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Though customer experience (CX) is identified as a key research priority, empirically led insight with tourism consumers' resulting emotional attachment (EA) and customer loyalty (CL) remains scarce, particularly during the COVID-19 crisis. Adopting service-dominant logic, this study develops a model that investigates the impact of customer engagement (CE) and customer co-creation (CC) on CX, which consequently effects EA and CL during the COVID-19 crisis in the tourism industry. First, our results suggest that CE and CC positively affect tourism CX. Second, results revealed the CX's significant positive effect on EA and CL. Third, findings confirmed the CE's and CC's indirect impact on EA and CL, as mediated via CX in pandemic situations. Our study offers key implications for destinations to develop tactics in surviving during a pandemic to rebuild tourism.

This paper presents integrated models of probabilistic vehicle routing and scheduling model with time windows and dynamic traffic simulation. The probabilistic model incorporates the uncertainty of travel times. After applying the models to test road network, incorporating the uncertainty of travel times using the models not only allows freight carriers to reduce their total costs, but also improves the environment in terms of decreasing CO₂ emissions. As well, the paper investigated the effects and profitability of co-operative freight transport systems using a simulation model based on forecast vehicle routing and scheduling model. Results showed cooperative freight transport systems can lead to a substantial reduction in total delivery costs and total travel times within the whole road network.

A trend in modern supply chain management has been to substitute information for inventory. In this chapter, an approach to how information and communication technology can be used to achieve this in a maritime logistics context is outlined and described based upon a bulk shipping case.

The approach used is based on data-driven modeling and analysis, in which current logistics and commodity storage costs are benchmarked against a “best possible solution.”

To make a new solution operative, a change should be made based upon an analytical decision-making approach, ICT infrastructure development, and inter-organizational development. Thus, the proper use of analytical and transactional information and communication technology in maritime logistics would enable logistics chain stakeholders to track stock levels and ultimately allocate vessels to move cargo when that is logistically most cost effective. Further, this could support a development in the contractual relationships between producer and shipping line changing from a Contract of Affreightment to a Service Level Agreement relationship.

There is room for enhanced use of information and communication technology to provide decision and operational support at strategic, tactical, and operational levels within maritime logistics. This chapter explains some of the driving forces for this, together with a tested approach and method for this, given into a specific, practical case.

In the last two decades, different policy initiatives have been set up to increase the share of intermodal freight transport through a modal shift. In the design of these policies, often critical break-even distances are set, showing the cost or price competitiveness of intermodal transport to delineate transport routes that qualify for such a modal shift. In this chapter, we discuss to which extent such break-even distances can be generalized on a larger scale and how they are calculated.

We use two price-based models to calculate break-even distances for an intermodal rail and an intermodal barge transport case. General break-even values do not show the price variation in the transport market and vagueness in the calculation of these values adds to this problem.

We find that for the inland waterway case, intermodal barge transport shows potential on shorter distances as well. In addition, different ways to lower the break-even distance are discussed and a framework for calculating break-even distances is suggested.

The research elaborates on break-even distances in a European context using price data which are fluctuating over time, location specific and often not publicly available.

Policy initiatives promoting intermodal transport should not focus solely on long distance transport. Moreover, evaluating the competitiveness of the intermodal sector solely on a price comparison dishonours its true potential.

This chapter challenges the current European policy on intermodal transport by showing the price competitiveness of intermodal transport in two cases.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.