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This study aims to further the understanding of multi-level analysis in inter-organisational relationships by investigating the interplay of governance, cooperation and coordination in inter-organisational projects (IOPs) on sub-system and project levels.

The authors use the Viable Systems Model as a framework to analyse inter-organisational project governance, cooperation and coordination by adopting a multiple-case study.

The findings illustrate how governance and coordination mechanisms exhibit a filter-down effect on lower sub-systems while cooperation influence is confined within each sub-system. While remarking the importance of specific sub-systems on the overall project performance, the interplay of governance, cooperation and coordination across sub-systems appears to be complex, with governance influencing cooperation and coordination, whereas cooperation and coordination influence each other with an incremental effect.

This study defines two propositions that explain how multiple levels of analysis (project and sub-systems) can support the governance of large inter-organisational projects. The authors elaborate theory on the interplay of inter-organisational project governance, cooperation and coordination.

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。

This study aims to promote sustainability-based education in fashion design and merchandising program to enhance students’ knowledge, skills and attitude about sustainability development, organizational responsibility and personal responsibility from the cotton industry perspective.

To conduct this study, three learning components were considered: learning from experts, learning by doing and outreach activity. Sustainability-related topics were strategically incorporated in different courses for one year; project-based learning approach was adopted; and pre–posttest survey was conducted to study the impact of sustainability-based education on student learning outcome. Rand’s principles-attributes matrix was applied to analyze the impact of sustainable education on student learning outcomes.

The results of course projects indicated enhanced student’s abilities on using use different types of cotton materials in product development, creative use of cotton in visual merchandising and development of business plans focused on sustainability. The two-group mean comparisons showed a significant positive impact on students’ knowledge in cotton and sustainability, followed by students’ skills and attitudes.

In response to the lack of systematic approach to incorporate sustainability-related topics in textile and apparel design discipline, this study offered an opportunity to involve approximately 110 students in various sustainability-based teaching and learning projects.