This research was conducted to investigate the impact of equity in foreign direct investment (FDI) firms on accountants' loyalty in Thanglong industrial park, Hanoi, Vietnam based on Equity theory. Data were collected through a survey with 150 accountants from Thanglong industrial park. With this data, we have used Cronbach's Alpha, Exploratory Factor Analysis (EFA) and correlation analysis to determine the effects of the independent variables on the dependent variable, i.e. the loyalty of accountants. The results show that the equity in FDI firms had positive relationships with the loyalty of accountants in Thanglong industrial park. Based on this finding, this paper gives several recommendations for improvement the loyalty of accountants in Thanglong industrial park.

The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

Electrical Discharge Machining (EDM) is one of the most basic non-conventional machining processes for production of complex geometries and process of hard materials, which are difficult to machine by conventional process. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The present study is focusing on the die sinking electric discharge machining (EDM) of AISI H 13, W.-Nr. 1.2344 Grade: Ovar Supreme for finding out the effect of machining parameters such as discharge current (GI), pulse on time (POT), pulse off time (POF) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) & Overcut (OC) using Square-shaped Cu tool with Lateral flushing. A well-designed experimental scheme is used to reduce the total number of experiments. Parts of the experiment are conducted with the L9 orthogonal array based on the Taguchi methodology and significant process parameters are identified using Analysis of Variance (ANOVA). It is found that MRR is affected by gap current & Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments are determined by which factor is most affected by the responses of MRR, Ra and OC. These experimental data are further investigated using Grey Relational Analysis to optimize multiple performances in which different levels combination of the factors are ranked based on grey relational grade. The analysis reveals that substantial improvement in machining performance takes place following this technique.

Vertex and p-center problems are two well-known types of the center problem. In this paper, a p-center problem with uncertain demand-weighted distance will be introduced in which the demands are considered as fuzzy random variables (FRVs) and the objective of the problem is to minimize the maximum distance between a node and its nearest facility. Then, by introducing new methods, the proposed problem is converted to deterministic integer programming (IP) problems where these methods will be obtained through the implementation of the possibility theory and fuzzy random chance-constrained programming (FRCCP). Finally, the proposed methods are applied for locating bicycle stations in the city of Tabriz in Iran as a real case study. The computational results of our study show that these methods can be implemented for the center problem with uncertain frameworks.

This paper focuses on the exploitation of the response surface methodology (RSM) to determine optimum cutting conditions leading to minimum surface roughness and cutting force components. The technique of RSM helps to create an efficient statistical model for studying the evolution of surface roughness and cutting forces according to cutting parameters: cutting speed, feed rate and depth of cut. For this purpose, turning tests of hardened steel alloy (AISI 4140) (56 HRC) were carried out using PVD – coated ceramic insert under different cutting conditions. The equations of surface roughness and cutting forces were achieved by using the experimental data and the technique of the analysis of variance (ANOVA). The obtained results are presented in terms of mean values and confidence levels. It is shown that feed rate and depth of cut are the most influential factors on surface roughness and cutting forces, respectively. In addition, it is underlined that the surface roughness is mainly related to the cutting speed, whereas depth of cut has the greatest effect on the evolution of cutting forces. The optimal machining parameters obtained in this study represent reductions about 6.88%, 3.65%, 19.05% in cutting force components (Fa, Fr, Ft), respectively. The latters are compared with the results of initial cutting parameters for machining AISI 4140 steel in the hard turning process.

Social commerce is a first-generation platform that uses a variety of social attributes to attract the attention of internet users. The impact of platform interactivity components; Content Usefulness, Engaging, Real-time communication, and E-wom on customers' purchase intentions was investigated in this study. A Quantitative research method was applied in this study using an online self-administrative questionnaire that was published using Google Docs to collect data, and 521 consumers participated in an online survey. The research used SPSS and AMOS to validate and test the hypotheses. All platform interactivity components were significantly affecting purchase intention. This study provides insights to marketing managers of how consumers perceive social commerce and what affecting them along with practical and theoretical implications.

This paper formulates robust optimization models for the problem of finding near-optimal locations for new intermodal terminals and their capacities for a railroad company, which operates an intermodal network in a competitive environment with uncertain demands. To solve the robust models, a Simulated Annealing (SA) algorithm is developed. Experimental results indicate that the SA solutions (i.e. objective function values) were comparable to those obtained using GAMS, but the SA algorithm could obtain solutions faster and could solve much larger problems. In addition, the results verify that solutions obtained from the robust models were more effective in dealing with uncertain demand scenarios.

The advent of digital marketing has replaced traditional marketing and shed a great impact on the global economic paradigm. Digital market helps to flourish the service sector by offering the customers desired products. Therefore, the aim of this study is to examine the impact of digital marketing capabilities on business performance enhancement in the United Arab Emirate (UAE) insurance sector. This study used the cross-sectional survey questionnaire method for data collection. This study empirically validated that digital marketing capabilities have a significant impact on business performance enhancement by using the partial least square-structural equation modeling analysis. The findings of this study will be helpful for the regulators and policymakers to design a policy to enhance the business performance by using the means of digital marketing.

This study aims to examine how Social Media Influencers (SMIs) influence patronage intentions (PI) through the influence of mediation on the brand credibility (BC) in the electronic devices sector in Jordan. Furthermore, the data were obtained by an online questionnaire based on Google Forms and a total of 315 followers in Jordan using an appropriate sampling method. structural equation modeling using Smart PLS 3 was used to investigate the relationships between SMIs, brand credibility, and patronage intentions (PI). The results indicated that SMIs directly influence PI and BC. BC Partially mediates the relationship between SMIs and PI. Research limitations in this study focus on a single sector electronic device at a specific location in Jordan. Future research may conduct cross-cultural studies to improve the generalizability of the present research findings.

The current research scrutinizes the relationship between the three model commitment components (affective, normative, and calculative commitment) and their various influences on customer loyalty. This is particularly in the banking sector setting in Jordan. A self-reported questionnaire was distributed to collect primary data for analysis. 333 completed questionnaires were analyzed via using PLS software to extract the effect of e-service quality on the relationship between customer commitment and loyalty. The results of this study demonstrate that the affective type of commitment has a positive impact on customer loyalty followed by normative commitment and lately by calculative commitment. Moreover, the results show that the influence of the dimensions of customer’s commitment on loyalty is moderated by e-service quality. This study indicates that affective commitment elements (self-identification, sense of belonging and emotional attitudinal components) are essential for customers when they deal with their bank. On the other hand, the cost associated with leaving has shown to have the weakest impact on customer loyalty. Companies must know that customers may switch even though the cost associated with leaving is high.