Research Articles (Business Management)

Permanent URI for this collectionhttp://hdl.handle.net/2263/2508

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Digitalisation in the procurement process of a water utility : a South African perspective
Ncanywa, Zisanda; Jojozi, Fani Nicholas; Eresia-Eke, Chukuakadibia E. (AOSIS, 2026-05-07)
ORIENTATION : Process digitalisation has been increasingly adopted as a strategic means of enhancing organisational performance. However, the digitalisation of inefficient processes risks perpetuating existing shortcomings by consistently delivering suboptimal results. Repetitive tasks are often default candidates for digitalisation, and some procurement tasks fall into this category. RESEARCH PURPOSE : Guided by four research questions, this study seeks to investigate the persistent bottlenecks arising from the entity’s reliance on manual practices in contract management, aiming to improve efficiency, transparency and overall process effectiveness. MOTIVATION FOR THE STUDY : Too often, procurement value is measured through cost savings, which are commonly achieved through sourcing and contracting. However, contract management remains a complex administrative task that is often disproportionately dependent on account managers for execution and oversight. This study investigates the digitalisation of the contract management task in the procurement process within a Water Utility in South Africa. RESEARCH DESIGN, APPROACH AND METHOD : A single-case qualitative research design was employed, using semi-structured interviews with key personnel involved in procurement activities within the selected entity. The data were thematically analysed to extract insights. MAIN FINDINGS : The findings reveal critical gaps in contract management, characterised by inefficiencies, inconsistencies and delays stemming from manual workflows. The study highlights that effective digitalisation requires not only technological adoption but also organisational readiness and supportive environmental factors. PRACTICAL/MANAGERIAL IMPLICATIONS : The digitalisation of the contract management task should be aligned with the organisation-wide digital strategy. Investing in digital infrastructure should be prioritised and supported by a skills audit and subsequent staff training. CONTRIBUTION/VALUE-ADD : This study contributes by revealing contract management gaps driven by inefficient, inconsistent manual workflows, and shows that successful digitalisation requires both technology adoption and organisational readiness with supportive environmental conditions.
Making sense of sustainability : a decade of research and emerging directions for the future
Monkge, Tshegofatso Alice; Le Roux, Catherine Anne; Letsholo, Rebaona Gladness (AOSIS, 2026-05-26)
ORIENTATION : Corporate sustainability is an evolving construct shaped by multiple interpretations and contextual meanings. This plurality affects managerial action and underscores the need for studies to deepen theoretical and practical understanding. RESEARCH PURPOSE : This study aims to synthesise the scholarly debate through a structured scoping review at the intersection of sensemaking and corporate sustainability. The systematic synthesis of a decade of scholarship provides a firmer conceptual grounding for future research. MOTIVATION FOR THE STUDY : Despite growing interest, research at the intersection of organisational sensemaking and corporate sustainability remains fragmented and conceptually underdeveloped. This study addresses this gap by mapping thematic patterns, identifying silos, and exposing overlooked perspectives. RESEARCH DESIGN, APPROACH AND METHOD : Based on 104 peer-reviewed articles (2014–2024), this structured scoping review uses thematic analysis and bibliometric techniques to trace the field’s evolution, drawing on Scopus and ScienceDirect for cross-disciplinary synthesis. MAIN FINDINGS : Three dominant thematic clusters around which sensemaking processes in corporate sustainability are articulated are revealed: (1) Responsibility, (2) Leadership, and (3) Strategy. An integrative conceptual framework is proposed, offering an interpretive lens to advance practice and future scholarship. The findings expose conceptual silos and underexplored empirical contexts as avenues for future research. PRACTICAL/MANAGERIAL IMPLICATIONS : Management could utilise the study’s findings to provide actionable insights for aligning strategic intent with sustainable practice, especially in conditions of ambiguity. CONTRIBUTION/VALUE-ADD : By presenting a framework that uncovers corporate sustainability interpretations in practice, the study not only advances scholarship in an underexplored domain but also highlights key theoretical tensions and outlines a future research agenda.
Stories of grit and grace : sociocultural pathways to success in township entrepreneurship
Mathebula, Veronica; Moos, Menisha; Crafford, Anne (World Scientific Publishing, 2026-07-16)
Entrepreneurship in South Africa’s townships is frequently examined through deficit-based lenses that emphasize poverty and infrastructure constraints, with less attention given to the sociocultural factors that sustain entrepreneurial success. Addressing this gap, this study explores how sociocultural factors facilitate and constrain successful township entrepreneurs in Gauteng, South Africa. Drawing on social network theory and a qualitative multiple-case narrative design, life story interviews were conducted with six established township entrepreneurs. The findings develop a contextually grounded typology of five interrelated sociocultural factors shaping entrepreneurial success: family, friends and other reference groups, culture, religion and political history. These factors generate multiple forms of capital, including relational, symbolic, spiritual and institutional resources, which entrepreneurs mobilize to access opportunities, build legitimacy, sustain resilience and create community effect. However, the findings also reveal the ambivalent nature of embeddedness, as the same networks that provide support may impose obligations and dependence. The study contributes to entrepreneurship scholarship in two ways. First, it offers a typology of sociocultural capital relevant to township economies. Second, it extends social network theory by showing that entrepreneurial networks in historically marginalized contexts are shaped not only by economic exchange but by identity, meaning-making and transcendent relationships. The article advances context-sensitive theorizing and offers practical insights for entrepreneurship development in townships and other Global South settings.
Unlocking shared value : reimagining multi-stakeholder collaboration for future-oriented post-school skills development
Selebi, Olebogeng; Ngwenya, Thembinkosi (Emerald, 2026-12-14)
PURPOSE : This study investigates how multi-stakeholder partnerships (MSPs) support skills development in the post-school education and training sector. It examines how MSPs enhance alignment between education systems and labour market needs within rapidly shifting economic and technological contexts. DESIGN/METHODOLOGY/APPROACH : Drawing on social systems and stakeholder theory, the study used 24 semi-structured interviews with senior system-level and leadership stakeholders across government, higher education, civil society, and industry. The qualitative, interpretivist approach extends global MSP literature by foregrounding coordination practices in a Global South setting. FINDINGS : Five themes emerged: stakeholder complementarity, strategic communication, institutional alignment, partnership sustainability, and shared visions of quality education. Although MSPs were widely valued, challenges included policy misalignment, unclear roles, and uneven engagement. RESEARCH LIMITATIONS/IMPLICATIONS : The sample reflects senior perspectives within one national context, excluding frontline educators and students. Future work should include longitudinal and mixed-methods designs to enhance applicability and incorporate a wider range of stakeholder voices. PRACTICAL IMPLICATIONS : Clearer role definition, stronger communication frameworks, curriculum-labour market alignment, and improved collaborative structures are recommended. SOCIAL IMPLICATIONS : Strengthened MSPs can support inclusive, future-oriented skills ecosystems that advance employability and socio-economic development. ORIGINALITY/VALUE : The study offers a systems-level perspective of MSPs as interdependent, intentionally structured arrangements. It contributes to global scholarship by identifying conditions that strengthen labour market alignment and skills pipeline coherence in resource-constrained contexts.
A thematic analysis of how small and medium-sized enterprises experience and adapt to exogenous shocks in Africa : a case study on anglophone countries
Zwane, Princess Nkambule; Moos, Menisha (Adonis and Abbey Publishers, 2026-03-01)
Research has extensively investigated how exogenous shocks (ES) affect small and medium-sized enterprises (SMEs). However, insights into how SMEs in Anglophone African countries experience and adapt to ES remain limited. This study reviewed the literature on SMEs and ES in Anglophone Africa, identifying the main themes and issues examined in this area over the past decade (2014-2024). The Scopus and Google Scholar databases were searched for research articles, and 38 articles were selected for analysis. A qualitative research approach was used, including an in-depth literature review and thematic analysis to uncover key themes and subthemes. The study found that SMEs in Anglophone Africa experience ES both negatively and positively. Their adaptive strategies to ES included innovation, pivoting, networking, and digitalisation. These findings align with elements identified in the literature. This study delineates critical research gaps in the intersection of SMEs and ES, providing nuanced insights from the Anglophone African perspective regarding SMEs’ experiences and their adaptive mechanisms to ES. Practically, these findings can assist policymakers in developing targeted interventions, avoiding a blanket approach, and enabling Anglophone African SMEs to make more informed decisions that enhance resilience and preparedness for future shocks.
Middle managers' sensemaking and sensegiving practices in a hybrid work context
Jimmy, Keeanne; Le Roux, Catherine Anne (JCMAN, 2025)
PURPOSE : Middle managers play a crucial role in interpreting and communicating organisational strategy, particularly in hybrid work environments where strategic objectives must be continuously adapted to operational realities. While existing research recognises their strategic influence, few studies explore middle managers' sensemaking and sensegiving practices within hybrid contexts. This study addresses this gap by exploring how middle managers navigate the complexities of a hybrid work environment to facilitate strategy transmission. DESIGN/METHODOLOGY/APPROACH : A qualitative case study was conducted within a large South African FMCG company. Data was collected through 12 semi-structured online interviews with middle managers from various business units. The study was guided by two research questions: (1) How do middle managers make and give sense within hybrid work contexts? (2) What practices are utilised by middle managers to interpret and transmit the strategy message? FINDINGS : Middle managers in the hybrid work context demonstrated how they transmitted strategic directives and recalibrated their actions. Sensemaking practices included re-establishing work boundaries, managing productivity perceptions, and leveraging informal communication networks. Key sensegiving practices included rephrasing strategic messages, personalising engagement, and fostering accountability. While digital tools support communication, in-person interactions remain essential for relational depth and strategic clarity. These findings offer insight into the situated micro-practices that bridge strategic intent and everyday work in a disrupted and evolving work environment. PRACTICAL IMPLICATIONS : The findings demonstrate the centrality of middle managers as meaning-makers and extend our understanding of how strategy work is performed under conditions of fluidity. To enhance strategic alignment, organisations should support middle managers by formalising hybrid work policies that balance digital and in-person collaboration. Management can use these practices to strengthen engagement, job crafting, work-life balance, and trust, thereby ensuring clarity in strategy execution.
Evaluating the role of consumer and business confidence in petroleum consumption
Das, Sonali; Phadi, Nteboheng Pamella (Inderscience, 2026-05-11)
Although organisations actively invest in technologies designed to capture customer data, the ability to translate this data into insight that is reflective of concurrent supply chain dynamics can be a non-trivial exercise. We examine both domestic capabilities, and global drivers, of the petroleum supply chain and undertake two investigations: 1) at the macro-level, we use secondary-data to investigate predictive models for consumption of petrol and diesel, both of which are directly and indirectly influenced by various local and international factors; 2) at the micro-level, we administered a tailored-questionnaire at a petroleum company to investigate any missing link between their supply-chain and their customer demand-chain with regards to three aspects namely, people, process and technology, which were then analysed within the structural equation model framework. Overall, the study provides valuable insights for future research aimed at better integration of customer sentiment and supply chain data analytics for petroleum procurement and planning.
Hybrid approaches to forecast daily average PM 2.5 concentrations : an application to the Brazilian state of Minas Gerais
Da Silva, Kim Leone Souza; Iftikhar, Hasnais; Rodrigues, Paulo Canas (Taylor and Francis, 2026)
The human impact on the planet is evident, and the need to mitigate these effects is becoming increasingly urgent. Air pollution, for instance, is inextricably linked to both the environment and human health. Particulate matter with diameters smaller than 2.5 micrometers, known as PM2.5, can have a severe impact on human health due to its ability to penetrate the respiratory system. The objective of this study is to forecast PM2.5 concentrations at monitoring stations within the Brazilian state of Minas Gerais. Hybrid models were developed to improve predictive performance by combining parametric, nonparametric, and neural network approaches for particulate matter forecasting. The results indicate that the proposed hybrid models perform competitively across various time horizons. These findings underscore the importance of combining different modeling approaches, leveraging each method's strengths to capture complex patterns and improve forecast accuracy. Furthermore, integrating parametric, nonparametric, and neural network techniques enables more robust, flexible modeling that can adapt to diverse temporal patterns and data characteristics. The methods developed in this study show promise and contribute to the advancement of research on air pollution in Brazil by providing a comparative analysis of the performance of forecasting models applied to different municipalities in Minas Gerais.
Functional time series modeling of traffic flow : a probabilistic approach to temporal symmetry
Jan, Faheem; Iftikhar, Hasnain; Gul, Naveed; Almuhayfith, Fatimah E.; Rodrigues, Paulo Canas (MDPI, 2026-05)
Reliable short-term traffic flow prediction is crucial for intelligent transportation systems to enable real-time control, mitigate congestion, and improve urban mobility. However, traffic dynamics are inherently uncertain, temporally dependent, and subject to pronounced intraday variability, making accurate forecasting challenging. To address these issues, this study introduces a Functional AutoRegressive (FAR) model that represents daily traffic profiles as continuous stochastic functions rather than discrete observations, thereby preserving temporal continuity and capturing underlying symmetric structures. The model is developed using high-frequency traffic data collected at 15-min intervals from the Dublin Airport Link Road, Ireland, covering January 2022 to December 2024; data from 2022–2023 are used for model estimation, while 2024 data are reserved for one-day-ahead out-of-sample evaluation. A moving-window filtering technique is incorporated to enhance robustness by probabilistically identifying outliers and reducing noise. The proposed FAR approach is benchmarked against conventional models, including autoregressive (AR), autoregressive moving average (ARMA), nonparametric autoregressive (NPAR), and vector autoregressive (VAR) models. Empirical results demonstrate that the FAR model consistently achieves superior forecasting performance across all traffic conditions, yielding a full-day MAPE of 9.160% compared to 11.623% for the VAR model, along with lower MAE (76.772) and RMSE (131.767). It also performs best on both workdays and weekends, with MAPEs of 8.129% and 10.438%, respectively. Moreover, the model remains robust across peak and off-peak periods, effectively capturing both symmetric and asymmetric traffic variations while offering a more interpretable representation of intraday patterns. These findings suggest that functional time series modeling provides an effective and computationally efficient framework for traffic forecasting, with strong potential for application in next-generation intelligent transportation systems.
A decomposition-driven hybrid approach to forecasting oil market dynamics
Dar, Laiba Sultan; Abdelwahab, Mahmoud M.; Aamir, Muhammad; Rind, Moeeba; Rodrigues, Paulo Canas; Abdelkawy, Mohamed A. (MDPI, 2026-03)
Modeling nonstationary time series in financial and energy markets remains challenging due to nonlinear dynamics, volatility clustering, and frequent regime shifts that distort the underlying probabilistic structure of the data. This study introduces a novel probabilistic–statistical decomposition framework, termed Robust Adaptive Decomposition (RAD), designed to preserve probabilistic symmetry between deterministic and stochastic components. In this context, symmetry refers to maintaining statistical balance—particularly in the means, variances, and distributional structures—between the extracted modes and the residual series, thereby preventing artificial bias or variance distortion during decomposition. The RAD framework adaptively determines the optimal number of modes needed to effectively separate short-term fluctuations from long-term structural movements. Unlike conventional techniques, such as Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and CEEMDAN, the proposed method incorporates a robustness mechanism that mitigates mode mixing and reduces distortions induced by extreme shocks and regime transitions. The empirical evaluation is conducted on six oil-related energy commodities—Brent crude oil, kerosene, propane, sulfur diesel, heating oil, and gasoline—whose price dynamics exhibit pronounced nonlinearity and structural volatility. When integrated with ARIMA forecasting models, the RAD-based framework consistently outperforms benchmark decomposition approaches. Across all datasets, RAD–ARIMA achieves reductions of approximately 65–90% in MAE, 60–85% in RMSE, and up to 95% in MAPE relative to CEEMDAN-based models. These results demonstrate that RAD provides a mathematically rigorous and computationally efficient preprocessing mechanism that preserves statistical equilibrium while effectively disentangling deterministic structures from stochastic noise. Beyond oil markets, the framework offers broad applicability in econometric modeling, financial forecasting, and risk management, contributing to probability- and statistics-driven symmetry analysis in complex dynamic systems.
A new class of median estimators using auxiliary information under PPS sampling : theoretical properties and empirical evaluation
Shah, Salman; Mahmoudi, Eisa; Qureshi, Moiz; Iftikhar, Hasnain; Rodrigues, Paulo Canas; Gonzales Medina, Ronny Ivan; Lopez-Gonzales, Javier Linkolk (Springer, 2026-03)
The use of auxiliary or supplementary information plays a crucial role in enhancing the efficiency of estimators in survey sampling. Among various measures of central tendency, the median has attracted considerable attention due to its robustness against outliers and skewed distributions. This study introduces a novel estimator for the finite population median that incorporates supplementary information under a probability proportional to size (PPS) sampling design. Analytical expressions for the bias and mean squared error (MSE) of the proposed estimator are derived up to the first order of approximation. The efficiency of the proposed estimator is evaluated through theoretical comparisons and empirical analyses against existing median estimators, using MSE and percent relative efficiency (PRE) as performance criteria. Furthermore, graphical representations are employed to illustrate the comparative performance. The proposed estimator is examined using three real-world datasets, and its precision is further validated through a comprehensive simulation study. The findings consistently demonstrate that the proposed estimator outperforms its existing counterparts in terms of efficiency and robustness.
The adoption puzzle : investigating the supply side determinants of blockchain technology adoption for entrepreneurial financing
Kuhlengisa, Mclntosh Mufunani; Eresia-Eke, Chukuakadibia E. (LPPM of Narotama University Surabaya, 2025-03-31)
PURPOSE : Access to financing is vital for the growth of entrepreneurial firms in emerging economies like South Africa. Technological innovations such as blockchain can reduce transaction costs and disrupt traditional models, offering benefits like reliability, trust, security, and efficiency. However, adoption barriers persist, including infrastructure limitations and the emerging nature of the technology. METHODOLOGY : This study employs a quantitative approach to investigate factors affecting the adoption of blockchain technology among employees of entrepreneurial financing firms through an online survey. Using Partial Least Squares Structural Equation Modelling and Artificial Neural Network Analysis (PLS-SEM ANN). FINDINGS : the findings indicate that facilitating conditions, social influence, anxiety, and attitude significantly impact the behavioral intention to adopt blockchain, while effort expectancy, performance expectancy, and self-efficacy do not. The study recommends creating supportive environments, leveraging social networks, addressing anxiety, and fostering positive attitudes toward blockchain. It suggests investing in infrastructure, increasing awareness of blockchain benefits, improving communication to alleviate anxieties, and showcasing success stories to enhance adoption. ORIGINALITY/VALUE : This paper is original
Analyzing entrepreneurial identity and capabilities as determinants for business sustainability in the dance Industry
Bornman, Dawid Alwyn Jacobus; James, Augustina Geertruida (Routledge, 2026)
This study explores how entrepreneurial identity and capabilities contribute to the adoption of innovative business practices in the contemporary dance industry, which has faced challenges exacerbated by the COVID-19 pandemic. Qualitative research interviews with fourteen dance professionals—company owners, studio owners, freelancers, and educators—provided insights into their entrepreneurial behaviors. The research identified five key themes: (1) entrepreneurial identity, (2) capabilities, (3) networks, (4) support structures, and (5) innovation. The pandemic forced dance businesses to find alternative revenue streams, which highlighted a lack of business skills and training as a shortcoming and the need for industry-specific entrepreneurial training programs. As academic research related to the dance industry as a creative business is almost nonexistent in a South African context, this study offers new insights into the industry and reveals that the pandemic pushed dance businesses to operate in new ways of doing business.
Psychological capital and firms' performance during crises : the role of entrepreneurial ecosystem quality
Kansheba, Jonathan Mukiza; Marobhe, Mutaju Isaack; Fubah, Clavis Nwehfor; Bhat, Mohd Abass (Emerald, 2026-06-02)
PURPOSE : The study integrates the conservation of resources (COR) and social embeddedness theories to explain how psychological capital (Psy-Cap) and entrepreneurial ecosystem quality (EEQ) affect the performance of small- and medium-sized enterprises (SMEs) during turbulence periods. DESIGN/METHODOLOGY/APPROACH : The study employes the structural equation modeling on a dataset of 364 Tanzanian SMEs to test both direct effects of Psy-Cap dimensions on firms' performance and the moderating effects of EEQ. FINDINGS : The study finds that self-efficacy, resilience and hope significantly enhance SMEs' performance during crises, whereas optimism negatively affects performance during prolonged turbulence. Furthermore, while EEQ significantly strengthens the positive effects of self-efficacy and resilience on performance but does not significantly moderate the relationships involving hope and optimism. The results highlight the nuanced role of psychological capital and entrepreneurial ecosystem quality in shaping firms' outcomes during turbulent times. ORIGINALITY/VALUE : This research contributes to the entrepreneurship literature by integrating psychological capital and the entrepreneurial ecosystem perspectives in a crisis context. It offers novel insights from a developing-country setting, thereby addressing the empirical gap in non-Western economies. Uniquely, the study reveals that excessive optimism may hinder firms' performance, challenging conventional assumptions. It also underscores the critical role of ecosystem quality in enhancing psychological resource utility for SME sustainability during crises.
A novel hybrid decomposition-based wind speed prediction model : application to Lahore, Pakistan
Noor, Malaika; Nazir, Hafiza Mamona; Qureshi, Moiz; Iftikhar, Hasnain; Rodrigues, Paulo Canas; Hashem, Atef F. (Institute of Electrical and Electronics Engineers, 2026-04-29)
Accurate modeling of Wind Speed (WS) data is essential in renewable energy, optimization of agriculture, and disaster risk reduction. However, in the presence of nonlinearity, non-stationarity, and noise in WS, the prediction becomes less efficient. A hybrid framework that combines denoising and decomposition has been developed using WS data from Lahore from 1986 to 2016. Four single-stage preprocessing techniques, namely Moving Average (MA), Singular Spectrum Analysis (SSA), Wavelet Transform (WAV), and Empirical Mode Decomposition (EMD), and a dual-stage denoising pipeline involving Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) were considered. To model the denoised WS data, statistical models, i.e., Autoregressive Moving Average (ARIMA), machine learning models, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Random Forest (RF), and Support Vector Machine (SVM); and deep learning models, namely Long Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network-LSTM (CNN-LSTM) have been implemented. Furthermore, the data were split 80-20 to assess the performance of the proposed models. Different evaluation metrics, i.e., Mean Absolute Error (MAE), Mean Bias Error (MBE), and Nash Sutcliffe Efficiency (NSE), as well as the Taylor diagram and error prediction box plot, have been used to compare the performance of the proposed methodology. Results confirmed that a simple model, such as ARIMA, with all preprocessing methods, is not suitable for WS prediction. However, SSA along with LSTM (complex model) achieved the best accuracy with least MAE and NSE i.e. 0.0627 and 0.9947 respectively. The application of a dual-stage VMD-EWT somewhat improved the performance of the weaker preprocessing methods, but it did not outperform single-stage denoising methods. Single-stage SSA, when combined with deep learning, yields the most reliable WS predictions, whereas dual-stage denoising improves stability and performance under weaker preprocessing methods.
Advanced data analysis with gretl
Yalta, A. Talha; Cottrell, Allin; Rodrigues, Paulo Canas (Springer, 2026-05)
The continuing growth of computationally intensive methods has made software an increasingly central component of empirical research in economics, econometrics, and statistics. In this context, the quality of scientific software matters not only in terms of numerical capability, but also in terms of transparency, accessibility, and reproducibility. These considerations are especially important in econometrics, where implementation details can materially affect empirical results and where broad access to reliable tools remains essential for both research and teaching.
Hierarchical structure of the entrepreneurial career competency instrument : evidence from frequentist and Bayesian bifactor structural equation modelling
Schaap, Pieter; Botha, Melodi (MDPI, 2026-04-08)
Robust measurement of entrepreneurial competencies (ECs) is crucial for entrepreneurship education, yet their internal structure remains theoretically contested and empirically underexamined. This study examined whether the four-factor Entrepreneurial Career Competency Instrument (ECCI) exhibits a hierarchical (bifactor) structure among South African entrepreneurs. Using two non-probability samples (N = 1305; N = 280), we analysed competing models, including a bifactor exploratory structural equation model (ESEM). The selected 56-item bifactor ESEM solution was examined for conceptual replicability in the smaller sample using Bayesian structural equation modelling (BSEM) with informative priors and sensitivity analyses to address small-sample uncertainty. Our findings revealed a theoretically supported hierarchical structure with a strong general factor and distinct specific factors: entrepreneurial career mindset, innovativeness, motivation, and implementation, enhancing the interpretation of scores. This study guides ECCI usage by suggesting total scores for broad assessments and domain scores for diagnostic feedback. Methodologically, the findings demonstrate that combining frequentist and Bayesian approaches across samples strengthened structural validity and provided insights into evaluating imprecise responses to self-report measures and addressing sampling constraints. Overall, this work contributes a robust structural model of the ECCI and enriches the EC literature, serving as a framework for refining, testing and applying attribute-based EC measures in diverse contexts.
Supply chain risk mitigation strategies for small, medium and large firms : lessons learned during COVID-19
Nel, Jacobus Daniel (AOSIS, 2025-10-16)
ORIENTATION : Many firms were not prepared for the disruptions caused by the coronavirus disease 2019 (COVID-19) pandemic. RESEARCH PURPOSE : The study determines how small, medium and large firms successfully responded to the pandemic. MOTIVATION FOR THE STUDY : There are lessons to be learned on how different sized firms successfully implemented risk mitigation strategies in response to the pandemic. These lessons can be used to manage future disruptions. RESEARCH DESIG, APPROACH AND METHOD : The study used quantitative research using an online survey instrument. The respondents were employed in different sized firms in South Africa during the COVID-19 pandemic and were all experienced in and knowledgeable on their firms’ supply chains. The research used analysis of variance procedures to compare the means of 230 small, medium and large firms across different research focus areas. MAIN FINDINGS : Small, medium and large firms successfully mitigated supply chain disruptions caused by the pandemic by increasing flexibility, agility, collaboration, visibility and adaptability across their supply chains. As a strategy, redundancy was preferred by larger firms. PRACTICAL/MANAGERIAL IMPLICATIONS : The findings identified increased flexibility, agility, collaboration, visibility and adaptability within different sized firms and across their supply chains as strategies to improve resilience and prepare for future disruptions. On redundancy, smaller firms used a low-diversification risk mitigation strategy, while larger firms implemented a high-diversification strategy, illustrating that both strategies can work. CONTRIBUTION/VALUE-ADD : The lessons learned from the identified supply chain risk mitigation strategies can motivate firms to prepare better for future disruptions.
Exploring the critical incident technique to inform distress causality and sensemaking
Jombe, Melody Kuziwa; Pretorius, Marius (AOSIS, 2025-05-09)
ORIENTATION : Before a business in distress can be turned around, it requires the accurate identification of the distress causality. Recognition and sensemaking of distress remains a significant factor to inform actions. RESEARCH PURPOSE : This study investigated and explored the application of the critical incident technique (CIT) to inform venture distress causality for sensemaking. MOTIVATION FOR THE STUDY : Often decision makers are faced with causal ambiguity and rationalism when attributing distress causality. Critical incident technique method has been extensively applied in health sciences as a diagnostic decision-making process to investigate causality. This study applied CIT method as a diagnostic tool to inform causality and origin of business venture distress. RESEARCH DESIGN, APPROACH AND METHOD : A qualitative study was conducted with a total of 25 participants who included business rescue practitioners (BRPs), creditors and managers. The data were collected through both semi-structured interviews and a card sorting activity. Thematic analysis indicated the core incidents. MAIN FINDINGS : The application of CIT method in causal attribution revealed a range of causes, distress origin and severity indicators of distressed ventures and the force that impels and propels management to act when faced with distress situations. PRACTICAL/MANAGERIAL IMPLICATIONS : Understanding distress causality and its associated origin is the first step for a successful turnaround. Management always faces challenges, critical incidents and crises that they fail to understand. Critical incident technique method may assist decision making. CONTRIBUTION/VALUE-ADDS : The study introduces CIT method to venture distress context, offering valuable insights into how decision makers can successfully attribute distress causality.
A multi-stage decomposition and hybrid statistical framework for time series forecasting
Abbasi, Swera Zeb; Abdelwahab, Mahmoud M.; Hussain, Imam; Qureshi, Moiz; Rind, Moeeba; Rodrigues, Paulo Canas; Hussain, Ijaz; Abdelkawy, Mohamed A. (MDPI, 2026-04)
Modeling and forecasting nonstationary and nonlinear economic time series remain fundamentally challenging due to structural breaks, volatility clustering, and noise contamination that distort the intrinsic stochastic structure. To address these limitations, this study proposes a novel three-stage hybrid statistical framework that systematically integrates multi-level signal decomposition with structured parametric modeling to enhance predictive accuracy. The proposed hybrid architectures—EMD–EEMD–ARIMA, EMD–EEMD–GMDH, and EMD–EEMD–ETS—employ a hierarchical decomposition–reconstruction strategy before forecasting. In the first stage, Empirical Mode Decomposition (EMD) decomposes the observed series into intrinsic mode functions (IMFs) and a residual component. In the second stage, Ensemble Empirical Mode Decomposition (EEMD) is applied to further refine the extracted components, mitigating mode mixing and improving signal separability. In the final stage, each reconstructed component is modeled using ARIMA, Exponential Smoothing State Space (ETS), and Group Method of Data Handling (GMDH) frameworks, and the individual forecasts are aggregated to obtain the final prediction. Empirical evaluation based on a recursive one-step-ahead forecasting scheme demonstrates consistent numerical improvements across all standard accuracy measures. In particular, the proposed EMD–EEMD–ARIMA model achieves the lowest forecasting error, reducing the root-mean-square error (RMSE) by approximately 6–7% relative to the best-performing single-stage model and by about 3–4% relative to the two-stage EMD-based hybrids. Similar improvements are observed in mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), indicating enhanced stability and robustness of the three-stage architecture. The results provide strong numerical evidence that multi-level decomposition combined with structured statistical modeling yields superior predictive performance for complex nonlinear and nonstationary time series. The proposed framework offers a mathematically coherent, computationally tractable, and systematically structured hybrid modeling strategy that effectively integrates noise-assisted decomposition with parametric and data-driven forecasting techniques.

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