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Considering the human factor, the quality of the personnel is vital to ensure especially the value creation in the ports. Therefore, employee quality stands out for withstanding the pressures that stem from global trade on its operational speed felt by ports in recent years. Accordingly, the selection of the qualified personnel at the ports is very critical and a tool based on dynamic capabilities is needed to manage this process well. The aim of this study is to develop a model based on dynamic capabilities for recruitment process of ports.

Port personnel should have dynamic capabilities detected from the literature. These capabilities were approached as criteria. In this study, Buckley's proposed fuzzy analytical hierarchy process (AHP) method was employed for weighting the whole criteria. After that, weights of the criteria were used to prioritize alternatives with the fuzzy TOPSIS method.

This model reflects port managers' priorities and port customers' evaluations. Thus, the model can also reflect the level of integration of ports' related department managers into the recruitment process. The analyses allow the evaluation of the attitudes of the human resources department in the related port while fulfilling the personnel recruitment function. As a result of analyses, differences between perceptions of port managers and customers served as a feedback to the human resource management department of the ports.

One of the originalities of this study was derived from its customer-oriented perspective. This is a unique study that gathers common personnel capabilities related to the operation, planning and customer relationship departments and evaluates the success of these capabilities from the customer perspective.

This study aims to assess readiness for climate change mitigation in the Kingdom of Bahrain.

Two stages were followed aiming at understanding the situation related to climate change mitigation in Bahrain and assessing the mitigation readiness. Baseline and mitigation scenarios for the period 2019–2040 were developed using the Low Emissions Analysis Platform software based on historical emissions and energy data for the period 1990–2018. Using the analytic hierarchy process, the mitigation readiness was assessed by 13 experts, and priority areas for mitigation action were identified.

CO₂e emissions are projected to grow continuously. However, no explicit climate change strategy is in place yet. Mitigation is tackled implicitly through energy efficiency and renewable energy initiatives. These initiatives can make 23% reduction in CO₂e emissions by 2040. Adopting additional measures is needed to achieve the recently set emission reduction target of 30% by 2035. The findings revealed potential areas for improving mitigation efforts in Bahrain. Priority areas for mitigation actions, as identified by experts, were mainly related to policy and governance. Focus needs to be paid to the social aspect of climate change mitigation.

Literature on mitigation readiness in developing countries is sparse. Knowledge of the requirements for climate change mitigation and assessment of the country’s performance can prioritize areas for improving mitigation action. Several lessons can be learnt from the case of Bahrain. In addition, the adopted methodology can be applied to other developing or Arab countries at local or institutional levels. However, its application to specific sectors may require adjustments.

Unforeseen events can disrupt the operational process and negatively impact emergency resources optimization and its supply chain. A limited number of studies have addressed risk management issues in the context of emergency supply chains, and this existing research lacks inbuilt and practical techniques that can significantly affect the reliability of risk management outcomes. Therefore, this paper aims to identify and practically analyze the specific risk factors that can most likely disrupt the normal functioning of the emergency supply chain in disaster relief operations.

This paper has used a three-step process to investigate and evaluate risk factors associated with the emergency supply chain. First, the study conducts a comprehensive literature review to identify the risk factors. Second, the research develops a questionnaire survey to validate and classify the identified risk factors. At the end of this step, the study develops a hierarchical structure. Finally, the research investigates the weighted priority of the validated risk factors using the fuzzy-analytical hierarchy process (FAHP) methodology. Experts were required to provide subjective judgments.

This paper identified and validated 28 specific risk factors prevalent in emergency supply chains. Based on their contextual meanings, the research classified these risk factors into two main categories: internal and external risk factors; four subcategories: demand, supply, infrastructural and environmental risk factors; and 11 risk types: forecast, inventory, procurement, supplier, quality, transportation, warehousing, systems, disruption, social and political risk factors. The most significant risk factors include war and terrorism, the absence of legislative rules that can influence and support disaster relief operations, the impact of cascading disasters, limited quality of relief supplies and sanctions and constraints that can hinder stakeholder collaboration. Therefore, emergency supply chain managers should adopt appropriate strategies to mitigate these risk factors.

This study will contribute to the general knowledge of risk management in emergency supply chains. The identified risk factors and structural hierarchy taxonomic diagram will provide a comprehensive risk database for emergency supply chains.

The research findings will provide comprehensive and systemic support for respective practitioners and policymakers to obtain a firm understanding of the different risk categories and specific risk factors that can impede the effective functioning of the emergency supply chain during immediate disaster relief operations. Therefore, this will inform the need for the improvement of practices in critical aspects of the emergency supply chain through the selection of logistics and supply chain strategies that can ensure the robustness and resilience of the system.

This research uses empirical data to identify, categorize and validate risk factors in emergency supply chains. This study contributes to the theory of supply chain risk management. The study also adopts the fuzzy-AHP technique to evaluate and prioritize these risk factors to inform practitioners and policymakers of the most significant risk factors. Furthermore, this study serves as the first phase of managing risk in emergency supply chains since it motivates future studies to empirically identify, evaluate and select effective strategies that can eliminate or minimize the effects of these risk factors.

The existing literature expresses a strong need to develop tools that support the manufacturing reshoring decision-making process. This paper aims to examine the suitability of analytical hierarchy process (AHP)-based tools for initial screening of manufacturing reshoring decisions.

Two AHP-based tools for the initial screening of manufacturing reshoring decisions are developed. The first tool is based on traditional AHP, while the second is based on fuzzy-AHP. Six high-level and holistic reshoring criteria based on competitive priorities were identified through a literature review. Next, a panel of experts from a Swedish manufacturing company was involved in the overall comparison of the criteria. Based on this comparison, priority weights of the criteria were obtained through a pairwise analysis. Subsequently, the priority weights were used in a weighted-sum manner to evaluate 20 reshoring scenarios. Afterwards, the outputs from the traditional AHP and fuzzy-AHP tools were compared to the opinions of the experts. Finally, a sensitivity analysis was performed to evaluate the stability of the developed decision support tools.

The research demonstrates that AHP-based support tools are suitable for the initial screening of manufacturing reshoring decisions. With regard to the presented set of criteria and reshoring scenarios, both traditional AHP and fuzzy-AHP are shown to be consistent with the experts' decisions. Moreover, fuzzy-AHP is shown to be marginally more reliable than traditional AHP. According to the sensitivity analysis, the order of importance of the six criteria is stable for high values of weights of cost and quality criteria.

The limitation of the developed AHP-based tools is that they currently only include a limited number of high-level decision criteria. Therefore, future research should focus on adding low-level criteria to the tools using a multi-level architecture. The current research contributes to the body of literature on the manufacturing reshoring decision-making process by addressing decision-making issues in general and by demonstrating the suitability of two decision support tools applied to the manufacturing reshoring field in particular.

This research provides practitioners with two decision support tools for the initial screening of manufacturing reshoring decisions, which will help managers optimize their time and resources on the most promising reshoring alternatives. Given the complex nature of reshoring decisions, the results from the fuzzy-AHP are shown to be slightly closer to those of the experts than traditional AHP for initial screening of manufacturing relocation decisions.

This paper describes two decision support tools that can be applied for the initial screening of manufacturing reshoring decisions while considering six high-level and holistic criteria. Both support tools are applied to evaluate 20 identical manufacturing reshoring scenarios, allowing a comparison of their output. The sensitivity analysis demonstrates the relative importance of the reshoring criteria.

The purpose of this study is to examine the influence of service quality on student’s satisfaction.

Using empirical research, the study identified previously validated scales of service quality and student satisfaction. Using the SERVPERF scale, data were collected from 279 students studying in public and private universities across Saudi Arabia. The model fit of the scale was assessed to ensure that the data produced accurate outcomes. Structural equation modelling was used to test the effects of independent variables on dependent variables.

The results suggest that four of the five dimensions of service quality, namely, tangibility, reliability, responsiveness and assurance had a significant effect on students’ satisfaction. Empathy was not found to contribute to student satisfaction. The findings broaden and deepen our understanding of how the dimensions of service quality reinforce students’ satisfaction.

Future research can also incorporate in the model other variables, academic and non-academic, related to student satisfaction.

The results have useful implications for decision-makers in higher education institutions who strive to enhance students’ satisfaction and increase the quality of higher education programmes, particularly in Saudi Arabia and the Gulf region in general.

This study uses the SERVPERF scale, which is empirically superior to the SERVQUAL scale for measuring student satisfaction.

This study aims to explore the role played by a formal cluster initiative in supporting small firms' internationalization processes. Taking a public–private interaction perspective, this study aims to understand interaction mechanisms within an internationalization project implemented by a formal cluster initiative.

This study uses a qualitative approach based on a case study of a Swedish formal cluster initiative involved in an internationalization project. The case is analyzed through the industrial marketing and purchasing approach, relying on the Actors–Resources–Activities (ARA) framework.

The analysis highlights the role of formal clusters as supporters and “accelerators” of internationalization processes. Based on the ARA framework, the roles of the public and private actors emerge: the cluster plays the role of orchestrator, supporter and financer, while on the businesses' side, participants assumed the role of customers, displaying various degrees of interest and commitment and giving rise to a leader–follower pattern. Activities occurred at multiple levels, interorganizational, intraproject, interprojects, through different timings and typologies. The main resources at stake were the combination of knowledge, complementary capabilities and financial incentives.

This empirical study provides novel empirical evidence and theoretical development over the phenomenon of formal clusters. This study contributes to the current debate on public–private interaction mechanisms and to the upgrading and circulation of international business knowledge.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

The purpose of this paper is to see whether attitudes toward older workers by managers change over time and what might explain development over time.

A unique panel study of Dutch managers is used to track the development of their attitudes toward older workers over time (2010–2013) by focusing on a set of qualities of older workers aged 50 and older. A conditional change model is used to explain the variation in changes by focusing on characteristics of the manager (age, education, gender, tenure and contact with older workers) and of the firm (composition staff, type of work and sector, size).

Managers have significantly adjusted their views on the so-called “soft skills” of older workers, like reliability and loyalty. Attitudes toward “hard skills” – like physical stamina, new tech skills and willingness to train – have not changed. Important drivers behind these changes are the age of the manager – the older the manager, the more likely a positive change in attitude toward older workers can be observed – and the change in the quality of contact with older workers. A deterioration of the managers’ relationship with older workers tends to correspond with a decline in their assessment of soft and hard skills.

Attitudes are not very susceptible to change but this study shows that a significant change can be expected simply from the fact that managers age: older managers tend to have a more positive assessment of the hard and soft skills of older workers than young managers.

This paper offers novel insights into the question whether stereotypes of managers change over time.

This study aims to examine the degree to which a selection of home country factors affects the proclivity of firms to internationalise. The study also proposes and tests a conceptual model that fuses institutional and resource-based theories to improve our understanding of firm internationalisation.

The study uses cross-sectional, national-level secondary data from the 2018 Global Entrepreneurship Development Institute and World Economic Forum data sets on global entrepreneurship and competitiveness indices for 137 countries. The data is analysed using correlation and hierarchical regression analysis to test the hypotheses.

The results indicate that national income, institutions, trade openness and availability of risk capital positively influenced firm internationalisation, while home-country networking had an inverse effect. However, home country infrastructure had no statistically significant effect on firm internationalisation.

The findings highlight the importance of considering home country attributes in understanding the internationalisation of firms.

This study contributes to the body of knowledge by providing empirical evidence of the role of local factors on the internationalisation of entrepreneurial ventures. It also tests a novel conceptual model that integrates institutional and resource-based theories to explain the nuances of the internationalisation of business ventures globally.