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Achieving swift and even flow of cargo through the border, the ultimate objective of cross-border logistics (CBL) requires the close coordination and collaboration of a multitude of stakeholders, as well as optimally configured systems. To achieve and sustain competitiveness in a dynamic international trade environment, CBL processes must undergo periodic analysis, improvement and optimization. This study aims to develop a modelling framework to capture CBL processes for analysis and improvement.

Relying on the extant literature, a meta-model is developed incorporating significant perspectives required to model CBL processes. Popular process modelling notations are evaluated against the meta-model and their ease of comprehension is also evaluated. The selected notation through evalution is augmented with addendums for a comprehensive depiction of CBL processes.

The capacity of role activity diagrams (RADs) to depict all perspectives, including interactions in a single diagram, makes them particularly suitable for modelling CBL processes. RADs have been complemented with physical flow diagrams and methods to capture temporal dimension, enabling a comprehensive view of CBL processes laying the foundation for insightful analysis.

The meta-model developed in this paper paves the way to develop an analysis framework which requires further research.

The lack of well-accepted modelling notations for studying CBL processes prompts researchers to search and adapt different formalisms. This study has filled this gap by proposing a comprehensive modelling framework able to capture CBL processes at different granularities in rich detail. Not only does the developed meta-model aid in selecting the notation, it is also useful in analysing the constituent elements of CBL processes.

The aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in an integrated way so that these three elements combined result in a methodology called the Agile DMAIC cycle, which brings more agility and reliability in the execution of the Six Sigma process.

The approach taken by the authors in this study was to analyze the studies arising from this union of concepts and to focus on using PM tools where appropriate to accelerate the DMAIC cycle by improving the first two steps, and to test using the AHP as a decision-making process, to bring more excellent reliability in the definition of indicators.

It was indicated that there was a gain with acquiring indicators and process maps generated by PM. And through the AHP, there was a greater accuracy in determining the importance of the indicators.

Through the results and findings of this study, more organizations can understand the potential of integrating Six Sigma and PM. It was just developed for the first two steps of the DMAIC cycle, and it is also a replicable method for any Six Sigma project where data acquisition through mining is possible.

The authors develop a fully applicable and understandable methodology which can be replicated in other settings and expanded in future research.

This study aims to investigate the combined use of System Dynamics (SD) applications in Enterprise Engineering (EE) research and practice. SD application in EE is becoming widely accepted as a tool to support decision-making processes and for capturing relationships within enterprises.

A systematic literature review (SLR) is conducted using a standard SLR method to provide a comprehensive review of existing literature. The search was conducted on ten platforms identifying 30 publications which were analysed through the use and development of a codebook.

The SLR showed that 90% of the result set consisted of peer-reviewed academic conferences and journal papers. The SLR identified a highly dispersed author set of 83 authors. Amongst these authors, Vinay Kulkarni was an active author who has co-authored up to four publications in this research area. The analysis further revealed that the combined use of SD applications and EE is an emerging research area that still needs to develop in maturity. While all phases of EE have received attention, the current research work is more focused on the design phase. The important gap between model development and implementation is identified.

The study elucidates the existing status of interdisciplinary research combining techniques from the SD and EE disciplines, suggesting future research topics that combine the strengths of these existing disciplines.

In order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.

First, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.

The cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.

This study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

Scholars and practitioners acknowledge that digital leadership can help organizations gain a competitive advantage. This article focuses on the characteristics, styles and skills needed for effective digital leadership. It looks at the role of digital leaders in innovating business models and introducing organizational change required for a successful digital transformation.

This paper is based on a comprehensive literature review of digital transformation, digital leadership, business model innovation, and organizational culture. It draws on institutional theory (INT) the neo-institutional theory (NIT). It draws from Science Direct, Web of Science and Google Scholar publications. A proposition and a conceptual framework are developed based on evaluating and synthesizing the literature.

We find that specific leader characteristics (agility, participative, innovativeness and openness), styles (democratic and transformational) and skills (cognitive, social, technological and digital) enable successful business model innovation and organizational change, all of which allow successful digital transformation of firms.

The literature on digital transformation has not been well integrated with the leadership literature. This is particularly true in terms of the role digital leaders play in the successful digital transformation of firms. The conceptual framework and a way forward proposed in this paper introduce future research directions on the topic.

This study, based on signaling theory, examines the pre-recruitment employer branding strategy during the COVID-19 pandemic. It investigates the relationship between spontaneous word-of-mouth (WOM) recommendations for companies and prospective candidates' job application intentions. Specifically, the study explores serial mechanisms mediating the characteristics of company online career pages, including the perceived informativeness of online job advertisements (ads), candidates' preferences for its web approach to them and the company’s reputation.

Reflecting prospective candidates from students and young alumni of universities, partial least squares structural equation modeling (PLS-SEM) was employed on a sample of 737 individuals representing various fields of study from Greek universities.

The findings highlight the effectiveness of positive WOM recommendations during the initial stages of recruitment, particularly amidst COVID-19 challenges in the labor market, notably impacting young candidates. The study suggests that spontaneous WOM, originating from trustful sources, motivates job seekers to actively engage with the company’s web career channels, seeking information and favorable indications of the company’s approach toward its candidates. Positive WOM, combined with informative content and a friendly communication style, plays a critical role in shaping the company’s reputation. Consequently, this encouragement motivates individuals to start their job search efforts and consider applying for positions within the specific organization.

This research provides valuable empirical evidence in the pre-recruitment field, particularly in unforeseen crisis circumstances such as the COVID-19 pandemic. It examines how spontaneous, positive WOM from sources, like peers and alumni, significantly influences young job seekers' perceptions and preferences regarding the company’s career web channels as sources of information and signals about working conditions. The combination of positive WOM with informative content and a friendly communication style in the web approach plays a crucial role in shaping a positive company reputation. Consequently, this encourages candidates to consider applying for positions within the company.

This research contributes to pre-recruitment studies, especially amidst crises like COVID-19. It examines how positive WOM from trusted sources like peers and alma mater alumni influences young job seekers' views on the company’s career web channels. By emphasizing the importance of combining positive WOM with informative web content and a friendly communication style, the study offers insights into effective recruitment strategies. It highlights the significance of positive and spontaneous WOM in attracting young talent and its impact on job seekers' decision-making, even in uncertain conditions. Overall, it advances recruitment practices for attracting candidates.

Tourism destination marketers use videos that incorporate storytelling and visual and audio components to evoke emotional arousal and memorability. This study aims to examine the increase in participants’ level of arousal and the degree of memorability after watching two different videos.

A quasi-experimental study was conducted with 45 participants who watched two destination promotional videos. One video used storytelling whereas the other used scenic images and music. The level of arousal was measured using both tonic and phasic electrodermal activity levels. The memorability of each video was measured after seven days by testing the recall accuracy.

Scenic imagery and music videos were associated with higher-than-average arousal levels, while storytelling videos generated larger-amplitude arousal peaks and a greater number of arousal-evoking events. After a week, the respondents recalled more events from the storytelling video than from the scenery and musical advertisements. This finding reveals that the treatment, storytelling and sensory stimuli in advertising moderate the impact of arousal peaks and memorability.

These results indicate that nonnarrative videos using only sceneries and music evoked a higher average level of arousal. However, memorability was associated with higher peak levels of arousal only in narrative storytelling. This is the first tourism study to report the effects of large arousal peaks on improved memorability in advertising.