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This article presents fieldwork perspectives and research reflexivity gained from the cross-national research team, with the aim of promoting better qualitative research practices in transnational research. It focuses on how the team incorporates diverse cultural perspectives and insider and outsider roles to enhance the research in the data collection process.

This article is drawn from the authors' qualitative research with 25 Japanese retirees in Thailand, addressing cultural challenges encountered by researchers in the cross-national team when conducting field research.

Our findings indicate that researchers with an emic view in the cross-national team who shared nationality and cultural background as the participants facilitated an effective recruitment process and productive collaboration in data gathering. They also served as cultural brokers, tailoring smooth communication during interviews on certain cultures, participant traits and sensitive issues. On the other hand, the outsiders helped the team uncover more transnational issues that the insiders had overlooked. Additionally, combining emic and etic perspectives helps to avoid ethnocentric narratives or purely etic and emic conclusions.

This article addresses a gap in the methodological reflections in transnational research that remains largely overlooked. Our reflection highlights the advantages of cross-national teams, which include researchers from emigration and immigration countries. Their status and roles as insiders and outsiders significantly facilitate a positive impact on the research process and increase the extent of investigating the complex cultural dynamics of transnational practices. The incorporation of emic and etic perspectives is suggested in the methodological approach for transnational migration research.

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 research aims to contribute to the literature on sustainable hospitality and tourism by applying social network analysis to identify sustainable tourism business networks and untangle the role of cognitive and geographical proximity in their formation.

Data mining and machine learning techniques were applied to data collected from the websites of tourism companies located in northeastern Italy, namely, the Veneto region. Specifically, the authors used Web scraping to extract relevant information from the internet.

The results support the existence of geographical clusters of tourist accommodation providers that are linked by strong cognitive proximity based on sustainability principles that are well communicated via their websites. This does not appear to be greenwashing because companies that have agreed on sustainability principles have also implemented concrete actions and tend to signal these actions through a variety of sustainability certifications.

The results may guide tourism managers and policymakers in developing tourism initiatives directed at the creation of fruitful collaborations between similarly oriented organizations and methods to support clusters of sustainable tourism accommodation. Identifying sustainable tourism networks may assist in the identification of potential actors of change, fueling a widespread transition toward sustainability.

In this study, the authors adopted an innovative methodology to detect sustainability-oriented tourism business networks. Additionally, to the best of the authors’ knowledge, this study is one of the first to simultaneously explore the cognitive and geographical connections between tourism businesses.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

Employees facing challenges in their careers – i.e. female, migrant, elderly and lower-educated employees – might expect job searches to have a low likelihood of success and might therefore more often stay in unsatisfactory positions. The goal of this study is to discover inequalities in job mobility for these employees.

We rely on a large sample of Dutch public sector employees (N = 30,709) and study whether employees with challenges in their careers are hampered in translating job dissatisfaction into job searches. Additionally, we assess whether this is due to their perceptions of labor market alternatives.

Findings show that non-Western migrant, elderly and lower-educated employees are less likely to act on job dissatisfaction than their advantaged counterparts, whereas women are more likely than men to do so. Additionally, we find that although they perceive labor market opportunities as limited, this does not affect their propensity to search for different jobs.

This paper is novel in discovering inequalities in job mobility by analyzing whether employees facing challenges in their careers are less likely to act on job dissatisfaction and therefore more likely to remain in unsatisfactory positions.

To date, research on migration and entrepreneurship has rarely focused expat-preneurs. Based on recent developments in both fields, this paper aims to investigate the under-researched phenomenon of Chinese self-initiated expatriates who choose to temporarily live and do business abroad on their own volition.

To address this research gap, 15 in-depth semi-structured interviews with Chinese expat-preneurs living in Portugal were conducted, exposing their narratives about their multiple challenges.

The findings corroborate the theoretical need to look beyond narrow economic explanations for why individuals engage in transnational entrepreneurship and entrepreneurial itinerancy. A collective identity and ethnic community are important because they influence how opportunities are identified and pursued.

This is a qualitative and interpretative study in a limited geographical area, which does not intent to ensure generalizability. More research is needed to further understand the phenomenon of entrepreneurial itinerancy in Europe and among other ethnic communities.

The findings provide insightful inputs to Portuguese policymakers and locals on how to support ethnic entrepreneurship. Chinese expat-preneurs also benefit from understanding how to strength their ethnic social ties in connection with local communities.

This study contributes to the entrepreneurship and international mobility literatures by providing new insights into the nature and dynamics of Chinese “expat-preneurs”, including their entrepreneurial itinerancy, necessary to properly understand entrepreneurs’ diversity and assist in extending transnational entrepreneurial theory.

This article provides an overview and analysis of 50 years of European policies, actions, and challenges to align its higher education and research, as well as lessons learned from this for similar initiatives elsewhere.

The study builds on a comprehensive overview and study of policy documents and scholarly literature to identify by decade the main policies and actions and the related challenges towards a European Higher Education and Research Area.

The findings make clear the key rationales, challenges, shifts and lessons to be learned from 50-year European policies for the alignment of higher education.

Its value lies in the historical overview and analysis of current initiatives, in particular the European Universities Initiative (EUI), to provide a historical and geographical context, which might give insight for similar initiatives elsewhere.