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South Africa’s wool industry plays an important role in the agricultural sector. The wool industry provides a valuable source of income for farmers who practice sustainable farming practices. However, wool farmers face numerous challenges, such as wool contamination, dirty wool and producing good-quality wool. Good-quality wool is determined by fibre diameter, clean yield, vegetable matter and staple length. This study aims to address these challenges.

A multiple regression analysis of price (R/kg) of White wool and Merino wool was applied to four variables fibre diameter: vegetable matter, clean yield and staple length. The analysis was based on the data for the 2009–2019 data from Cape Wools auctions.

Fibre diameter, clean yield and staple length, with exception of vegetable matter, made a statistically significant contribution to the determination of wool price after all other independent variables were controlled for (p < 0.05). A one-unit (micron) increase in fibre diameter resulted in a 0.404-unit decrease in wool price (R/kg). A one-unit (mm) increase in staple length resulted in a 0.022-unit increase in wool price (R/kg). There was no statistically significant association between vegetable matter and wool price. A one-unit increase in clean yield was associated with a 0.111-unit increase in wool price (R/kg).

Since wool fleeces consist of the largest portion of wool shorn from sheep, it is important for wool farmers to focus on wool with low fibre diameter, high clean yield percentage, low percentage of vegetable matter content and good length of the wool.

Since wool fleeces consist of the largest portion of wool shorn from sheep, it is important for wool farmers to focus on wool with low fibre diameter, high clean yield percentage, low percentage of vegetable matter content and good length of the wool.

In a developing country such as South Africa, this study is important for the following reason. It is understanding the wool characteristics that have the most significance influence on the determination of wool price for Merino wool and White wool might effectively help the wool farmers to adapt their production systems to improve the wool characteristics that determine wool price.

This study identified a need for a study to be conducted on all wool classes.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

With the emergence of the digital era, the role of digital leaders in developing digital capabilities and driving their firms towards digital transformation has gained significant attention. Digital dynamic capabilities involve continuous engagement of leaders in sensing, seizing, and transforming activities needed to digitally transform their firms. However, little attention is given toward the role of digital leadership in developing digital dynamic capabilities. We seek to develop an understanding of the role of digital leadership in building digital dynamic capabilities for successful digital transformation.

We conducted a systematic literature review and looked at relevant articles using Google Scholar, ScienceDirect, and Scopus databases with key search items being “digital leadership”, “dynamic capabilities”, “digital dynamic capabilities,”. We used AND, OR operators in between the key terms to search for the relevant articles.

Our conceptual framework and propositions demonstrate the digital leader's role in building three core dynamic capabilities: digital sensing (technological trends, digital scouting, digital vision, future interpretation, and digital strategies), digital seizing (organizational agility and digital portfolio), and transforming (redesigning internal structures and ecosystem partnerships) for successful digital transformation.

This study pioneers an integrated framework that elucidates the role of digital leadership in fostering digital dynamic capabilities essential for successful digital transformation. While previous research has examined digital leadership and transformation in separate silos, our work bridges this gap by defining and dissecting three core capabilities—digital sensing, digital seizing, and transforming. By doing so, we offer both academic and practical communities a nuanced understanding of how digital leadership shapes dynamic capabilities. The study serves as a foundational roadmap for future research and offers actionable insights for organizations striving to navigate the complex landscape of digital transformation.

The objective of this research is to determine the influence of solvency and liquidity on the profitability [return on assets (ROA)] of Tunisian banks from Q2-2020 to Q3-2022 by considering asset quality as a moderating variable.

This study uses data on liquidity, solvency, ROA and asset quality for 12 banks. It also considers bank size, gross domestic product (GDP) growth and inflation as control variables. The methodology is based on panel data with generalized least squares (GLS) estimation to assess the moderate influence of the asset quality on solvency, liquidity and ROA. Also, the generalized method of moments (GMM) estimation is used as a robustness test.

The results of the GLS model estimation indicated a negatively significant moderating correlation between the liquidity and the solvency. The data from the GMM model indicate that the liquidity variable predicted by the liquidity has a positively significant influence on a bank's ROA as well as for the solvency variable, which is predicted by the capital capacity. Therefore, we conclude that these two variables had a positively significant impact on the ROA.

The studies have many implications for banks and their management in addition to the industry regulators. The results of this study will enable political decision-makers to determine the banks' profits based on their liquidity and solvency.

This analysis provides financial explanations and recommendations for stakeholders in Tunisian banks. Furthermore, these banks must also be able to maintain their liquidity and solvency to ensure their profits in times of COVID-19.