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This paper investigates the performance assessment of liner shipping industry and presents a multi-dimensional evaluation framework to ensure both financial and non-financial monitoring. The traditional performance assessment approach is based on the financial indicators such as ratio analysis, but it is limited to the fiscal perspective. The meaning of performance has dramatically changed and non-financial (and intangible) assets increased their importance in recent years. Under these circumstances, the multi-attribute performance assessment methods play a critical role to combine many aspects of the business. Balanced Scorecard (BSC) is originally developed for the multi-attribute performance assessment and its philosophy on business process evaluation pioneered the importance of key performance indicators and the quality management issues including the internal customer. Service quality and the business performance assessment are some of the hot issues in the liner shipping industry and the long term competitiveness is a critical concern in the recent liner shipping business. The BSC method is utilized to ensure a cumulative analysis of the short/long and tangible/intangible indicators of performance and computes the weight of each criterion by using Fuzzy-AHP method in the liner shipping industry.

The national logistics policy report published by Ministry of Land, Infrastructure and Tourism in 2011 proposes to establish international logistics strategy teams in 10 different regions around Japan to satisfy the increasing demand for advance transportation infrastructure and stay competitive in Asia Pacific. The globalization of world economies creates many opportunities as well as challenges for international logistics companies to gain more business chances in this changing environment. The purpose of this paper is to improve service quality of international logistics companies and explores the quality function deployment in terms of quality evaluation method. The logistics service is particularly characterized by offering a series of transport solution and including other logistics activities. The major customers of the logistics services are the industrial clients. The customer satisfaction is key managerial mission since the competitiveness is a growing issue in this industry. The quality function deployment is one of the unique procedures to expose the requirements of customer and transform them into managerial tasks by cross correlation analysis between requirements and technical measures. The empirical study is performed to investigate service quality of the logistics industry by focusing on a group of leading logistics companies.

The purpose of this paper is to investigate and clarify “irrationality” problem through the maritime industry practices and leading incentives behind common investors.

This paper includes a review of broader business and economics literature; review of shipping business practices and detection of institutional pathways and misleading mechanisms behind the irrational preferences; investigation of data (for some arguments); and introduction of a theoretical approach.

There are several industry practices and norms well established and followed by decision makers, which may cause and initiate illogical and irrational (long-run) preferences. Short-termism is an erroneous habit of common shipping investors, which is embedded and forced through traditional financial math (i.e. discounted cash flow), financial system (e.g. initial public offerings with high-frequency transactions, interest rate governance and asset valuation mechanism) or flawed contracting tradition (i.e. commission bias).

Both shipping business and financial institutions need to redesign their working mechanisms, evaluation systems, risk detection and assessment procedures. As discussed in Section 4.7, commission-based (float) services must be converted to regular flat rate payments with long-term contracts to protect investors from rational choices of intermediaries in the short-run which encourages investor’s irrationality. Having a long-term service contract will also improve sustainability of intermediaries and lower their business risk (win-win).

The impact of this paper is two-fold. First, it raises critical questions about professional decay and drawbacks of some traditional instruments in the shipping business. For the first time, this paper emphasises on various challenges which deteriorate credibility of the industry and causes ill-defined investments. Some arguments have extreme priority for strengthening the foundations of the industry. Second, this paper establishes a new stream of scholarly research highlighting weaknesses of conventional economic approach and demand for outsourcing other schools of economics (e.g. institutional and behavioural) into the shipping business.

This research analyzes the cycle of the dry bulk shipping market (DBSM) as a representative of spot and period charter rates in dry bulk shipping to develop strategies for investment timing (i.e. asset play) and fleet trading (chartering strategy).

Spectral analysis is a numerical approach to extract significant cyclicality, which may be utilized to develop trading strategies. Instead of working with a single dataset (univariate), a system approach can be utilized to observe a significant shipping market cycle in its multi-variate circumstance. In this paper, a system dynamics design is employed to extract cyclicality in the DBSM in its particular industrial environment. The system dynamic design has competitive forecasting accuracy relative to univariate time series models and artificial neural networks (ANNs) in terms of forecasting outcomes.

The results show that the system dynamic design has a better forecasting performance according to three evaluation metrics, mean absolute scale error (MASE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

Cyclical analysis is a significantly useful instrument for shipping asset management, particularly in market entry–exit operations. This paper investigated the cyclical nature of the dry bulk shipping business and estimated significant business cycle periodicity at around 4.5-year frequency (i.e. the Kitchin cycle).

Bulk shipping mostly facilitates the smooth flow of raw materials around the globe. Regardless, forecasting a bulk shipbuilding orderbook is a seldom researched domain in the academic arena. This study aims to pioneer an econophysics approach coupled with an autoregressive data analysis technique for bulk shipbuilding order forecasting.

By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The model has been evaluated through autoregressive integrated moving average analysis, and the outcome indicates a relatively stable good fit.

The outcomes of the econophysics model indicate a relatively stable good fit. Although relevant maritime data and its quality need to be improved, the flexibility in refining the predictive variables ensure the robustness of this econophysics-based forecasting model.

By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The research result helps shipping investors make decision in a capital-intensive and uncertainty-prone environment.

The aim of this paper is to investigate the gap between cost-based and time-based revenue recognition schemes in the accounting of ship-owning corporations, and to propose cost-based revenue recognition (as in general accounting practice) in connection with the performance obligations.

For a comparative analysis of time-based (traditional approach) and cost-based schemes, a sample of dry bulk ships is selected and voyage estimations are performed by certified professional shipbrokers (Fellow of the Institute of Chartered Shipbrokers) (data collection and voyage estimation by practitioner). Performance obligations are also defined by certified shipbrokers (i.e. survey and expert opinion) and certified public accountant based on common shipping business practice and accounting practice in general.

Empirical results indicate the significant gap between two alternative schemes. Cost-based revenue recognition accelerates the revenue recognition (benefit of shipowner), and it enables comparability among other industries since cost-based allocation is the common practice in accounting (matching principle, Generally Accepted Accounting Principles).

It is obviously impossible to observe all kinds of freight market transactions for all different kinds of vessel particulars. The sample size does not undervalue the current study since the central idea of this paper is not the verification of the cost-based recognition in all possible transactions.

The proposed approach debiases the existing recognition practice as well as improving the speed of revenue recognition. In the existing practice, time-based recognition is still based on voyage estimations (time estimation). Voyage estimations conventionally answer two questions: “What is the cost of the voyage?” and “What is the duration of the voyage?” Therefore, the proposed approach does not require any additional work done. Common practice also clarifies the cost-based schedule for revenue recognition.

This paper addresses the unconventional accounting practice and its incomparability problem for the first time. To the best of the authors’ knowledge, this paper is also the first study on accounting economics of the shipping business. This paper proposes a practical solution to the debate raised by Financial Accounting Standards Board 2014-09 regulation on accounting standards by utilizing a staging approach and cost-based revenue allocation.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

There is a growing interest in fuzzy time series (FTS) forecasting, and several improvements are presented in the last few decades. Among these improvements, the development of causal models (i.e., multiple factor FTS) has sparked a particular literature dealing with the causal inference and its integration in the FTS framework. However, causality among variables is usually introduced as a subjective assumption rather than empirical evidence. As a result of arbitrary causal modeling, the existing multiple factor FTS models are developed with implicit forecasting failure. Since post-sample control (unknown future, as in the business practice) is usually ignored, the spurious accuracy gain through increasing factors is not identified by scholars. This paper discloses the use of causality in the FTS method, and investigates the spurious causal inference problem in the literature with a justification approach. It invalidates the contribution of dozens of previously published papers while justifying its claim with illustrative examples and a comprehensive set of experiments with random data, as well as real business data from maritime transportation (Baltic Dry Index).