Hedonic shopping motivations, supermarket attributes, and shopper loyalty in transitional markets: Evidence from Vietnam

Purpose - This study aims to explore the impact of hedonic shopping motivations (HSM) and supermarket attributes (SMA) on shopper loyalty (SLO). Design/methodology/approach A sample of 608 supermarket shoppers in Ho Chi Minh City, Vietnam was surveyed to test the model. Structural equation modeling was used to analyze the data. Findings It was found that SMA and HSM had positive effects on SLO. It was also found that the impact of hedonic motivations on SLO was different between the younger and older, as well as lower and higher income groups of customers. However, no such difference was found between female and male shoppers. Research limitations/implications A major limitation of this study was the use of a sample drawn from one transitional market. Cross-national samples will be a direction for further research. Also, the study focuses on attitudinal loyalty. Behavioral loyalty should be taken into account in future research. Practical implications The findings suggest that the supermarket managers showed concentrate their positioning strategies not only on the utilitarian dimension but also on the hedonic motivations to stimulate SLO, especially for older and higher income segments of customers. Originality/value The major contribution of the study is to empirically examine the role of hedonic motivations in SLO in Vietnam, a transitional market

The role of market and learning orientations in relationship quality: Evidence from Vietnamese exporters and their foreign importers

This study examines the roles of market and learning orientations in relationship quality between exporters in transition economies and their foreign importers and subsequently, export performance. A random sample of 283 export firms in Vietnam provides evidence to support the hypothesized main effects. The results further indicate that learning orientation plays a role in building high-quality relationships for both new and mature relationships. However, the impact of market orientation on relationship quality is found only in the new relationship. In addition, firm-ownership structure does not moderate the relationships between learning orientation, market orientation, relationship quality, and export performance

Optimizing culture conditions for the production of endo-&#946-1,4-glucanase by Aspergillus awamori strain Vietnam Type Culture Collection (VTCC)-F099

In the present study, twenty six strains of Aspergillus awamori from the Vietnam Type Culture Collection (Institute of Microbiology and Biotechnology, Vietnam University Hanoi) were used for the endoglucanase production by growing at 37°C in the growth medium. Result showed that A. awamori strain VTCC-F099 produced the highest level of endo β-1,4-glucanase in the growth medium, pH 6.5, at 30°C for 96 h, agitated at 200 rpm. The optimal concentration of the inducer CMC (carboxymethyl cellulose) for the endoglucanase production by A. awamori VTCC-F099 was 2%. Among tested carbon sources (coconut fiber, coffee shell, corncob, dried tangerine skin, peanut shell, rice bran, saw dust, sugar-cane bagasse as organic wasters and glucose, lactose sucrose as pure carbon sources), corncob showed the highest endoglucanase production by A. awamori VTCC-F099 at the concentration of 3%. Ammonium acetate was the best among nitrogen source (casein, peptone, fish powder, soybean powder as organic sources and CH3COONH4, NH4NO3, (NH4)2SO4, urea as inorganic sources) for the endoglucanase production by A. awamori VTCC-F099 at the concentration of 0.3%.Key words: Aspergillus awamori, carboxymethyl cellulose, endoglucanase production, optimization of culture conditions

Cloning, high-level expression, purification and characterization of a staphylokinase variant, SakøC, from Staphylococcus aureus QT08 in Escherichia coli BL21

The staphylokinase (Sak) is emerging as an important thrombolytic agent for the treatment of patients suffering from cardiovascular disease. Hence in this study, we reported the cloning, high-level expression, purification and characterization of the Sak variant SakøC from Staphylococcus aureus QT08 in Escherichia coli Bl21. The sak gene of 489 bp encoding a protein (163 amino acids) with a predicted molecular mass of 18.5 kDa and pI 7.28 showed 99.8 to 99.6% identity with corresponding sequences from S. aureus strains deposited in GenBank (AF332619, X00127, EF122253 and M57455). The DNA sequence (411 bp) encoding the mature Sak (15.5 kDa) truncated 27 N-terminal amino acids was expressed in E. coli BL21/pESak under the control of the strong promoter tac in the presence of isopropyl-β-D-1-thiogalactopynoside (IPTG) as inducer. The expression level of rSak was estimated at about 42% of the total cellular proteins by densitometry scanning, which is the highest expression level of rSak expressed in any E. coli system. The recombinant staphylokinase was purified by Ni2+- ProBondTM column to a single homogeneous 16-kDa band on sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) with a specific activity of 15175 U/mg protein, a recovery yield of 58% and a purification factor of 2.56. The optimal pH and temperature for the rSak activity was 9 and 37°C, respectively. rSak was stable over a temperature range of 25 to 50°C and at pH range of 7 to 9. Metal ions and detergents also showed an inhibitory effect on rSak, especially Zn2+ and Cu2+ which completely inhibited the enzymatic activity.Key words: Staphylococcus aureus QT08, staphylokinase, cloning, high-level expression, purification, characterization

Proof-of-Stake Consensus Mechanisms for Future Blockchain Networks: Fundamentals, Applications and Opportunities

© 2013 IEEE. The rapid development of blockchain technology and their numerous emerging applications has received huge attention in recent years. The distributed consensus mechanism is the backbone of a blockchain network. It plays a key role in ensuring the network's security, integrity, and performance. Most current blockchain networks have been deploying the proof-of-work consensus mechanisms, in which the consensus is reached through intensive mining processes. However, this mechanism has several limitations, e.g., energy inefficiency, delay, and vulnerable to security threats. To overcome these problems, a new consensus mechanism has been developed recently, namely proof of stake, which enables to achieve the consensus via proving the stake ownership. This mechanism is expected to become a cutting-edge technology for future blockchain networks. This paper is dedicated to investigating proof-of-stake mechanisms, from fundamental knowledge to advanced proof-of-stake-based protocols along with performance analysis, e.g., energy consumption, delay, and security, as well as their promising applications, particularly in the field of Internet of Vehicles. The formation of stake pools and their effects on the network stake distribution are also analyzed and simulated. The results show that the ratio between the block reward and the total network stake has a significant impact on the decentralization of the network. Technical challenges and potential solutions are also discussed

Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks

© 2019 IEEE. In this paper, we propose a novel approach to jointly address energy management and network throughput maximization problems for heterogeneous IoT low-power wireless communication networks. In particular, we consider a low-power communication network in which the IoT devices can harvest energy from a dedicated RF energy source to support their transmissions or backscatter the signals of the RF energy source to transmit information to the gateway. Different IoT devices may have dissimilar hardware configurations, and thus they may have various communications types and energy requirements. In addition, the RF energy source may have a limited energy supply source which needs to be minimized. Thus, to maximize the network throughput, we need to jointly optimize energy usage and operation time for the IoT devices under different energy demands and communication constraints. However, this optimization problem is non-convex due to the strong relation between energy supplied by the RF energy source and the IoT communication time, and thus obtaining the optimal solution is intractable. To address this problem, we study the relation between energy supply and communication time, and then transform the non-convex optimization problem to an equivalent convex-optimization problem which can achieve the optimal solution. Through simulation results, we show that our solution can achieve greater network throughputs (up to five times) than those of other conventional methods, e.g., TDMA. In addition, the simulation results also reveal some important information in controlling energy supply and managing low-power IoT devices in heterogeneous wireless communication networks

Learning Latent Distribution for Distinguishing Network Traffic in Intrusion Detection System

© 2019 IEEE. We develop a novel deep learning model, Multi-distributed Variational AutoEncoder (MVAE), for the network intrusion detection. To make the traffic more distinguishable, MVAE introduces the label information of data samples into the Kullback-Leibler (KL) term of the loss function of Variational AutoEncoder (VAE). This label information allows MVAEs to force/partition network data samples into different classes with different regions in the latent feature space. As a result, the network traffic samples are more distinguishable in the new representation space (i.e., the latent feature space of MVAE), thereby improving the accuracy in detecting intrusions. To evaluate the efficiency of the proposed solution, we carry out intensive experiments on two popular network intrusion datasets, i.e., NSL-KDD and UNSW-NB15 under four conventional classifiers including Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The experimental results demonstrate that our proposed approach can significantly improve the accuracy of intrusion detection algorithms up to 24.6% compared to the original one (using area under the curve metric)