Publicações

The rapid adoption of computing technologies and the Internet has transformed various aspects of society, including education, communication, commerce, and governance. While computer and communication technologies offer significant benefits, they also present complex ethical and security challenges. This paper explores the ethical and security dimensions of computing technologies, focusing on issues such as data privacy, algorithmic bias, cybersecurity threats, and digital well-being. Through a detailed analysis of these challenges, the paper examines how data collection, automated decision-making, and digital surveillance can undermine user autonomy, exacerbate inequalities, and compromise user privacy. The discussion is guided by ethical frameworks, including deontological and consequentialist perspectives, providing a balanced view of the ethical implications of technology use. The paper also proposes best practices for ethical technology integration, including clear data protection policies, bias mitigation strategies, transparent AI design, and user education programs. By promoting digital literacy and fostering a culture of ethical technology use, institutions can harness the benefits of computing technologies while minimizing risks. This paper emphasizes the need for a collaborative approach involving educators, administrators, developers, and policymakers to ensure that technology serves as a tool for empowerment rather than exploitation.

Purpose

This study examines the influence of corporate governance, firm-level characteristics, external factors and risk-taking on bank credit ratings in three distinct regions: Africa, the EU13 and Latin America/Caribbean.

Design/methodology/approach

This research analyzes a panel dataset comprising 752 banks from 95 countries from 2011 to 2020, using ordered logistic regression.

Findings

The results reveal that corporate governance factors, including board size, board age, and board gender diversity, significantly impact credit ratings. Firm-specific characteristics, including age, market discipline, and opacity, negatively correlate with credit ratings. External factors, particularly the presence of the Big Four audit firms and economic growth, positively influence credit ratings. Institutional quality negatively impacts credit ratings, while risk-taking shows a significant positive association.

Practical implications

This study encourages banks and policymakers to re-evaluate governance structures, risk management strategies, and region-specific approaches to credit assessment. A thorough understanding of credit rating determinants is essential for fostering a resilient and sustainable financial environment.

Originality/value

This study underscores the critical role of robust corporate governance, institutional quality, and audit oversight in shaping credit ratings within the global banking sector. It challenges the prevailing one-size-fits-all approach to credit-rating assessments and supports the Sustainable Development Goal (SDG) 8, Target 10, which aims to strengthen financial institutions. The findings also contribute to the ongoing discourse on credit ratings within the United Nations Economic Commission for Africa framework.

Purpose

This study examines why micro and small enterprises (MSMEs) continue to lag in adopting big data analytics (BDA) despite its widely recognized strategic value. Drawing on the unified theory of acceptance and use of technology (UTAUT), we analyze how performance expectancy, effort expectancy and facilitating conditions shape adoption intentions among 500 Brazilian business owners and managers in the retail sector, while accounting for the moderating roles of experience, task usefulness, and market competition.

Design/methodology/approach

A structured questionnaire was used in a telephone survey via an automated telephone center. The analysis, based on PLS-SEM, identifies the most relevant factors affecting the adoption of business data analytics tools. Similarly, a stepwise estimation strategy was implemented for assessing the direct effects of core UTAUT constructs and for evaluating the antecedents and interaction terms that significantly increase explained variance in IABDA.

Findings

Using PLS-SEM, the findings reveal a paradox: although performance expectancy strongly drives adoption intention, prior experience with data analytics reduces willingness to adopt, suggesting that familiarity may expose hidden implementation challenges rather than alleviate them. Contrary to dominant assumptions, effort expectancy does not significantly influence adoption decisions, indicating that perceived complexity is not a primary deterrent in this context. This suggests that resource constraints, such as budget constraints and the lack of specialized personnel, create barriers that hinder adoption despite prior familiarity with technology.

Originality/value

The study contributes to the literature by shifting the focus from structural barriers to cognitive and experiential mechanisms underlying technology adoption in MSEs. It highlights that adoption decisions are not solely driven by perceived benefits but are shaped by nuanced interpretations of feasibility, usefulness and prior experience. These insights offer important implications for policymakers and practitioners seeking to accelerate data-driven transformation in resource-constrained firms.

The purpose of this study is to analyze the extent to which intangible assets drive Small and Medium-Sized Enterprise (SME) sales growth in post-pandemic contexts characterized by volatility, uncertainty, complexity, and ambiguity. The empirical analysis is based on a sample of 588 Portuguese SMEs operating in medium-high and high-tech industries, using data from 2023. The results show that intangible assets are significant drivers of SME sales growth in this context. In contrast, tangible assets and firm age are found to have a negative influence on growth, suggesting that structural rigidity and organizational inertia may limit firms’ ability to adapt to dynamic environments. Regarding control variables, return on assets is positively associated with growth, while liquidity and financial structure do not show consistent statistical significance across models. The study contributes to the literature by showing that SME growth in uncertain environments is not linear, but rather conditioned by the firm’s ability to reconfigure its asset portfolio. The findings highlight the importance of intangibility, resource efficiency, and financial flexibility as key drivers of growth in dynamic economies. Furthermore, the results underline the relevance of policy mechanisms aimed at supporting SMEs through more flexible financial instruments and innovation-oriented strategies.