The Impact of Live-Streaming Shopping Characteristics on Behavioral Intention of Shoppers

Currently, live-streaming shopping has become one of the popular approaches to conducting business.  Businesses shift from traditional online shopping to live-streaming shopping approach because of the benefits that live-streaming shopping could offer.  This paper aims to study the impact of live-streaming shopping characteristics on behavioral intention among shoppers.  Researchers used literature analysis and a Stimulus-Organism-Response (S-O-R) framework as the methods of the study.  The findings showed that all characteristics had a positive impact on the behavioral intention of shoppers.  The limitation of the study was the researcher focused only on the Stimulus and Response of the framework; the Organism factor of the framework was omitted

Employees’ Turnover Intention in Malaysian Manufacturing Company

Employee turnover is a significant issue in human resource management, which refers to the employees’ willingness to leave their current organization within a predetermined time frame. Thus, it is essential to pinpoint the factors affecting this decision, which include salary, working environment, employer-employee relationship, and leadership styles.  The primary data was collected from a sample of 136 employees from a manufacturing company in Johor, Malaysia, and all questionnaires were successfully collected from the respondents. The data were analyzed using the Statistical Package for the Social Sciences (SPSS) software version 23, and Pearson Correlation and regression analysis were carried out to investigate the influences between the variables. The results from the correlational study revealed that all independent variables have a negative relationship with the dependent variable. Multiple regression analysis showed that the four independent variables substantially explain 54% of the variance for employee turnover intention. However, when assessing the coefficient for each independent variable, only salary, employer-employee relationships, and leadership styles were significant, and the remaining variable was insignificant. For future research, it is recommended that an explicit sample size should be adopted so that the findings can be generalized to other similar populations

Effect of processing parameters on cellulose content extracted from pineapple leaf

The development of dielectric materials from natural fibers of high cellulosic materials could potentially reduce environmental pollution and human health risk due to their biodegradability and non-carcinogenic properties. Comprehending the important process parameters is always challenging in material development to produce a dielectric material with great performance. Therefore, this research aims to evaluate the effect of processing parameters on cellulose extraction from pineapple leaves and determine the most significant parameters contributing to the extraction process and its corresponding permittivity value. The soda pulping method was used in cellulose extraction, and the content was analyzed by the Kurschner-Hanack method. The one-factor-at-a-time analysis was adopted to study the effect of pulping time on cellulose content while keeping the other parameters constant. The two-level factorial analysis was used to determine the significant parameters and the best conditions for the cellulose extraction with pineapple leaf to soda ratio (1:5 and 1:10), soda concentration (5 and 10 wt%), temperature (60 and 100 °C), and pulping time (46–75 min) as processing parameters. The results showed that the pineapple leaf to soda ratio was the most significant parameter in cellulose extraction. A maximum cellulose value of 40.51% was obtained at 1:5 pineapple leaf to soda ratio, 10 wt% soda concentration, 100 °C temperature, and 75 min of pulping time, contributing to a 1.6626 permittivity value. Therefore, the best extraction conditions and significant process parameters determined in this study can be used to tailor the parameters to the desired conditions for a higher cellulose yield and permittivity value

Evaluation of warehousing productivity performance indicators by the FAHP method

A warehouse is an important component in logistics operation as it is a huge contributor to speed up and cost the supply chain management.To monitor the performance of the warehouse operation, management will analyse the measurement of warehousing productivity. The basis of measuring productivity performance in the warehouse is based on how much it cost to perform an operation by utilising the warehouse resources. The purpose of this paper is to rank the most important warehouse productivity indicator for improving the warehouse operation efficiency. By indicating the main warehouse resources and its sub-criteria, a hierarchy structure of ratio-based warehousing productivity performance indicators is constructed. It presents an empirical methodology of the fuzzy analytical hierarchy process (FAHP) method, an integration between the fuzzy logic method with an analytical hierarchy process (AHP) method. The results indicate that Warehouse Management System scores the highest weight value which followed by Storage Space Utilisation and Throughput accordingly. This contributes to grab more attention on the utilization of technologies into the warehouse operation. This article also identifies several additional research opportunities on warehouse performance evaluation assessment

An Innovative Risk Matrix Model for Warehousing Productivity Performance

In today’s era of industrial economics, warehousing is a complex process with many moving parts and is required to contribute productively to the success of supply chain management. Therefore, risk management in warehouses is a crucial point of contention to ensure sustainability with global supply chain processes to accommodate good productivity performance. Therefore, this study aims to analyse risks factors that affect warehouse productivity performance towards a systematic identification of critical factors that managers should target to sustain and grow warehouse productivity. This study utilised a traditional risk matrix framework, integrating it with the Borda method and Analytical Hierarchy Process (AHP) technique to produce an innovative risk matrix model. The results indicate that from the constructed ten warehouse operation risk categories and 32 risk factors, seven risk categories, namely operational, human, market, resource, financial, security and regulatory, including 13 risk factors were prioritised as the most critical risks impacting warehouse productivity performance. The developed risks analysis model guides warehouse managers in targeting critical risks factors that have a higher influence on warehouse productivity performance. This would be extremely helpful for companies with limited resources but seek productivity improvement and risks mitigation. Considering the increasing interest in sustainable development goals (economic, environmental, and social), arguably, this work support managers in boosting these goals within their organisation. This study is expected to benefit warehouse managers in understanding how to manage risk, handle unexpected disruptions, and improve performance in ever-changing uncertain business environments. It often has a profound effect on the productivity level of an organisation. This study proposes an innovative risks analysis model that aims to analyse risks, frame them, and rate them according to their importance, particularly for warehousing productivity performance