand fit enhances the transfer of warmth perceptions but not of competence perceptions. Our findings contribute to the emerging literature on stereotype content transfer and offer managerial insights for selecting social media influencers.
To encourage a more critical, evidence-based use of neuromarketing, this study conducts a systematic conceptual and empirical examination of neuromarketing myths. We propose an operational definition of neuromarketing myths and use a mixed-methods approach to identify 21 myths through a literature review and interviews with 13 experts. These myths are organised into four conceptual categories and examined in a large-scale quantitative survey (N = 639). The results reveal a high overall prevalence of neuromarketing myths in all stakeholder groups, with academics showing comparatively lower myth endorsement levels. Distinct patterns emerge across myth categories indicating that even experienced professionals remain susceptible to certain methodological and ethical misconceptions. The implications highlight the need for targeted myth-debunking and neuro-literacy interventions in marketing education and professional practice.
Strong-field ionization by thermal light is studied. Even though thermal light can be considered as completely classical stochastic light, it can also be treated as quantum light in the sense that it can be represented by a superposition of coherent states, similarly as has been done for the bright squeezed vacuum light, for example. Such a distribution over coherent states contains components with an intensity much higher than the average intensity of the thermal light. This increases the ionization probability by many orders of magnitude. For low intensities, in the multiphoton regime, the enhancement is by the factor of , with the multiphoton order of the process. This result is in agreement with an old experiment [Phys. Rev. Lett. 32 (1974): 265] in which a similar enhancement factor appears if a multimode laser pulse is used instead of a single-mode pulse. It is also shown that the plateau length in high-order above-threshold ionization by thermal light is extended by an order of magnitude in comparison with that of coherent laser light with the same average intensity.
PurposeDigital transformation (DT) is widely recognised as a strategic imperative for organisations seeking to sustain competitiveness in increasingly digitalised environments. Despite extensive research, empirical evidence on how digital transformation translates into financial and non-financial performance remains inconclusive. This study addresses this gap by proposing and empirically testing a model that links digital maturity to organisational performance through internal process efficiency as a process-based mediating mechanism.Design/methodology/approachThe study draws on survey data from 300 managers in Croatian enterprises across multiple industries and tests the proposed model using structural equation modelling (SEM) to examine both direct and mediated relationships.FindingsThe results show that both managerial and technological dimensions of digital maturity are positively associated with internal process efficiency, which in turn enhances financial and non-financial performance. The mediating role of internal process efficiency has been confirmed, particularly in contexts where strategic readiness alone does not translate into operational outcomes.Practical implicationsThe findings indicate that digital maturity should not be approached only as a technological upgrade, but as an organisational transformation that requires integrating strategic intent, technological capabilities, and internal process redesign. The proposed model helps decision-makers align digital investments with process optimisation to improve organisational performance.Originality/valueThis study advances the literature by introducing a two-dimensional operationalisation of digital maturity and empirically validating the mediating role of internal process efficiency in a transitional economy context. The findings provide novel empirical evidence on this mechanism within a European transitional economy.
