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Delving into the Architecture of International B2B Relationship Marketing During the COVID-19 Pandemic: From Business Convergence to Partnership Effectiveness

ABSTRACT Purpose The study seeks to address the influence of different facets of business-to-business (B2B) relationship marketing on internationalization effectiveness, by taking into account the turbulent environment generated by the COVID-19 pandemic. The focus falls on the factors that generate, frame, catalyze, sustain and strengthen international business relationships between managers from organizations with converging interests. Methodology B2B relationship marketing in the international arena is analyzed by means of various angles such as business convergence, business context, interpersonal compatibility, business credibility and network interconnections as availed by the “new normal” triggered by the COVID-19 pandemic. A questionnaire-based survey with 158 business owners and managers from European industrial companies was carried out during November 2023 to investigate the configuration and dynamics of B2B relationship marketing in the unprecedented and systemic COVID-19 crisis. Data was analyzed via structural equation modeling using SmartPLS 4. Findings The findings showed that while business convergence remains a strong influencer of all the other relational dimensions, the restrictions and limitations engendered by the pandemic have also altered international B2B relationship marketing to such an extent that the only significant factor for internationalization effectiveness relies on business networking. Research and practical implications The study has both theoretical and practical implications, bringing forward a topical perspective on international B2B relationships during COVID-19 disruptions and their impact on the effectiveness of international operations. Originality The study advances a phenomenological view of the unprecedented changes and their influences on the overseas operations of SMEs in a particular field. It complements and adds to the literature by stressing a more articulated relationship-centric architecture, which probed relevance during the pandemic.

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Artificial intelligence algorithm for neoplastic cell percentage estimation and its application to copy number variation in urinary tract cancer.

Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

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