Purpose: This paper seeks to document an approach to reduce scrap losses using the root cause analysis technique in a lean manufacturing environment.Design/methodology/approach: The study uses lean manufacturing root cause problem solving (RCPS) technique. The study starts with the collection phase, followed by the analysis phase and ends with the solution phase. Supporting data are presented using a Pareto chart to prioritise wastage in order to be more focused for improvement. The Toyota Production System's 5‐whys analysis is performed to analyse the cause of wastages, to formulate and implement corrective actions.Findings: The application of the 5‐whys analysis in a manufacturing industry (XYZ Corporation) provides a fact‐based and structured approach to problem identification and correction that not only reduces, but also totally eliminates defects. Corrective action has permanently eliminated the top defect, which is the “last piece material scratch” and this results in zero scrap thereafter. In this study it was also proven that with sound understanding of manufacturing coupled with possible solutions using the 5‐whys analysis the authors were not only able to eliminate waste, but also to do it with zero‐cost.Originality/value: The approach documented in the paper can be extended to other areas in the manufacturing industry to help improve overall equipment efficiency, breakdown, time loss, customer complaints, etc.

Stock market prediction is an attractive research problem to be investigated. News contents are one of the most important factors that have influence on market. Considering the news impact in analyzing the stock market behavior, leads to more precise predictions and as a result more profitable trades. So far various prototypes have been developed which consider the impact of news in stock market prediction. In this paper, the main components of such forecasting systems have been introduced. In addition, different developed prototypes have been introduced and the way whereby the main components are implemented compared. Based on studied attempts, the potential future research activities have been suggested.

Since the 2008 global financial crisis, many innovations have emerged in the financial sector as investors started to look for alternative methods to eliminate irrational decision-making in wealth management, and Robo-advisors is among those. Nine years after the first launching of Robo-advisors in the U.S. in 2008, the Securities Commission Malaysia has been issuing licenses to seven Robo-advisor platforms. The current COVID-19 outbreak has made this industry more in demand, increasing 763% in registration in 2020. However, much skepticism about Robo advisors’ ability and reliability in providing a similar quality or better advisory service compared to human-financial advisors. Therefore, this study examines the factors influencing the acceptance of Robo-advisors in wealth management in Malaysia. Adopting some factors from various established technology acceptance models, an online survey with 122 respondents was conducted using convenience sampling. Findings show that Relative Advantage, Effort Expectancy, and Social Influence significantly positive influence the Malaysian Behavioral Intention to Accept Robo-Advisors. On the contrary, there is no significant relationship between Perceived Risk and Malaysian Behavioral Intention to Accept Robo-Advisors. The study provides a positive insight into factors influencing the acceptance of Robo-Advisors in Malaysia.

The stress of environmental regulations, sustainable development objectives, and global warming is becoming more prominent now. Most studies conclude that the industrial sector is largely at fault and under tremendous pressure to address these climate change issues. This study highlights the significance of green innovation to Chinese firms in mitigating these conservational challenges, and the study probes the association between green innovation and absorptive capacity. Additionally, board capital (the social and human capital of directors) and environmental regulation—both drivers of green innovation—are explored as moderators between green innovation and absorptive capacity. With appropriate econometric methods and theoretical support from the natural resource-based review, the resource dependency theory, and the Porter hypothesis, the results indicate the positive relationship between green innovation and absorptive capacity. They also reveal board capital and environmental regulation as positive moderators, emphasizing their significance to green innovation. This study offers several suggestions and directives for stakeholders, such as businesses, policymakers, and governments, to foster green innovation for greater profitability, minimizing negative industrial consequences.

The Delphi method has been used as a way to reach consensus among experts established in the 1950s. The method was originally conceived to be used as a forecasting instrument for business in a mixed-method study. The Delphi method is usually conducted with elements of anonymity, iteration, controlled feedback and statistical group response. Delphi method had traditionally used expert opinions through methods such as interviews and brainstorming for the initial point of studies conducted. However, with the expanding volumes of research papers and articles in this age, can techniques such as Traditional Literature Review (TLR) and Systematic Literature Review (SLR) also be viable technique to be an initial point of studies for a Delphi study. As such this study aims 1) to examine and discover whether TLR and SLR are appropriate techniques to be used in a Delphi study and 2) construct a Delphi study process overview. This study adopts a methodology in which seven articles that have used TLR and SLR in their Delphi study will be analyzed. The results shows that TLR and SLR are appropriate techniques to be used in a Delphi study and that there are two types of processes to incorporate them into the study. We have also constructed a Delphi process overview through our analysis. We hope that the results of this study will be able help researchers who are interested in doing a Delphi study to know on what are the benefits of TLR and SLR in these studies along with how to incorporate them.