This study investigates the effect of perceived risks, i.e., financial risk, product risk, and convenience risk, as well as the COVID-19 pandemic situation on online shopping behavior among Malaysians. This study uses convenience sampling techniques and comprises 185 respondents who have experience buying online. In addition, the study setting was non-contrived, and data was gathered using a closed-ended questionnaire through an online survey. Descriptive analysis was conducted using SPSS version 25.0 software and SmartPLS version 3.2.8 to test the proposed hypotheses. This study found that perceived risk factors such as financial, product, and convenience risk did not influence online shopping behavior. In contrast, the COVID-19 pandemic positively influences online shopping behavior among consumers. It showed a new development in the theory of online shopping behavior, where users continue to make purchases despite being aware that there may be various risks due to the spread of COVID-19. The role of the ministry, business owners, and consumer associations needs to be given attention to form a sustainable electronic commerce system and protect the rights of consumers. This research can help consumers understand their rights.

In a confirmatory study, researchers are expected to employ the covariance-based structural equation modeling (CB-SEM). One of the key presumptions when utilizing CB-SEM is that the data is multivariate normal. Nevertheless, a perfect normal distribution is rarely observed in real-life data. To resolve this, the unweighted least square (ULS) is designed to specifically deal with non-normal data in SEM. However, ULS often yields improper solutions like negative, or boundary estimates of unique variances since it considers measurement errors in observed variables. The disturbance in SEM is reflected in unique variance, which is random error due to unreliability or measurement error and reliable variation in the item that indicates unknown latent causes. Consequently, this can generate bias in indicator loadings estimates. As an action to disentangle this issue, the present study proposes the implementation of regularization parameters by adding small positive values to the variance-covariance matrix. The ratio of bias to variance in a model can be improved to obtain the best estimation performance. Pro-Active Monte Carlo simulation was used to produce multivariate non-normal data with designated sample sizes and population characteristics. The data were analyzed using R Programming Environment by employing “psych”, “MASS”, “foreign”, “mvrnonnorm”, “purr”, and “semTools” packages with 1000 replications to produce multivariate non-normal data. Next, the “lavaan” package was used for SEM and regularized SEM analyses. The outcome of this study proves the capability of regularized ULS to improve parameter estimation.

Poverty is one of the significant problems, and almost all countries worldwide face poverty issues that negatively impact society. This study is written to investigate the students' knowledge and awareness of poverty and interest level in conducting e-business and garage sales. Also, examine the relationship of knowledge and awareness on students' intention to conduct e-business and garage sales. There were 152 respondents from the same university who participated in this study. The data analyzed using descriptive and inferential statistics performed by SPSS-23 and SmartPLS 3.3.3. The survey showed that the student's knowledge and awareness of poverty are still lacking and need improvement. Half of the respondents said they are not familiar with SDG1, and they were not aware that their university has done an event related to SDG1 and poverty. There were 25% and 85% of students interested in conducting e-business and garage sales that increased their pocket money and reduced poverty rates among university students. Overall, this study's result shows a positive response from all respondents, and most of them have general ideas of poverty. In addition, this study has identified that the variable knowledge and awareness have a significant positive effect on student's intention to conduct e-business and garage sales.

In this paper, a new hybrid AHP and Dempster-Shafer Theory of Evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, four groups of risk factors, including Properties, Operational and Technological, Financial, Strategic risk factors, are considered. Then using an L24 Taguchi method, several experiments with various dimensions have been designed and solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index (VI), Reduced Risk Indicator (R.R.I%), and Solving time. The findings indicated that, compared to the classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.

This work investigated and predicted the fatigue life durability of Austenitic Stainless Steel 316L due to its importance in plant industries worldwide. Modelling and simulations were performed to clarify the fracture as well as stress distribution using integrated mechanism. Experimental fatigue validations were also carried out to demonstrate the effect of various fatigue life parameters. Various loading conditions with variable load amplitudes were validated utilizing a frequency of 5 Hz and a stress ratio of 0.1. The accuracy of the simulation results were also verified based on the experimental data. High consistencies between the predicted fatigue life and the experimental results were achieved which increases the validity of the built model.

This paper investigates the relationship between supply chain management practices and organizational performance with the mediating role of innovation. Data were collected from 207 small and medium enterprises (SMEs) in Punjab, Pakistan. PLS-SEM was used to analyze the proposed hypotheses. Findings reveal that strategic partnership with supplier and level of information sharing had no influence on organizational performance. In addition, quality of information sharing, internal supply chain process, and lean practices had significant influence on organizational performance. Moreover, all five practices of supply chain management had significant and positive influence on innovation. Meanwhile, innovation significantly and positively mediated the relationship between supply chain management five practices and organizational performance. The findings of this study will help managers of SMEs enhance their performance.

The Textile and Apparel industry of Malaysia used to contribute about 1.2% of gross domestic product (GDP) in the past. However, this position has followed a reverse trend in recent years. The imports were increased as compared to the exports which decreased the overall contribution of Textile and Apparel industry between 2015 and 2016. To address this issue, this study examined the effective supply chain activities through radio frequency identification (RFID) using survey method to collect the necessary data. Questionnaires were distributed among the employees of Textile and Apparel companies. While analyzing the data by using PLS-SEM, it was found that RFID was contributing majorly in supply chain operations and maintained significant and positive effect on the performance of supply chain. Moreover, staff services quality had moderating role between RFID and the operations of Textile and Apparel industry. The study is one of the first attempts to examine the effect of RFID on supply chain operations of Textile and Apparel companies. Thus, the current study is helpful for practitioners to resolve the issues of concerned industry.

This paper investigates the impact of employee commitment as well as the recruitment process on the performance of internal supply chain. Additionally, the current study is intended to investigate the mediating role of the recruitment process on the relationship between employee commitment and firm supply chain performance. The main objective behind any recruitment process is to recruit committed employees to help a firm enhance the performance of the organizations. Therefore, the current study is carried out to fill a gap by exploring the relationship between recruitment processes, employee commitment, and firm supply chain performance. To achieve the research objective, the study has analyzed the data gathered from 284 mangers of manufacturing organizations of United Arab Emirates. 284 questionnaires out of 550 are found useful, so the response rate is 52 percent. The structural equation modelling using AMOS is used to analyze the data. The results of the study show an agreement with the results of the hypotheses. The direct relationship between commitment types and firms’ internal supply chain performance, and between recruitment process and firms’ internal supply chain performance are found positive and significant. The results of the study will be helpful for HR practitioners, operation managers, researchers, and policy makers in understanding the impact of employee commitments and recruitment process on internal supply chains’ performance of manufacturing firms.

Machine failures during production period may impose thousands to millions of dollars to a manufacturing system. In this paper, the impact of machine failures on production lines in a closed-loop supply chain systems is examined. For this purpose, a new method is proposed for scheduling manufacturing workshops in a supply chain systems. The aim is to determine the best production plans in a manufacturing system by considering alternative preventive maintenance programs while machine failures can affect system performance. To solve the model, a hybrid Ant Colony and Simulated Annealing algorithms is developed and the results are compared with branch and bound method. Our findings show that the condition of emerging machine failure affects machines’ capacity which yields to lost sale. The impacts of using appropriate preventive maintenance on reducing lost sale is also examined. The results indicate that the proposed method can significantly reduce the level of sale variation in supply chain systems.

The primary objective of this paper is to investigate the relationship between total quality management practices (TQMP), supply chain management practices (SCMP), information technology capabilities, supply chain technology adoption and firm supply performance. In addition, the study also tries to investigate the mediating role of information technology capabilities and supply chain technology adoption in the relationship between TQMP, SCMP, and firm supply performance. The study is carried out on a sample of textile firms of Pakistan. To achieve the research objective, Smart PLS-3 is used for the analysis of the data gathered from the textile firms of Pakistan. The results of the study show a great deal of agreement with the hypothesized results. The information technology capabilities, supply chain technology adoption both appear to play mediators between TQMP, SCMP, and firm supply performance. The results of the study will be useful for policymakers and researchers to understand the emerging role of technology in strategic management and operational management.