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There is great uncertainty and volatility in the evaluation and measurement of green supplier satisfaction. The purpose of this paper is to fill this gap based on the information entropy theory (IET) to describe the probability of green supplier satisfaction degree.

The authors introduce a formal model using analytic hierarchy process (AHP), IET and entropy technique for order preference by similarity to an ideal solution (TOPSIS) method to evaluate green supplier satisfaction and promote them for the better implementation of green supply chain management practices.

The first finding is developing an effective framework for green supplier satisfaction, incorporating various measures of environmental dimension. Second, a hybrid uncertainty decision method is introduced, by integrating AHP and IET and entropy-TOPSIS.

One of the main limitations of the research is that the authors introduced a conceptual example. Real-world applications need to investigate the accuracy and effectiveness of these measures, and the operational feasibility of this method.

This is one of the first works to provide a comprehensive appraisal model for evaluation of green supplier satisfaction. This study and research method can form general guidelines, and organizations can increasingly benefit from using green supplier satisfaction evaluation as a management tool. Green supplier satisfaction evaluation is just the beginning.

This paper explores the implementation of yield management using different reservation modes at a global hotel network (referred to as the “Group”).

The Group operates close to half‐a‐million rooms in about 4,000 hotels world‐wide. Following an overview of yield management in hotel industry, the two reservation modes used in the Group are presented. The performance of Group's online and off‐line reservation modes globally over a two‐year period is then discussed in terms of three yield management performance measures, namely: average price (AP), occupancy rate, and average revenue per available room.

The findings indicate that the online mode outperforms the off‐line mode with respect to performance measures of AP and average revenue. Further to a global‐based comparison, a localized evaluation of these two modes is also presented for two sub‐groups of hotels clustered in a given region. Statistical analysis of findings is provided, pointing to a substantial revenue increase for the hotel sub‐group that switched from the off‐line to the online reservation mode, compared with the hotel sub‐group that continued to operate off‐line. The paper concludes with a brief discussion on the strengths, weaknesses, opportunities and threats associated with the online reservation mode.

Future research could look into the impact of specific macro and micro economic conditions on the three yield management performance measures defined.

The research reported is of value to hotel executives who want to pursue online reservations.

The purpose of this study is to reveal the magnitude of empty container movements (ECM) arising from cargo seasonality by means of long-term datasets of Turkish terminals. Trade imbalance is one of the well-known major reasons of ECM. Cargo seasonality apart from some other operational drivers and market effect, i.e. commercial decisions of the ship operators, is the major operational driver in Turkish terminals effecting ECM. Furthermore, this study highlights the significance of market effect, leading to take measures for more effective empty container operations in terms of decision makers leading the ship operators.

Time series analysis of full container datasets was performed through X-13ARIMA-SEATS methodology, implementing seasonal adjustment.

The results indicate that 17 of 112 time series in hand, based on a terminal/hinterland, container type and “in and out” foreign trade, exhibit cargo seasonality. Roughly, the amount of ECM originating from cargo seasonality in Turkish terminals represents 10 per cent of total ECM except trade imbalance in those terminals where seasonality is present. This reveals that ECM arising from market effect should not be underestimated.

Reefer container traffic could not be sorted from the datasets.

This paper focuses on one of the major reasons of ECM, cargo seasonality. It brings a novel point of view and interpretations which were not suggested previously about ECM, motivating to overcome inefficiency in container operations.