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This study aims to promote strategic maritime management as a new emerging discipline to foster research in strategic maritime issues.

An existing academic discipline maturity model is adapted by including four phases of dynamic evolutionary paths to evaluate the phase of maturity of a research discipline. The model is validated by means of two matured disciplines: strategic management and maritime economics.

It is found that the current research of strategic maritime management is at a phase of emergence of discipline and ready to move to the maturity phase. It is also found that the evolution of the path of strategic maritime management resembles the early evolution path of strategic management but lags 30 years behind. Future research directions of strategic maritime management can be referred to the research streams in the maturity phase of strategic management.

The adapted academic discipline maturity model brings in the longitudinal and dynamic perspectives of the evolution of an academic discipline, which helps maritime strategists identify gaps and opportunities and evaluate the appropriateness of applying a strategic management paradigm to a specific research topic.

The adapted academic discipline maturity model brings in the longitudinal and dynamic perspective of the evolution of an academic discipline, which helps maritime strategists define the gaps and opportunities in strategic maritime management research.

Disruptive technologies in the global logistics industry are often regarded as a threat to the existing business models of incumbents’ companies. Existing research, however, focuses mainly on whether technologies have disruptive potential, thereby neglecting when such disruptive transitions occur. To understand the timing of potential disruptive technological change, this paper aims to investigate the elements of the underlying ecosystem shaping these transitions.

Building on the established ecosystem framework from Adner and Kapoor (2016a), this paper constructs four categories of technology substitution to assess how quickly disruptive change may occur in the global logistics industry and defines key technology substitution determinants in logistics to emphasize the role of ecosystems for further consideration into disruptive innovation theory.

Based on the key determinants, this paper proposes first definitions of distinctive ecosystems elements linked to the three types of innovations, namely, sustaining innovations, low-end disruptions and new-market disruptions, thereby integrating ecosystems into Christensen’s (1997) disruptive innovation theory.

By developing a framework that conceptualizes the pace of technology substitution, this paper contributes to a more nuanced understanding of how logistics managers and academics can better predict disruptive transitions and develop strategies to allocate resources.

This study advocates the importance of taking an evolutionary perspective in the strategic configuration of closed-loop supply chains (CLSC) in the transition to a circular economy. Building on the supply chain management and industrial dynamics research domains, an evolutionary analytical framework was developed and applied in the empirical context of the ongoing industrial transition to e-mobility.

This study is designed as an in-depth exploratory case study to capture the multi-layer dynamic complexities and their interplay in CSLC development. The empirical investigation was based on two-year interactions between the authors and various departments in a leading European heavy vehicle manufacturer. The proposed evolutionary analytical framework was used for investigating the dynamics of four CLSC configurations through ten possible trajectories.

The findings demonstrate that the evolution of each CLSC configuration comes with multiple challenges and requirements and point out the necessity for the co-development of technologies, product design and production, and infrastructure through long-term relationships among key supply chain actors. However, this evolutionary journey is associated with multiple dilemmas caused by uncertainties in the market and technology developments. All these factors were properly captured and critically analyzed, along with their interactions, thanks to the constructs included in the proposed evolutionary analytical framework.

The proposed evolutionary framework is applicable for examination of SC transformation in the context of market and technology development, and is particularly relevant for transitioning from linear SC to CLSC. The framework offers a single actor perspective, as it does not directly tackle dynamics and effects of actions taken by SC actors.

The developed framework can support SC managers in identifying, framing, and comparing alternative strategies for CLSC configuration in the transition process.

This study proposes the framework for understanding and guiding the evolutionary process of CLSC development. Its uniqueness lies in the integration of concepts from innovation and evolutionary theories coming from industrial dynamics and SCM literature streams.

We consider a domestic monopolist who is protected by an import quota on the product he produces. He faces a domestic demand curve which is characterized by a constant price elasticity. He is unable to export and has an upward sloping marginal cost curve. We demonstrate that in this case his employment of labor rises with the import quota until imports rise to a fraction lie of domestic output where e is the elasticity of domestic demand. Thus, the employment maximizing quota sets permissible imports at a fraction of domestic output which is at least as high as the reciprocal of the elasticity of demand. We also make a case for liberalizing all the way right away, "cold turkey liberalization. "

This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.