Relationships between leadership styles and organisational innovation in Vietnamese public enterprises: the mediating role of employee creativity

Innovation plays an increasingly important role in the survival and growth of all organisations irrespective of public or private enterprises. Although public enterprises are critical vehicles for innovation, their innovative activities are lower and less efficient than private-owned ones, respectively in emerging and developing economies compared to developed ones. While research pointed out weak innovative capabilities of Vietnamese state-owned and private enterprises and the underdeveloped national innovation systems, most innovative activities occurred in technical, product and process arena. As an immediate source of sustainable competitive advantage, studies on organisational innovation at workplaces, business practices and external relations that refer to organisational methods are still rare in terms of academic publications, particularly regarding the performance of Vietnamese enterprises. Inadequate research on innovation are also recorded in the manufacturing and processing industry in Vietnam, specifically public coffee enterprises. Additionally, leadership remains an important factor that influences creativity and innovation in the organisations. Prior studies affirmed the existing linkages between leadership styles, specifically transformational and transactional leadership, employee creativity and innovation at the organisational level. However, these interrelationships are not fully understood, which is evidenced by the fact that the potential mediating role of employee creativity has not been unveiled. Moreover, existing gaps in the relationship between leadership role and innovation also influence the organisational effectiveness of public enterprises, which necessitates further investigation. Therefore, expanding and replicating past research, this study aims to look into the linkages between transformational leadership, transactional leadership and its dimensions on employee creativity and components of organisational innovation at public enterprises operating in the Vietnamese coffee sector. Additionally, employee creativity as a potential mediator in the leadership styles – organisational innovation relationships was examined. A cross-sectional, quantitative and non-experimental correlation design was adopted in this study. Data were collected using a questionnaire from 369 employees working across departments in 39 Vietnamese public coffee enterprises nationwide utilising cluster sampling technique. IBM-SPSS and AMOS software were used to test the proposed theoretical model following by formulated hypotheses based on the collected data. Accordingly, descriptive analysis, inferential statistics, confirmatory factor analysis, measurement of fit indices as well as analyses for modification purposes were conducted. Additionally, by applying structural equation modelling approach, the path coefficient total effects of all variables on the dependent construct were investigated and assessing the mediating effect was also proceeded. Findings of this study revealed that transformational leadership, transactional leadership, employee creativity were significant predictors of organisational innovation. Regarding the relationship between transformational leadership, its four dimensions and organisational innovation, transformational leadership was instrumental to organisational innovation. Moreover, apart from idealised influence, inspirational motivation, intellectual stimulation and individualised consideration were found to significantly influence business practices, workplace organisation and external relations and these effects were positive. The analysis of the relationship between transactional leadership and organisational innovation revealed that two dimensions of transactional leadership, namely contingent rewards and management by exception active had a significantly negative effect on three dimensions of organisational innovation. Similarly, findings partially supported the significantly positive effect of all dimensions of transformational leadership on employee creativity except for idealised influence. On the other hand, transactional leadership and its two dimensions were detrimental to employee creativity. Besides, employee creativity was found to significantly affect all dimensions of organisational innovation. Finally, employee creativity partially mediated transformational leadership - organisational innovation relationship. Likewise, the transactional leadership – organisational innovation relationship was also partially mediated by employee creativity. Results of this study contribute to literature on leadership styles, creativity and innovation, looking into the context of Vietnamese public enterprises in the coffee industry. Specifically, this study provides precious empirical results in examining the effects of transformational and transactional leadership on employee creativity and organisational innovation. Accordingly, organisational innovation is more likely to be fostered by the positive influence of leadership behaviours and the improvement of employee creativity. Therefore, it benefits the management of organisations and policy makers by enlightening the management to recognise which leadership style will effectively suit public enterprises in tandem with promoting employee creativity to foster organisational innovation. Additionally, results also highlighted the significant role of employee creativity in enhancing the positively direct effect of transformational leadership on organisational innovation and modifying the negatively direct effect of transactional leadership on organisational innovation. Thus, the management of public enterprises in Vietnam should employ flexible and reformed policies regarding benefits and competitive gains to improve employee creativity, which in turns boosts organisational innovation. Equally important, selecting employees with required skills and appropriate personality profile concerning creative abilities must be considered during the recruitment process in the enterprises

Hierarchical Sliced Wasserstein Distance

Sliced Wasserstein (SW) distance has been widely used in different application scenarios since it can be scaled to a large number of supports without suffering from the curse of dimensionality. The value of sliced Wasserstein distance is the average of transportation cost between one-dimensional representations (projections) of original measures that are obtained by Radon Transform (RT). Despite its efficiency in the number of supports, estimating the sliced Wasserstein requires a relatively large number of projections in high-dimensional settings. Therefore, for applications where the number of supports is relatively small compared with the dimension, e.g., several deep learning applications where the mini-batch approaches are utilized, the complexities from matrix multiplication of Radon Transform become the main computational bottleneck. To address this issue, we propose to derive projections by linearly and randomly combining a smaller number of projections which are named bottleneck projections. We explain the usage of these projections by introducing Hierarchical Radon Transform (HRT) which is constructed by applying Radon Transform variants recursively. We then formulate the approach into a new metric between measures, named Hierarchical Sliced Wasserstein (HSW) distance. By proving the injectivity of HRT, we derive the metricity of HSW. Moreover, we investigate the theoretical properties of HSW including its connection to SW variants and its computational and sample complexities. Finally, we compare the computational cost and generative quality of HSW with the conventional SW on the task of deep generative modeling using various benchmark datasets including CIFAR10, CelebA, and Tiny ImageNet.Comment: 28 pages, 7 figures, 3 table

Improving Generative Flow Networks with Path Regularization

Generative Flow Networks (GFlowNets) are recently proposed models for learning stochastic policies that generate compositional objects by sequences of actions with the probability proportional to a given reward function. The central problem of GFlowNets is to improve their exploration and generalization. In this work, we propose a novel path regularization method based on optimal transport theory that places prior constraints on the underlying structure of the GFlowNets. The prior is designed to help the GFlowNets better discover the latent structure of the target distribution or enhance its ability to explore the environment in the context of active learning. The path regularization controls the flow in GFlowNets to generate more diverse and novel candidates via maximizing the optimal transport distances between two forward policies or to improve the generalization via minimizing the optimal transport distances. In addition, we derive an efficient implementation of the regularization by finding its closed form solutions in specific cases and a meaningful upper bound that can be used as an approximation to minimize the regularization term. We empirically demonstrate the advantage of our path regularization on a wide range of tasks, including synthetic hypergrid environment modeling, discrete probabilistic modeling, and biological sequence design.Comment: 28 pages, 2 figures, 5 tables. Anh Do, Duy Dinh, and Tan Nguyen contributed equally to this wor

On the Performance of Power Beacon-Assisted D2D Communications in the Presence of Multi-Jammers and Eavesdropper

In this work, we investigate the performance analysis of a device-to-device (D2D) communication network under an eavesdropper E attack. Besides, we assume that E is located in the proximal region where it can overhear the information from the source S. Specifically, S transmits information to the destination D, adopting the power beacon's energy to surmount the limited energy budget. Moreover, to reduce the quality of the eavesdropping link, the cooperative jamming technique can be used, where the multi-friendly jammers are employed to generate the artificial noises to E continuously. As considering the above presentation, we derive the quality of system analysis in terms of the outage probability (OP), intercept probability (IP), and secrecy outage probability (SOP) of the proposed system model. Finally, the Monte-Carlo simulations are performed to corroborate the exactness of the mathematical analysis.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited

Interference Analysis for OFDM Transmissions in the Presence of Time-Varying Channel Impairments

This paper is concerned with the detrimental effect of phase noise on the performance of orthogonal frequency division multiplexing (OFDM) transmissions over time-selective channels. In the literature, most of the existing papers analyze the performance of OFDM systems in the presence of either time-selective channels or phase noise. Unlike the existing studies, this paper formulates an approximate expression of signal-to-interference-plus-noise ratio (SINR) at an OFDM receiver in the presence of both phase noise and time-selective channel response. The formulated SINR expression can be used as a guideline in determining appropriate OFDM transmission settings under a given quality-of-service (QoS) requirement. To illustrate the tightness of the approximate SINR formulation, empirical and theoretical values of SINR under different OFDM system settings are presented in this paper

INVESTIGATION ON HYDROLOGIC PERFORMANCE OF PERVIOUS CONCRETE PAVEMENT BY FINITE ELEMENT ANALYSIS

Pervious concrete pavement has been used widely as an effective practice for water management in low-impact development techniques. The hydrologic performance of pervious concrete pavement depends significantly on the rainfall intensity and the designed slope. This study assessed the hydrologic performance of pervious concrete pavement by evaluating the time for surface ponding via finite element analysis. A series of simulations were carried out to explore the relationship between hydrologic performance and pervious concrete pavement by the Hydrus 2D program. The research’s results showed that as the slope increased, the time of surface ponding also increased. The data indicated that the slope variable had a low impact on the water level in pervious concrete pavement under a constant rainfall intensity. Observation of the effect of rainfall intensity showed that when the rainfall intensity increased twofold, the time for surface ponding dropped about two times. Furthermore, when surface ponding appeared, pervious concrete pavement at higher rainfall intensity had lower water content. The rainfall intensity also significantly affects the hydrologic performance of the pervious concrete pavement. This study only assessed the hydrologic performance by using the time for surface ponding via finite element analysis. Further experimental studies should be conducted to examine the relationship of other factors to the hydrologic performance of pervious concrete pavement

Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning

Unsupervised and semi-supervised learning are important problems that are especially challenging with complex data like natural images. Progress on these problems would accelerate if we had access to appropriate generative models under which to pose the associated inference tasks. Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Neural Rendering Model (NRM), a new probabilistic generative model whose inference calculations correspond to those in a given CNN architecture. The NRM uses the given CNN to design the prior distribution in the probabilistic model. Furthermore, the NRM generates images from coarse to finer scales. It introduces a small set of latent variables at each level, and enforces dependencies among all the latent variables via a conjugate prior distribution. This conjugate prior yields a new regularizer based on paths rendered in the generative model for training CNNs-the Rendering Path Normalization (RPN). We demonstrate that this regularizer improves generalization, both in theory and in practice. In addition, likelihood estimation in the NRM yields training losses for CNNs, and inspired by this, we design a new loss termed as the Max-Min cross entropy which outperforms the traditional cross-entropy loss for object classification. The Max-Min cross entropy suggests a new deep network architecture, namely the Max-Min network, which can learn from less labeled data while maintaining good prediction performance. Our experiments demonstrate that the NRM with the RPN and the Max-Min architecture exceeds or matches the-state-of-art on benchmarks including SVHN, CIFAR10, and CIFAR100 for semi-supervised and supervised learning tasks

Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data

Modern deep neural networks have achieved impressive performance on tasks from image classification to natural language processing. Surprisingly, these complex systems with massive amounts of parameters exhibit the same structural properties in their last-layer features and classifiers across canonical datasets when training until convergence. In particular, it has been observed that the last-layer features collapse to their class-means, and those class-means are the vertices of a simplex Equiangular Tight Frame (ETF). This phenomenon is known as Neural Collapse ($\mathcal{NC}$). Recent papers have theoretically shown that $\mathcal{NC}$ emerges in the global minimizers of training problems with the simplified ``unconstrained feature model''. In this context, we take a step further and prove the $\mathcal{NC}$ occurrences in deep linear networks for the popular mean squared error (MSE) and cross entropy (CE) losses, showing that global solutions exhibit $\mathcal{NC}$ properties across the linear layers. Furthermore, we extend our study to imbalanced data for MSE loss and present the first geometric analysis of $\mathcal{NC}$ under bias-free setting. Our results demonstrate the convergence of the last-layer features and classifiers to a geometry consisting of orthogonal vectors, whose lengths depend on the amount of data in their corresponding classes. Finally, we empirically validate our theoretical analyses on synthetic and practical network architectures with both balanced and imbalanced scenarios.Comment: 93 pages, 20 figures, 4 tables. Hien Dang and Tho Tran contributed equally to this wor

How to drive brand engagement and EWOM intention in social commerce: A competitive strategy for the emerging market

Brand engagement and eWOM intention have been found to be critical factors driving competitive advantage for companies, as the evolution of social networking sites has changed the perspective of how companies engage with customers. Based on social exchange theory, the current research proposes an empirical model that emphasizes (1) the unique role of social commerce characteristics, including personalization, socialization, and information availability, in enhancing consumer-brand engagement, (2) the connection between consumer-brand engagement and eWOM intention, and (3) the moderating influence of trust towards such connection. A survey of 248 Facebook users with online shopping experience was employed. By using PLS-graph 3.0, structural equation modelling, the findings demonstrate that personalization and socialization positively influence brand engagement, which in turn leads to eWOM intention. Furthermore, trust moderates the brand engagement-eWOM intention relationship. Unexpectedly, information availability has shown no significant effect on brand engagement. The study encompasses the knowledge of social exchange theory into the social commerce environment by investigating the linkage between the social commerce environment and brand engagement. It contributes value to marketing theories by describing the moderating role of trust from the viewpoint of Gen Y. In addition, the study's findings may shed light on how firms in emerging markets can increase competitiveness by stimulating brand engagement and eWOM intention, as well as enhancing consumer trust in the comments regarding the products/services within the social commerce environment. © 2020 Tomas Bata University in Zlín. All rights reserved.Internal Grant Agency of the Faculty of Management and Economics, Tomas Bata University in Zlin [IGA/FaME/2018/015

Indicators for TQM 4.0 model: Delphi Method and Analytic Hierarchy Process (AHP) analysis

Anchoring on Socio-technical system (STS) theory, this study applied Delphi and analytic hierarchy process (AHP) techniques to explore the key factors and specific indicators of the TQM 4.0 model implementation in manufacturing enterprises. An analysis of two Delphi rounds through experts who are academia, consultants, and production/quality supervisors/managers found ten factors and 41 indicators. In the third round, the study weighted the importance of each factor and indicator through an analysis of the AHP technique. The research suggested that social factors were more important than technical factors. Importantly, the findings indicated three key factors of the TQM 4.0 model, including top management, quality culture 4.0, and integrating sustainable development. Furthermore, the study revealed that top management commitment, quality-driven mindfulness, and employee empowerment were specified as the most critical indicators of the TQM 4.0 model. Results could be valuable for both researchers and practitioners in assessing TQM 4.0 implementation in the manufacturing sector in the future. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Tomas Bata University in Zlin [VaV-IP-RO/2020/01