Digital Transformation (DT) allows logistics service providers (LSPs) to gain competitive advantages by reducing costs, and creating customer experience, innovation and efficiency. This paper proposes a systematic framework to analyse the obstacle factors hindering DT of Thailand small and medium enterprises freight LSPs. First, thirteen obstacles are identified through the extensive literature and validated by a panel of experts. Second, a nine-level hierarchical structure is determined based on Interpretive Structural Modelling to demonstrate the complex interrelationships among obstacle factors. Finally, thirteen obstacles are categorized regarding the driving and dependence power by employing Matrix Impact of Cross Multiplication Applied to Classification approach. The results indicate a lack of digital culture being the most important obstacle hindering DT, followed by lack of support and commitment from top management and lack of risk taking initiative. This finding could help LSPs who aim for DT to take appropriate steps to alleviate obstacles.

Determining 2n values of fuzzy measure prior to applying Choquet integral normally turns into a complex undertaking, especially when the decision problem entails a large number of evaluation attributes, n. Many patterns of fuzzy measure have thus been suggested to deal with this complexity. λ0-measure is one such pattern. However, the original λ0-measure identification method was found to be unsuccessful in providing clear-cut indications on the relationships held by the attributes. A revised version of the method was then introduced to tackle this issue, but unfortunately it requires a large amount of initial data from the respondents compared to the original method. This paper therefore proposes an alternate version of λ0-measure identification method that synchronously compensates the shortcomings associated with each existing method. The proposed method uses interpretive structural modelling (ISM) to uncover the actual relationships held by the attributes. The outputs of ISM (i.e. digraph, driving power and dependence power) are then utilised to determine the inputs required to identify the complete set of λ0-measure values. A supplier selection problem was used to demonstrate the feasibility of the method. Also, the usability of the method was compared over the existing ones.

The contribution of Indian construction sector to the GDP is approximately 10%. Under new government policy, it is anticipated that $1000 Billion share for exclusively infrastructure segment would be completed within the next few years. Construction sector in developing country like India still depends on labor and the practice of mechanization, adopting to use of versatile construction equipment is not in force. The need for implementing new technologies and automation is essential to improve the quality, safety and efficiency. To meet the challenges ahead the construction, organizations should focus on utilization of machinery/equipment to achieve desirable results. Modern construction is characterized by the increase in utilization of equipment to accomplish numerous construction activities. The selection of construction equipment often affects the required amount of time and effort. It is therefore important for managements of construction organizations and planners to be familiar with the features of various types of equipment commonly used in construction activities. The selection of appropriate equipment is a crucial decision making process as it involves huge capital investment. The purpose of the present study is to develop a model pertaining to the factors influencing the selection of construction equipment by using interpretive structural modelling and the results indicate that productivity and safety are the important factors in selection of equipment in Indian construction organizations.