Human resource in today’s world is recognized as a strategic asset which drives organizations to superior in the industrial competition. With the effective identification of employee attributes, all the determinants of productivity can be obtained. This research assesses engineers’ motivation factors in the construction industry’s private sector and further examined their response to various attributes, including age and gender. A structured and self-administrated systematic qualitative approach was utilized to collect data from 120 engineers. Twenty-six motivational factors were grouped according to Maslow’s need theory, and the paper presents a comprehensive analysis based on the relative importance and response percentages of the identified factors. The most influential need level among them was statically determined as the esteem needs to be followed by basic needs, belonging needs, safety needs, and self-actualization needs. Out of the twenty-six factors, good work discipline is the most influential motivational factor for female engineers, whereas company name and stability have become the most important factor for male engineers. A variation of those factors' relative importance is also determined for various age groups. These results would help senior management to formulate effective policies to improve employee retention in the construction industry.
Due to the increasing vulnerability of supply chains, the concepts of supply chain risk management and supply chain resilience have gained attention both in the industry and in academia, globally. This research focused on the supply chain risk management landscape in Sri Lanka's manufacturing industries as prevalent in mid-2019. To understand this, an investigation on the maturity of supply chain risk management processes in Sri Lankan organisations was carried out during the month of June in 2019. The research further extended to explore the practitioner perception on supply chain vulnerability and to examine the relationships among supply chain vulnerability, supply chain risk management maturity, and supply chain resilience for the Sri Lankan manufacturing sector. To gather data for the research, a carefully designed online questionnaire was circulated among industry practitioners in Sri Lanka representing the management tier of their employer. Altogether 42 responses were received of which 39 were considered for analysis that comprised a confirmatory factor analysis to validate the adopted theoretical model. The findings suggest that overall supply chain risk management process maturity in Sri Lanka is at a 'proactive' level where individual firms have succeeded in adopting formal standalone processes to counteract supply chain risks despite failing to achieve significant integration across the end-to-end supply chain. It could also be concluded that Sri Lankan supply chain professionals perceive vulnerability of Sri Lankan supply chains as being moderate and that supply chain risk management process maturity positively influences supply chain resilience in the Sri Lankan context.
Purpose The crude oil supply chain (COSC) is one of the most complex and largest supply chains in the world. It is easily vulnerable to extreme events. Recently, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (often known as COVID-19) pandemic created a massive imbalance between supply and demand which caused significant price fluctuations. The purpose of this study is to explore the influential factors affecting the international COSC in terms of consumption, production and price. Furthermore, it develops a model to predict the international crude oil price during disease outbreaks using Random Forest (RF) regression. Design/methodology/approach This study uses both qualitative and quantitative approaches. A qualitative study is conducted using a literature review to explore the influential factors on COSC. All the data are extracted from Web sources. In addition to COVID-19, four other diseases are considered to optimize the accuracy of predictive results. A principal component analysis is deployed to reduce the number of variables. A forecasting model is developed using RF regression. Findings The findings of the qualitative analysis characterize the factors that influence international COSC. The findings of quantitative analysis emphasize that production and consumption have a higher contribution to the variance of the data set. Also, this study found that the impact caused to crude oil price varies with the region. Most importantly, the model introduced using the RF technique provides a high predictive ability in short horizons such as infectious diseases. This study delivers future directions and insights to researchers and practitioners to expand the study further. Originality/value This is one of the few available pieces of research which uses the RF method in the context of crude oil price forecasting. Additionally, this study examines international COSC in the events of emergencies, specifically disease outbreaks using machine learning techniques.
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