Publicações

Organizations increasingly measure and report sustainability, yet the impacts they report are produced in projects. This paper proposes a relational reconceptualization of sustainability maturity in project management, as the coupling of measurement means to the project decisions where sustainability ends are produced, and examines it against practitioners’ accounts. Sixty interviews (from an initial corpus of 62) with project management practitioners across six sectors and three organizational scales were analyzed through hybrid qualitative content analysis (27 codes; blind double coding, κ = 0.70). Practitioners rated their organizations readily but on improvised scales that were differentiated internally and narrated as trajectory, including regression; no participant invoked any named maturity model. In this sample, measurement accumulates at the organizational level while the project level frequently goes unmeasured: 26% of the 54 organizations, and 47% of the multinationals among them, maintain corporate measurement with no project-level measurement. This asymmetry is an observable signature consistent with vertical, means–ends decoupling; whether the corporate measurement that exists fails to inform project decisions is an inference the present design cannot test. External requirements dominate the reported drivers and are frequently welcomed as resources for growth; in exploratory sector comparisons, construction shows the highest project-level measurement and IT the lowest. The findings are consistent with the coupling conception, which they were used to develop rather than to test, and point to measurement attachment, rather than instrument availability, as the likely binding constraint in sustainability management systems.

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.