Drawing on the resource-based view of the firm, investigation on the effects of internal resources and capabilities (R&D and technology) on small and medium-sized enterprises (SMEs) innovation performance is attempted. Using primary (survey-based) and financial data from 405 Greek manufacturing SMEs, new evidence on the impact of R&D activities, IT and other determinants such as ERP, size and exports on innovation performance is provided. It is found that IT (in terms of knowledge to quality management programs) enhanced positively on firm innovation performance, while R&D activities have no impact on firm innovation performance. In addition, absorptive capacity on innovation, access to finance, patents and firm size induce positively firm's innovation performance. The novelty of this study is the introduction of a proxy of innovation measuring overall innovation performance (both product and process innovation), computerisation of firm operation using ERP systems and investments in acquisition of knowledge about quality management programs as proxies of IT.

Purpose: The purpose of this paper is to examine the gender role phenomenon and the stereotyping of requisite managers’ personal characteristics in the Greek society of today. Design/methodology/approach: Data were collected quantitatively based on the informants’ perceptions on successful managers’ personality traits and according to the informants’ personality characteristics. Questionnaires were administered online to two separate convenience samples. Reliability analysis (Cronbach’s α) was employed for scale refinement, while intraclass correlation coefficient (r’) and t-test analysis examined the similarity of respondents’ responses across the items of the refined scale. Findings: The results indicate that gender role stereotypes are challenged. It seems that the perceived managers’ personality is comprised of both agentic/masculine and communal/feminine characteristics and this perception is not perceived differently by men and women. This debates on whether the “glass ceiling” exists due to other determinants. Originality/value: The study contributes to the literature on gender role stereotyping research and perceptions of managerial personality characteristics in Greece.

This study focuses on the role of business cooperation and firms’ exporting activity as the determinants of Greek manufacturing SMEs’ innovative extend use, contributing to the existing empirical literature. The empirical analysis based on unique both quantitative and qualitative data, derived from a survey covering more than 158 small and medium-sized Greek manufacturing firms, and examines factors affecting innovation activity, emphasizing on clustering activities. We find that inter-firm cooperation enhances innovation activity, which in turn empowers firms’ growth by improving domestic and overseas sales performance. This study opens the floor for a greater perspective in managerial and financial firms’ characteristics; Firms should take initiatives to promote collaborative networks for innovation and create trade associations that represent SMEs, in order to facilitate social interaction. Also, government should offer incentives to SMEs with high innovation potential (e.g. tax allowances) and invigorate linkages between universities, research centers and the private sector by creating effective institutional arrangements. Finally, we seek to provide policy implications to business owners, policy makers and academics, to optimize performance, in the shadow of economic turbulence that the country experiences.

Social media generates massive amounts of unstructured, multilingual textual data that contain many sentiments, emotions, and opinions from users that provide insights for enterprises undergoing digital transformation. In this paper, we evaluate a variety of commercially available, lightweight large language models (LLMs) for sentiment, emotion, aspect-based sentiment, and toxic speech detection on noisy, real-world social media datasets. These lightweight models are compared (against traditional RoBERTa-based classifiers and heavyweight “pro” LLMs) in terms of performance, latency, and cost trade-offs that are key for scalable deployment across the enterprise. Our results show that lightweight LLMs achieve good accuracy with considerably lower response times and costs, which makes them suitable as the next step in digital transformation for real-time social media analytics. Additionally, we investigate the importance of prompt engineering and show that preprocessing can be very limited for LLMs. This study provides operational guidelines regarding model selection based on latency, accuracy, and cost trade-offs as well as optimal prompt engineering techniques. Finally, it evaluates the intersection of operational performance and resource efficiency to help researchers and developers integrate LLM-based social media analytics into digitized business practices.