Employee performance in the public sector is a crucial factor in determining the effectiveness of government services. However, low levels of motivation and non-participatory bureaucratic communication remain obstacles to improving employee performance. This study aims to analyze the influence of leadership style and communication style on employee performance, considering work motivation as a mediating variable. This study uses a quantitative approach with a survey method on 204 employees. Data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique using the SmartPLS 3 application. The results of the analysis indicate that leadership style and communication style have a positive and significant effect on employee motivation and performance, both directly and indirectly through motivation as a mediating variable. Motivation is proven to be a key factor that bridges the influence of these two variables on performance. This study reinforces the importance of participatory leadership style and open communication in building motivation and improving the performance of public sector employees. The practical implication is the importance of developing leadership capacity and a more inclusive communication system in government organizations.
Bangladesh has experienced continuous progress in the digital landscape, with a significant portion of the population now having access to online services. This rapid expansion has made online shopping more accessible and contributed to its growing popularity. This study aims to examine the consumer perceptions toward online shopping and identify the factors influencing consumer satisfaction in Sylhet city. Data were collected using purposive sampling from 150 respondents through a structured questionnaire. Descriptive statistics were used for demonstrating demographic characteristics of the respondents and to know the consumer perceptions on online shopping. Reliability analysis, factor analysis and multiple regression were employed to identify the significant factors that affect consumer satisfaction. The findings showed that most of the respondents became aware of online shopping through social media, purchased products from online occasionally and mainly used Facebook rather than another social platform. Factor analysis technique suggested that among the five identified factors product & service value is the most important factor affecting overall satisfaction of consumers. Furthermore, multiple regression analysis revealed that all the identified factors had a statistically significant influence on consumer satisfaction. This research finding gives valuable insight to online retailers on what aspect they should focus on to enhance consumer satisfaction.
This research introduces a dynamic, real-time and hybrid intelligent fuzzy Multi-Criteria Decision-Making (MCDM) framework for supplier evaluation in the uncertain logistics con-text. The proposed framework is based on fuzzy logic, dynamic entropy weighting, temporal Basic Unit-Interval Monotonic (BUM) aggregation, Dynamic Fuzzy TOPSIS and Adaptive Neuro-Fuzzy Inference System (ANFIS) optimization to enhance the accuracy, robustness and real-time responsiveness of the decision-making process. The framework dynamically changes the importance of each criterion as a result of the logistics conditions; and assigns greater weight to recent operational information through temporal weighting mechanisms. The fuzzy inference modelling is very useful to place the uncertainty and nonlinear decision relationship in control, and the optimization of ANFIS has improved its adaptive learning capability and prediction reliability. The proposed model was implemented and validated in MATLAB and Simulink environments with a dynamic logistics dataset, including the sup-plier evaluation criteria like shipping cost, lead time, reliability, cargo status, fulfillment ef-ficiency, and route risk. The experimental results yielded 97.0% prediction accuracy, a cor-relation coefficient of 0.933 and low prediction error, which indicated good learning capabil-ity and computational stability. In addition, the real-time Simulink implementation proved adaptive decision behavior in changing continuously logistics conditions. The results ob-tained show that the proposed hybrid intelligent framework is an efficient, scalable and reli-able solution for the applications of intelligent supplier selection and real-time logistics deci-sion making.
Whereas the introduction of toll road services in the Horn of Africa (HoA) region has been celebrated as a crucial initiative for meeting the growing demand for better transport services, improving citizens’ travel efficiency and stimulating economic development, academic research has done little in examining how these emerging services enhance benefits and satisfaction for users. The lack of empirical insights restricts operators’ ability to improve toll road conditions, customer service at plazas, and emergency response services. This study addresses this gap by conducting an empirical survey among 150 randomly selected toll road users in Ethiopia, a core state in the HoA region. Using a three-factor Transportation Service Quality Framework (TRSQ) and Importance-Performance Analysis (IPA), the findings reveal that toll road conditions and safety, and customer service at plaza dimensions measured using multi-item scales significantly determine users’ satisfaction. Although rescue and emergency response factors show an insignificant effect, our Importance-Performance Analysis highlights that management should still give this dimension due attention to enhance user satisfaction. Relevant managerial implications are discussed.
This study investigates the total factor productivity growth (TFPG) of India's organized manufacturing sector over 43 years (1980–2023), comparing pre- and post-liberalization periods. Using a gross output framework with three inputs (material, labour, capital) and a Translog index, the findings reveal negative TFPG in most disaggregated industries during the pre-reform era, a trend that largely persisted after liberalization. However, the sharpness of this negativity declined noticeably, and a positive shift emerged in the post-pandemic period (2016–17 to 2022–23), except in the chemical sector. Overall, aggregate manufacturing TFP showed pessimistic trends post-reforms. Despite this, Indian manufacturing remains vital for national economic growth, particularly outperforming selected countries during the COVID-19 pandemic. The results underscore the need for productivity-focused trade and industrial policies to help firms build competitive advantages and access export markets. This research offers novel insights into the long-standing debate on whether trade liberalization has significantly enhanced industrial performance in India's shifting policy landscape.
The survey assessed the strategies that could effectively improve the application of the participative leadership model in CSR infrastructure delivery in the Niger Delta with a quantitative research design approach. Through multi-stage sampling, a total of 125 participants comprising CSR officers, project managers, site supervisors and community liaison representatives, invited from the core Niger Delta states, Akwa Ibom and Bayelsa, responded to structured, cross-sectional questionnaires as a survey instrument. Data were descriptively analysed using frequency and percentage, while inferential statistics used the Kruskal-Wallis H test. The survey findings indicate 'Relevant training on adoption and implementation of a participative approach', 'Establishment of an organised community project office and non-permanent advisory committees', and 'Allocation of resources for participative engagement and commitment' as key strategies to enhancing the effective application of a participative approach to CSR infrastructure delivery. These strategic variables for fulfilling corporate responsibility are also a panacea for the effective application of the participative model. The study concludes that entrenching these variables would curb the current fragmented application of the model. Additionally, it would strengthen participative engagement and move CSR delivery from mere passive consultation to active partnership for sustainable regional progress and stability, fostering mutual trust and improving corporate-community relationships.
Despite 72 percent of organizations adopting artificial intelligence, nearly half of implementations fail due to employee resistance, a paradox that challenges technology acceptance models. Prior research has examined either cognitive acceptance or motivational responses in isolation, leaving the interplay between these pathways and their ethical contingencies unresolved. This research addresses this critical gap through a sequential explanatory mixed-method design combining a quantitative survey of 1,785 employees in AI-adopting organizations with in-depth qualitative interviews with 19 participants, analysed using SmartPLS 4 for structural equation modelling and NVivo 14 for thematic analysis. The findings reveal three key insights. First, psychological empowerment mediates the relationship between technology characteristics and job satisfaction more strongly than technology acceptance, a counterintuitive finding that challenges the technology acceptance model's 30-year dominance. Second, job satisfaction emerges as the strongest predictor of organizational performance. Third, ethical leadership moderates both pathways, such that high ethical leadership amplifies empowerment effects by 34 percent. This research advances a novel Ethical Dual-Pathway Technology Acceptance Model (EDP-TAM), offering actionable guidance for organizations to implement AI that simultaneously optimises performance and safeguards employee well-being.
The study aims to analyze trends in the number of leading Angolan insurance companies for benchmarking purposes in the sector between 2020 and 2023, using the DEA-VRS window model. The analysis was conducted by calculating the efficiency scores of each insurer in two-year-wide moving windows, followed by a comparison of efficiency distributions over time and the identification of the insurers that served as benchmarks in each window for benchmarking purposes. The analysis was conducted by calculating the efficiency scores of each insurer in two-year movable windows, followed by a comparison of efficiency distributions over time and the identification of the insurers that served as benchmarks in each window for benchmarking purposes. The results reveal a trend of increasing average efficiency scores and a concomitant reduction in the number of insurers identified as benchmarks, a pattern that was consistently evident across the three analysis windows. In practical terms, the findings also indicate that the universe of benchmarks is becoming more concentrated, which requires attention to the periodic updating of the analysis windows and the selection of variables to ensure that benchmarking continues to reflect efficient practices within the analyzed sample. The article stands out for exploring a little-studied market, using robust primary data, offering practical value by identifying benchmark insurers, and revealing an unexpected inverse relationship between average efficiency and the number of benchmarks DMUs.
Drawing on conservation of resources theory, this study examines how flexible human resource management affects employee deviant innovation. Survey data were collected from 282 employees working in information technology, machinery manufacturing, and biopharmaceutical firms in Shanghai, Hefei, and Zhejiang Province. Both dimensions of flexible human resource management, coordination flexibility and resource flexibility, were positively related to employee deviant innovation. Psychological resilience mediated both relationships. The supervisor-subordinate relationship moderated the link between psychological resilience and deviant innovation: the stronger the relationship, the weaker the positive effect of resilience on deviant innovation. These findings clarify how flexible human resource management shapes deviant innovation, identify the psychological mechanism through which it operates, and specify the relational condition under which that mechanism is strongest.
There are many studies made to explain the relationship between the organizational factors. Burden of them, tried to mathematically model this relationship using traditional methods like PLS (Partial Least Squares), SEM (Structural Equation Modeling), RA (Regression Analysis) or Co-relation analysis. Besides, the limited explanatory variables were recently applied in such studies. Additionally, there are very rare quantitative investigations discovering the structural cause and effect interconnections among the huge number of organizational factors. This research has been designed to first distinguish the cause and effect group among the 20 organizational factors and second to weight these factors as well. DEMATEL and SWARA methodologies are the main multi criteria tools used in the present study. Results show the top 4 factors of EBLPOC(Employees' Basic Level of Perception Regarding Organizational Complexities), OCB(Organizational Citizenship Behavior), BC(Business Climate) and EJP(Employee Job Performance) hold relatively the utmost weights (closely 0.826 together). EBLPOC, EJP and BC are the cause factors. OCB is recognized as the effect factor. Furthermore, it is worthy to mention that the factor of EBLPOC (as a qualitative variable which were usually missed or ignored in previous studies), is totally ranked at first position. Finally, present study is inherently readdressing the organization's leaders, chief executives, intermediate managers and policy makers to focus on some classical foremost factors like OCB, BC and EJP (the old wounds) again.
This study synthesizes what is known about time-to-impact in quality improvement (QI) and transformation initiatives and, drawing on a large body of High Performance Organization (HPO) transformation cases, identifies which factors delay or accelerate impact and which organizational practices shorten benefit lead-times. It combines a structured synthesis of academic studies addressing impact, time-to-impact, timing evidence, and associated factors/practices with a case-survey of 54 documented HPO transformation cases that were systematically coded using a common framework. Factors and practices are prioritized based on their recurrence across cases, after which the resulting empirical patterns are mapped back to literature-based categories to assess convergence and divergence. Across the HPO cases, the most recurrent sources of delay are weak change communication and dialogue, weak follow-up and accountability, inconsistent leadership attention, and limited transparency in performance measurement and reporting. The most recurrent accelerators are strong communication and knowledge sharing, a tight review cadence with closed feedback loops, transparent measurement and reporting, and cross-unit collaboration. The most frequently reported time-to-impact reduction practices include institutionalizing a recurring communication-and-review cadence, increasing measurement transparency, clarifying leadership roles and accountabilities, and creating structured dialogue channels supported by internal coaches or champions. Overall, the findings overlap substantially with the literature, but differ in emphasis, and the study positions time-to-impact as a distinct dimension of improvement effectiveness while offering an evidence-informed, prioritized set of practical levers grounded in both research and cross-case practice.
This paper explores the association between supplier management orientation (SMO), information integration (II), organizational culture (OC) and supplier delivery performance (SDP). A systematic questionnaire survey was employed to gather cross-sectional data from 403 manufacturing firms in Uganda's Greater Kampala Metropolitan Area were analyzed using SmartPLS 4. The study shows a full mediating role of OC in the relationship between SMO, II and SDP. A positively significant relationship was established between II and OC, and between OC and SDP. Also, the relationship between SMO and OC was positively significant. Whereas the effect of SMO, II, OC on SDP is significant, related literature remains limited. This signifies a deficiency in the current literature that necessitates additional research to investigate other external factors that substantially impact SDP. Managers need to nurture cultural qualities, like teamwork, openness, and responsiveness, that enhance employees to effectively interpret information and act in a coordinated manner. This study extends the Resource-Based View Theory to the SDP domain, by drawing insights on the mediating role of OC in the relationship between SMO and SDP.
Sugarcane must be harvested at the time of maturity, known as the Period of Industrial Utilization (PIU). The PIU analysis is expensive and is performed in a laboratory by measuring an index defined as Total Recoverable Sugar (TRS). The fact is that harvesting is the most expensive stage in sugarcane production and decision-making in this segment depends on TRS level estimates. However, forecasting models aimed at replacing laboratory analyses do not meet the reality regarding the estimation of the TRS index. There is a great demand for tools capable of estimating the index and/or the factors that affect TRS. In this context, this article presents a case study whose objective is to apply statistical models to estimate the TRS index in the sugarcane production of a mill in the interior of São Paulo, Brazil. Variance Analysis (ANOVA) with one classification and Multiple Linear Regression (MLR) models are applied by using Minitab®. These models are based on covariates related to the TRS index to estimate the productivity of 48,151 plots from the 2016/2017 to the 2022/2023 harvests. It is shown that the adjusted models identify the most important covariates (5% significance level) that affect productivity and the TRS index. The accuracy is satisfactory for all covariates of the adjusted MLR model and for the coefficients that measure the proportion of data variability for productivity (76%) and the TRS index (55%). This article brings important contributions to the sugar and ethanol industries worldwide and in Brazil.
This study investigates the dynamics of relative technical efficiency across Angolan universities from 2017 to 2022, aiming to establish operational benchmarks and support strategic decision-making in higher education. Data were collected from eighteen officially recognized public and private institutions using annual statistical reports published by MESCTI. The methodology relies on non-parametric intertemporal models, specifically DEA-VRS and Window DEA, which capture performance variations across consecutive time windows. Results include efficiency scores per institution and time frame, input and output slacks required to achieve technical efficiency, and identification of reference peers for benchmarking purposes. Findings reveal a consistently declining trend in average efficiency over the period, with UCC being the most frequently referenced unit. Critical variables such as enrollment and course offerings underscore the need for diversification and alignment with labor market demands. Estimated slacks offer actionable insights for policy and cost optimization. As a methodological extension, the study proposes investigating the relationship between variable-level inefficiencies (SBM) and global efficiency scores (CRS) to better understand interwindow performance trajectories.
This study investigates how effectively European Union Member States transform circular economy (CE) practices into social well-being, addressing a critical but underexplored dimension of circular transition research. While existing CE assessments primarily emphasize material flows, recycling performance, and resource productivity, far less is known about how circularity contributes to consumer-relevant and inclusive social outcomes. Using an output-oriented Data Envelopment Analysis (DEA) framework under variable returns to scale, the study evaluates the social efficiency of CE across 27 EU countries, drawing on indicators of circular material use, material intensity, international recycling flows, self-perceived health, social inclusion, and real income. The results reveal substantial heterogeneity in social efficiency, showing that higher levels of circular activity do not automatically translate into stronger well-being outcomes for consumers. Countries such as Denmark, Finland, Estonia, Ireland, Luxembourg, and Spain achieve full efficiency, indicating effective alignment between circularity practices, welfare structures, and socially inclusive outcomes. Conversely, several countries operate under decreasing returns to scale, suggesting that CE initiatives may dilute social effects and generate uneven distributional outcomes when implemented beyond their optimal capacity. Overall, the findings highlight the importance of policy coherence, institutional capability, and consumer-oriented governance in ensuring that circular economy strategies deliver inclusive and socially sustainable benefits.
This paper presents a novel hybrid model that integrates predictive and optimization techniques to enhance the scheduling and management of electricity generation in large-scale power systems, with a focus on the variability of photovoltaic (PV) energy. By combining a long short-term memory (LSTM) neural network with an optimization framework, the model forecasts PV power generation over a one-month horizon using historical data, validated against actual production. The optimization component, built on a refined large-scale power system model, incorporates these predictions using a block representation approach to simulate diverse generation technologies, including natural gas, fossil fuel-based thermal units, hydroelectric, PV, nuclear, and wind power plants. This integrated approach addresses the stochastic nature of renewable sources, distinguishing it from prior studies that focus solely on prediction or optimization. The Argentine Interconnection System (SADI) serves as the case study, leveraging over a decade of time-series data to evaluate the model’s performance. Results demonstrate reliable prediction and scheduling capabilities, achieving a low prediction error of approximately 0.01% for key PV sources. Implemented in Python within the Spyder environment, with TensorFlow and Keras for LSTM predictions and PYOMO for optimization, the model offers a practical and effective solution for system operators to optimize resource allocation in renewable-heavy power systems.
This research paper investigates the integration of Explainable Artificial Intelligence (XAI) into Predictive Maintenance (PdM) systems, aiming to enhance transparency, interpretability, and reliability in industrial applications. The primary contribution is the introduction of the Explainability Parameters (XPA) framework, which offers a structured methodology for evaluating and applying XAI in PdM. The study systematically reviews recent advancements and challenges in the literature, categorising explanations into pre-modelling, in-modelling, and post-modelling processes. It presents and analyses significant case studies across various industrial sectors to illustrate the practical implications and hurdles of XAI methodologies. Key findings indicate that while XAI significantly improves the effectiveness and trustworthiness of PdM by clarifying model predictions, its implementation is hindered by the complexity of industrial data and the absence of standardised evaluation methods. The XPA framework addresses these challenges by providing tailored metrics for specific applications and advocating for a multi-phase approach to convert technical outputs into actionable maintenance recommendations. The originality of this paper lies in its comprehensive review and the establishment of rigorous standards for assessing XAI methodologies, thereby bridging the gap between theoretical frameworks and practical applications. By promoting adaptable XAI frameworks that cater to real-world industrial needs, this study fosters trust in automated decision-making processes. It enhances the overall understanding of XAI's role in PdM.
The objectives of the study were to investigate 1) whether paternalistic leadership affects exploitative and exploratory innovation, and 2) whether intrinsic motivation and environmental dynamism moderate the direct effects of paternalistic leadership on exploitative and exploratory innovation. The study was conducted in Sri Lanka by taking a sample of respondents from the information technology sector. The results indicate a notable distinction between the factors driving exploitative and exploratory innovation. Both types of innovations are significantly affected by paternalistic leadership. However, it has a positive influence on exploitation while it has a negative influence on exploration. Intrinsic motivation significantly predicts only the exploratory innovation while environmental dynamism significantly predicts only the exploitative innovation. This divergence can be explained by the inherent differences in the nature of these two types of innovation. Overall, this research advances theoretical understanding and provides practical guidance.
Amidst rivalry and intricate value networks the relationship between Supply Chain Management (SCM) and Total Quality Management (TQM) has emerged as a fundamental element of contemporary operational strategies and is essential for securing long-term competitive benefits. This bibliometric analysis methodically charts the framework and research directions within this crucial overlap. Drawing on a collection of 371 publications from the Scopus database, this study offers an in-depth summary of the discipline's progression from 1994, to 2025. The study indicates a developed research area that has seen a significant rise in scholarly attention with the number of publications increasing more than threefold since 2017. Major contributions are regionally clustered with the USA, India and China standing out as the leading contributors. A thematic keyword map uncovers the fields framework: 'Total Quality Management' and 'Supply Chain Management' serve as central driving themes propelling the research. These are underpinned by fundamental themes, like 'sustainability'. The analysis also indicates an evolution in terminology, with older concepts like 'just in time' now appearing as declining themes, superseded by more integrated frameworks. This survey serves as a valuable resource for researchers and practitioners by providing a data-driven landscape of the field's foundational pillars, dominant topics, and future research trajectories.
The research aimed to examine the role of business incubators and accelerators in promoting innovation among Saudi entrepreneurs, in support of the Kingdom's Vision 2030 agenda of diversifying the economy and strengthening entrepreneurship. The study population consisted of male and female entrepreneurs in Saudi Arabia, and a random sample of 120 participants with varying educational levels and experiences was selected. The researcher used a quantitative approach based on a questionnaire whose validity and reliability were statistically verified. The results showed that most participants had previously joined incubators and accelerators, and emphasized their significant importance in developing ideas, building relationships, accelerating growth, and supporting projects with training and modern technologies. The research recommended the continuous development of these programs, facilitating government policies and procedures, and increasing entrepreneurs' awareness of the importance of innovation to achieve sustainable economic and social development.
