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Urban digital transformation has become a key strategy in global countries. This study aims to provide a comprehensive and dynamic exploration of the intrinsic traits associated with urban digital transformation, in order to yield detailed insights that can contribute to the formulation of well-informed decisions and strategies in the field of urban development initiatives.

Through analysis of parallels between urban digital transformation and gyroscope motion in physics, the study developed the urban digital transformation gyroscope model (UDTGM), which comprises of seven core elements. With the balanced panel dataset from 268 cities at and above the prefecture level in China, we validate the dynamic mechanism of this model.

The findings of this study underscore that the collaboration among infrastructure development, knowledge-driven forces and economic operations markedly bolsters the urban digital transformation gyroscope’s efficacy.

This research introduces a groundbreaking framework for comprehending urban digital transformation, potentially facilitating its balanced and systemic practical implementation.

This study pioneers the UDTGM theoretically and verifies the dynamic mechanism of this model with real data.

This study aims to identify the main challenges to achieving humanitarian logistics in the context of United Nations peace missions in sub-Saharan Africa and to present suggestions for overcoming the logistical gaps encountered.

The methodological approach of the work focuses on the comparative case study of the United Nations Mission in South Sudan, the United Nations Multidimensional Integrated Stabilisation Mission in the Central African Republic and The United Nations Organisation Stabilisation Mission in the Democratic Republic of Congo from 2014 to 2021. The approach combined a systematic literature review with the authors’ empirical experience as participant observers in each mission, combining theory and practice.

As a result, six common challenges were identified for carrying out humanitarian logistics in the three peace missions. Each challenge revealed a logistical gap for which an appropriate solution was suggested based on the best practices found in the case study of each mission.

This paper presents limitations when addressing the logistical analysis based on only three countries under the UN mission as a case study, as well as conceiving that certain flaws in the system, in the observed period, are already in the process of correction with the adoption of the 2016–2021 strategy by the UN Global Logistic Cluster. The authors suggest that further studies can be carried out by expanding the number of cases or using countries where other bodies (AU, NATO or EU) work.

To the best of the authors’ knowledge, this study is the first comparative case study of humanitarian logistics on the three principal missions of the UN conducted by academics and practitioners.

According to the Project Management Institute, 70% of projects fail globally. The causes of project failure in many instances can be identified as non-technical or behavioral in nature arising from interactions between participants. These intangible risks can emerge in any project setting but especially in project settings having diversity of cultures, customs, beliefs and traditions of various companies or countries. This paper provides an objective framework to address these intangible risks.

This paper presents a structured approach to identify, assess and manage intangible risks to enhance a project team’s ability to meet its objectives. The authors propose a user-friendly framework, Intangible Risk Assessment Methodology for Projects (IRAMP), to address these risks and the factors that cause them. Meta-network (e.g., a network of networks) simulation and established social network analysis (SNA) measures provide a quantitative assessment and ranking of causal events and their influence on the intangible behavior centric risks.

The proposed IRAMP and meta-network approach were utilized to examine the project delivery process of an international energy firm. Data were gathered using structured interviews, surveys and project team workshops. The use of the IRAMP to highlight intangible risk areas underpinned by the SNA measures led to changes in the company’s organizational structure to enhance project delivery effectiveness.

This work extends the existing project risk management literature by providing a novel objective approach to identify and quantify behavior centric intangible risks and the conditions that cause them to emerge.

Drawing on resource and capability theory, this study aimed to analyze the relationship between the dynamic capabilities (DC), the knowledge management (KM) process (KMP) and results in customers and people. More specifically, the study argues that the KM process mediates the relationship between DC and the results outlined above. In addition, a predictive analysis is carried out that demonstrates the relevance of the KM process in the model.

The study sample is made up of 118 Spanish organizations that have some kind of recognition of excellence awarded by the European Foundation for Quality Management (EFQM). Partial least squares methodology is used to validate the research model, the hypothesis testing and the predictive analysis.

The results show that organizations which leverage the DC through the KMP improve customer and people outcomes. Moreover, the predictive power is higher when the KMPmediates the relationship between the DC and the results.

There is no consensus in the literature on the relationship between DC, KM and performance. Moreover, there are also not enough papers that study KM or DC through the dimensions that define these constructs or variables. Given this need, this work considers the KMP according to the stages of knowledge creation, storage, transfer and application. Similarly, DC is dimensioned in sensing, learning, integrating and coordinating capabilities. These, as reconfigurators of knowledge assets, influence the KMP. Accordingly, the empirical model connects these knowledge domains and analyses their link to outcomes.

The purpose of this paper is to explore the emergence of innovation in smart service systems to conceptualize how actor’s relationships through technology-enabled interactions can give birth to novel technologies, processes, strategies and value. The objectives of the study are: to detect the different enablers that activate innovation in smart service systems; and to explore how these can lead dynamically to the emergence of different innovation patterns.

The empirical research adopts an approach based on constructivist grounded theory, performed through observation and semi-structured interviews to investigate the development of innovation in the Italian CTNA (Italian acronym of National Cluster for Aerospace Technology).

The identification and re-elaboration of the novelties that emerged from the analysis of the Cluster allow the elaboration of a diagram that classifies five different shades of innovation, introduced through some related theoretical propositions: technological; process; business model and data-driven; social and eco-sustainable; and practice-based.

The paper embraces a synthesis view that detects the enabling structural and systems dimensions for innovation (the “what”) and the way in which these can be combined to create new technologies, resources, values and social rules (the “how” dimension). The classification of five different kinds of innovation can contribute to enrich extant research on value co-creation and innovation and can shed light on how given technologies and relational strategies can produce varied innovation outcomes according to the diverse stakeholders engaged.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.

This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.

Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.

The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

This paper examines the organizational resilience of audit firms during the early stages of COVID-19. The unexpected restrictions placed on travel and on-site working created unanticipated barriers for auditors in Hong Kong. The authors expect that auditors with greater organizational resilience can respond to unexpected situations and restore expected performance levels relatively quickly.

The authors utilize a sample of 1,008 companies listed on Hong Kong Stock Exchange (HKEX) with a financial year-end of December 31. The authors identify five proxies contributing to organizational resilience: auditor size, industry specialization, diversity, geographic proximity to the client and auditing a new client. The authors use audit report timeliness as this study's main dependent variable.

This study's full-sample results suggest that larger auditors, industry specialists and auditors with closer relationships to clients issued more timely audit reports during the pandemic. The analysis of a subsample of companies that initially published unaudited financial statements reveals that industry expertise and longer auditor-client relationships significantly reduced the need for year-end audit adjustments. Finally, the authors find that larger auditors were more likely to offload clients, whereas industry specialists were more likely to retain clients.

The results of the paper suggests that audit firm characteristics associated cognitive abilities, behavioral characteristics and contextual conditions are associated with audit firm organizational resilience and, consequently, helps auditors respond unexpected changes in the audit environment.

The findings of the paper are informative for those involved in audit firm management or auditor hiring and retention decisions.

This study is the first to link organizational resilience to the performance of audit firms in a time of unexpected events. The authors connect three auditor and two auditor-client dimensions to the organizational resilience of the audit firms.