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Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

The unstable labor productivity and periodic planning method cause barriers to improving construction logistics management. This paper aims to explore a demand-driven mechanism for efficient construction logistics planning to record the material consumption, report the real-time demand and trigger material replenishment from off-site to on-site, which is aided by Building Information Modeling (BIM) and the Kanban technique.

This paper follows the design science research (DSR) principles to propose a system of designing and applying Kanban batch with 4D BIM for construction logistics planning and monitoring. Prototype development with comparative simulation experiments of a river remediation project is conducted to analyze the conventional and Kanban-triggered supply. Two-staged industrial interviews are conducted to guide and evaluate the system design.

The proposed BIM-enabled Kanban system enables construction managers and suppliers to better set integrated on- and off-site targets, report real-time demands and conduct collaborative planning and monitoring. The simulation results present significant site storage and schedule savings applying the BIM-enabled Kanban system. Feedback and constructive suggestions from practitioners are collected via interviews and analyzed for further development.

This paper brings to the limelight the benefits of implementing BIM-enabled demand-driven replenishment to remove waste from the material flow. This paper combines lean production theory with advanced information technology to solve construction logistics management problems.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

The logistics industry has undergone a tremendous transformation. This transformation is necessary to cope with the fundamental changes in customer expectations and the need for digitalization imposed by the pandemic, changes in the socioeconomic world, and innovative technology solutions. This paper aims to present digital transformation as an integrated framework for transforming the operating model and applying advanced solutions to the ecosystem of a quintile logistics (5PL) company. 5PL operators are typically an ecosystem. Loosely coupled or self-organized entities that collaborate in a symbiotic relationship represent this ecosystem. They aim to jointly develop capabilities, create innovative services or solutions, share knowledge, facilitate transactions, and leverage network synergies in a logistics environment to provide optimized or novel customer- or partner-centric solutions (Lamberjohann and Otto, 2020).

Currently, there is no single definition of an integrated logistics operations model in 5PL practice, so the qualitative method used in this paper allows for investigation from an exploratory perspective. The paper follows a qualitative research methodology, collecting and analyzing data/facts through interviews and visits to subject matter experts, industry practitioners, and academic researchers, combined with an extensive review of academic publications, industry reports, and written and media content from established organizations in the marketplace. This paper follows a qualitative research methodology, as it is an inquiry rather than a statistical study. The qualitative method allows the study of the concepts of phenomena and definitions, their characteristics, and the defining features that serve as the basis (Berg, 2007). It emphasizes generalized interpretation and deeper understanding of concepts, which would be more difficult in quantitative, statistically based research. Fact-finding was conducted in two ways: in-depth interviews with experts from academia, information and communication technology organizations, and key players in the logistics industry; and academic publications, industry reports, and written and media content from established national and international organizations in the market.

The operations model introduced considers six aspects: persons, processes, platforms, partners, protection and preservation. A virtual team approach can support the personal side of the 5PL ecosystem’s digital transformation. Managing a 5PL ecosystem should be based on collaborative planning, forecasting, and replenishment methods (Parsa et al., 2020). A digital platform can support trust among the stakeholders in the ecosystem. A blockchain solution can powerfully support the 5PL ecosystem from partner relationships’ points of view. The implementation of a cybersecurity reference model is important for protection (Bandari, 2023). Reverse logistics and an integrated approach support the preservation of the ecosystem.

While the author has experience applying the different components of the operations model presented, it would be interesting to find a 5PL that would use all the components presented in an integrated way. The operations model presented applies to any similar ecosystem with minor adaptations.

This paper addresses operations models and digital transformation challenges for optimizing 5PL operators. It provides several opportunities and considerations for 5PL operators interested in improving their management and operations to cope with the growing challenges of today’s world.

The competitiveness and long-term performance of 5PL operators depend on selecting and carefully implementing their operations models. This paper emphasizes the importance of using advanced operations models.

The operations model derives from the author’s personal experiences in research and the innovative application of these models to logistics operators (DHL, UPS, Poste Italiane and others). This paper brings together academic and industry perspectives and operations models in an integrated business digital transformation. This paper defines an original optimal operations model for a 5PL operator and can add sustainable value to organizations and society. In doing so, it outlines different solution requirements, the critical success factors and the challenges for solutions and brings logistical performance objectives when implementing a digital business transformation.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

Through the “Going Digital Initiative,” the Ghanaian government has introduced policies that aim at improving the information and communication technology (ICT) infrastructure of the country. These ICT policies have benefited numerous sectors of the Ghanaian economy. In logistics management, ICT has impacted drone medical delivery in the healthcare and maritime sectors. However, the importance of ICT is not realized in the motorcycle goods transport (MGT) industry, regardless of its popularity and high economic dependency. Second, all research on motorcycles is focused on diverse social concerns, and no study has attempted to analyze ICT implementation for MGT operations. This is a significant gap in logistics management. Hence, the study aimed to investigate the impact of ICT on Ghana's MGT industry empirically.

The study adopts a two-phase data collection approach to collect the data. The authors use partial least square structural equation modeling to analyze the study's measurement and structural assessment model.

ICT positively impacts MGT and the drivers considered. The drivers positively influence MGT. The study further analyzes novel results on the relationships between the drivers and their mediating roles in enhancing MGT performance.

The study's originality is the extension of ICT adoption and usage in MGT. The lack of literature on the importance of ICT for MGT services makes this study the primary source of literature, and the relationships investigated are unique as the research area is unexplored.

This paper aims to investigate how manufacturing firms behave to mitigate business risk during and post-COVID-19 coronavirus disease (COVID-19) on the global supply chain.

A systematic literature review for data mining was used to address the research objective. Multiple scientometric techniques (e.g. bibliometric, machine learning and social network analysis) were used to analyse the Lens.org, Web of Science and Scopus databases’ global supply chain risk mitigation data.

The findings show that the firms’ manufacturing supply chains used digitalisation technologies such as Blockchain, artificial intelligence (AI), 3D printing and machine learning to mitigate COVID-19. On the other hand, food security, government incentives and policies, health-care systems, energy and the circular economy require more research in the global supply chain.

Global supply chain managers were advised to use digitalisation technology to mitigate current and upcoming disruptions. The manufacturing supply chain has high uncertainty and unpredictable global pandemics. Manufacturing firms should consider adopting Blockchain technology, AI and machine learning to mitigate the epidemic risk and disruption.

This study found the publication trend of how manufacturing firms behave to mitigate the global supply chain disruptions during the global pandemic and business uncertainty. The findings have contributed to the supply chain risk mitigation literature and the solution framework.

This study aims to analyse the resilience strategy utilized by small and medium-sized enterprises (SMEs), enabling these businesses to effectively adapt their operations in response to varying conditions by providing them with essential resources. SMEs operate in marketplaces that are both dynamic and frequently tumultuous. These markets provide SMEs with a variety of obstacles, including economic ups and downs, advances in technology, evolving customer tastes and new regulatory requirements. SMEs need to create a strategic strategy to survive and grow in such situations. This strategy ought to help strengthen their resiliency and make it possible for them to make the most of emerging opportunities while simultaneously lowering the dangers.

The questionnaires adopted and adapted from previous research served as the basis for gathering the data. The manufacturing industry was polled through the use of questionnaires. To test the hypothesis, the data were analysed using Smart PLS. Through the use of closed-ended questions directed to the proprietors, managers or senior executives of SMEs, data were collected from each and every institution in the sample. Following the examination of the data by means of descriptive analysis and the presentation of several scenarios using information relating to SMEs, the findings were presented.

The ambidextrous strategies that are used by SMEs have a propensity to offer a constructive contribution to SMEs. In this study, it was discovered that ambidexterity, which is defined as the capacity to both seek and capitalise on possibilities, has a significant bearing on the organisational effectiveness of SMEs. The results showed that ambidextrous strategies have a propensity to work as mediators in interactions involving proactive resilience tactics and performance.

The research expands our understanding of how SMEs in the manufacturing sector may improve their performance by concentrating on growing their ambidextrous strategies.

This study provides a plausible explanation of two crucial management mechanisms for enhancing the sustainability of organisational effectiveness. The relationships between ambidextrous capabilities and firm effectiveness are malleable, and this study suggests that nurturing formal and informal relationships may be the key to SMEs' long-term sustainable performance. Improving the knowledge and performance of supply chain systems for SMEs in the manufacturing sector and boosting their competitiveness in domestic and international markets are the practical contributions of this study.

Our comprehension of monitoring, cooperation and innovation within social management was deepened as a result of these facts. In addition, the study conducted in the sector uncovered four essential connections that outline how managers should actively work towards lowering social risks, developing new possibilities and increasing business performance. These capacities and links, when taken as a whole, provide the foundation upon which an integrated framework and five research propositions are built.

This research offers a convincing explanation of fundamental management processes for enhancing the sustainability of organisational effectiveness. This research implies that developing formal and informal interactions may be the key to the sustainable performance of SMEs over the long run. The relationships between ambidextrous capabilities, methods and organisational effectiveness are flexible, and this study also suggests that these relationships may be shaped. The practical contributions made by this research include boosting the understanding and performance of supply chain systems for SMEs as well as the competitive power of these businesses in both local and international markets.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

The agricultural supply chain is susceptible to disruptive geopolitical events. Therefore, agri-food firms must devise robust resilience strategies to hasten recovery and mitigate global food security effects. Hence, the central aim of this paper is to investigate how supply chains could leverage digital technologies to design resilience strategies to manage uncertainty stemming from the external environment disrupted by a geopolitical event. The context of the study is the African agri-food supply chain during the Russian invasion of Ukraine.

The authors employ strategic contingency and dynamic capabilities theory arguments to explore the scenario and conditions under which African agri-food firms could leverage digital technologies to formulate contingency strategies and devise mitigation countermeasures. Then, the authors used a multi-case-study analysis of 14 African firms of different sizes and tiers within three main agri-food sectors (i.e. livestock farming, food-crop and fisheries-aquaculture) to explore, interpret and present data and their findings.

Downstream firms (wholesalers and retailers) of the African agri-food supply chain are found to extensively use digital seizing and transforming capabilities to formulate worst-case assumptions amid geopolitical disruption, followed by proactive mitigation actions. These capabilities are mainly supported by advanced technologies such as blockchain and additive manufacturing. On the other hand, smaller upstream partners (SMEs, cooperatives and smallholders) are found to leverage less advanced technologies, such as mobile apps and cloud-based data analytics, to develop sensing capabilities necessary to formulate a “wait-and-see” strategy, allowing them to reduce perceptions of heightened supply chain uncertainty and take mainly reactive mitigation strategies. Finally, the authors integrate their findings into a conceptual framework that advances the research agenda on managing supply chain uncertainty in vulnerable areas.

This study is the first that sought to understand the contextual conditions (supply chain characteristics and firm characteristics) under which companies in the African agri-food supply chain could leverage digital technologies to manage uncertainty. The study advances contingency and dynamic capability theories by providing a new way of interacting in one specific context. In practice, this study assists managers in developing suitable strategies to manage uncertainty during geopolitical disruptions.