The objectives of this research are to study the direct influence on the competitive advantage

and pattern development of variables affecting the competitive advantage of the Thai oil

palm industry. This research employs a quantitative research method. The population for

the study consists of 150 oil palm industrial operators in Thailand. Questionnaires are used

in the data collection and the data are analyzed by using SEM. The research results reveal

that the Knowledge Management Process and Supply Chain Integration positively influence

the competitive advantage in the quality, delivery, and cost. The competitive advantage

receives a positive direct impact from the Knowledge Management Process and Supply

Chain Integration. The variation of competitive advantage can be explained as 84%. The

obtained results can be used for developing the industry to create economic growth and

sustainable competitive advantage.

The focus of this paper is to propose a method for prioritizing knowledge and technology

factor in companies’ business strategy. The data has been gathered and analyzed from

Malaysian-owned company of medium size type industry, employing around 250 employees

and listed in the Malaysian Bourse Stock of Exchange, since 2000. Sense and respond model

is used to determine competitive priorities of the firms. Then knowledge and technology

part of sense and respond questionnaire is used to calculate the variability coefficient i.e. the

uncertainty caused by technology and knowledge factor. The results show that the company

is not leading in term of technology (spear head technology share is around 33%). Therefore,

the enhancement of technology and knowledge to SCA values is not significantly seen in

this study. The usage of the core technologies is around 41% and it might seem relatively

enough. In terms of basic technology, while its share is the lowest (around 25%), it has the

highest source of uncertainties among technology types. In this case, the proposed model

helped to have a clear and precise improvement plan towards prioritizing technology and

knowledge focus.

The sustainable development of human activities is directly related to the protection of the environment by lowering the anthropogenic stress. Pharmaceuticals – due to their growing consumption (use in medicine, veterinary, animal production, cosmetics) and their incomplete removal in wastewater treatment plants – are classified as a group of new and rapidly emerging pollutants which have been proven to have a negative impact onto water organisms. In order to ensure the proper protection of human health and the environment there is an urgent necessity of determining pharmaceuticals in clinical, cosmetic, food and environmental samples. Gas (GC) and high performance liquid chromatography (HPLC) are valuable techniques for such determination, especially when they are coupled with mass spectrometry (GC-MS; LC-MS) or tandem mass spectrometry (GC-MS/MS; LC-MS/MS). The purpose of this paper is to present an analysis of sustainability features of analytical techniques in the light of necessity to determine trace amounts of pharmaceuticals in the aforementioned different matrices. Using the Delphi method we performed an analysis of the key sources of the competitive advantages of the application of GC and GC-MS techniques for determining the pharmaceutical residue in clinical, cosmetic, food and environmental samples – compared to techniques based on HPLC or LC-MS. The analysis covered the following areas: (i) the features of the technique, (ii) the price, and (iii) the applicability in various sectors of economy.

The main focus of this paper is to propose a method for prioritizing knowledge and technology factor of firms towards sustainable competitive advantage. The data has been gathered and analyzed from two high tech start-ups in which technology and knowledge play major role in company’s success. The analytical hierarchy model (AHP) is used to determine competitive priorities of the firms. Then knowledge and technology part of sense and respond questionnaire is used to calculate the variability coefficient i.e. the uncertainty caused by technology and knowledge factor. The proposed model is tested in terms of two start-ups. Based on the initial calculation of uncertainties, some improvement plan is proposed and the method is applied again to see if the uncertainty of knowledge and technology decreases. In both cases, the proposed model helped to have a clear and precise improvement plan and led in reduction of uncertainty.