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Many of today’s information and technology systems and environments facilitate inquiry, learning, consciousness-raising and knowledge-building. Such platforms include e-learning systems which have learning, education and/or training as explicit goals or objectives. They also include search engines, social media platforms, video-sharing platforms, and knowledge sharing environments deployed for work, leisure, inquiry, and personal and professional productivity. The new journal, Information and Learning Sciences, aims to advance our understanding of human inquiry, learning and knowledge-building across such information, e-learning, and socio-technical system contexts.

This article introduces the journal at its launch under new editorship in January, 2019. The article, authored by the journal co-editors and all associate editors, explores the lineage of scholarly undertakings that have contributed to the journal's new scope and mission, which includes past and ongoing scholarship in the following arenas: Digital Youth, Constructionism, Mutually Constitutive Ties in Information and Learning Sciences, and Searching-as-Learning.

The article offers examples of ways in which the two fields stand to enrich each other towards a greater holistic advancement of scholarship. The article also summarizes the inaugural special issue contents from the following contributors: Caroline Haythornthwaite; Krista Glazewski and Cindy Hmelo-Silver; Stephanie Teasley; Gary Marchionini; Caroline R. Pitt; Adam Bell, Rose Strickman and Katie Davis; Denise Agosto; Nicole Cooke; and Victor Lee.

The article, this special issue, and the journal in full, are among the first formal and ongoing publication outlets to deliberately draw together and facilitate cross-disciplinary scholarship at this integral nexus. We enthusiastically and warmly invite continued engagement along these lines in the journal’s pages, and also welcome related, and wholly contrary points of view, and points of departure that may build upon or debate some of the themes we raise in the introduction and special issue contents.

Working conditions on many digital work platforms often contribute to the grand challenge of establishing decent work. While research has examined the public regulation of platform work and worker resistance, little is known about private regulatory models. In this paper, we document the development of the “Crowdwork Agreement” forged between platforms and a trade union in the relatively young German crowdworking field. We find that existing templates played an important role in the process of negotiating this new institutional infrastructure, despite the radically new work context. While the platforms drew on the corporate social responsibility template of voluntary self-regulation via a code of conduct focusing on procedural aspects of decent platform work (i.e., improving work conditions and processes), the union contributed a traditional social partnership template emphasizing accountability, parity and distributive matters. The trade union’s approach prevailed in terms of accountability and parity mechanisms, while the platforms were able to uphold the mostly procedural character of their template. This compromise is reflected in many formal and informal interactions, themselves characteristic of a social partnership approach. Our study contributes to research on institutional infrastructures in emerging fields and their role in addressing grand challenges.

This study aimed to utilize Epistemic Network Analysis (ENA) for a thorough evaluation of policy documents concerning the digital transformation in Vietnam's higher education sector.

Adopting a quantitative ethnography approach, this research employed ENA to analyse a curated collection of 21 documents that specifically addressed higher education (HE) and digital transformation within Vietnam. The study also incorporated qualitative content analysis, utilizing the constant comparison method as outlined by Onwuegbuzie et al. (2009), for data coding. ENA facilitated the examination of connections among various policy aspects.

The study revealed a consistent overarching theme in Vietnam's digital transformation policies during and post-pandemic, focusing on key areas such as ADMINISTRATION, VISION, QUALITY, and INFRASTRUCTURE. However, a temporal shift in emphasis was observed: during the pandemic, policies were more focused on ADMINISTRATION and INFRASTRUCTURE, while post-pandemic, there was an increased emphasis on COLLAB, VISION, and TEACH_LEARN.

This research represents one of the initial efforts to showcase the utility and significance of ENA in analysing policy documents. It underscores ENA's potential in elucidating the complex interplay of policy elements in the context of digital transformation in higher education, particularly within a developing country setting.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

This paper examines the relationship between transport connectivity and regional economic development in China. It develops measurements appropriate for transport connectivity based on a set of evaluation models. This model is used to analyze the logistic connectivity of China’s 31 provinces by focusing on 11 variables, including some new factors (Density of road network, Density of railway network, Number of Internet Users) not used in previous studies, over the 13-year period from 2002 to 2014. Using panel data regression analysis, the empirical results show a statistically significant and positive impact of transport connectivity (factors like Density of road network, Density of railway network and Number of Internet Users) on economic development in China. In particular, the Number of internet users is a key factor reflecting information connectivity in all the variables. Comparative analysis regarding economic development is conducted to benchmark between coastal provinces and interior provinces. Like most previous research, this study yields the same finding of higher impact of transport connectivity on economic development in eastern provinces than in western provinces. This study suggests that decentralized decision-making will be significantly more efficient for analyzing regional infrastructure development. It also shows that the influence of transport connectivity on economic development is dependent on a certain developmental stage. This suggests that an economic region should adopt different development strategies for transport connectivity during different stages of development.

Drawing on attribution theory, the current paper aims to examine the effects of review content structures on online review helpfulness, focusing on three pertinent variables: review sidedness, information factuality, and emotional intensity at the beginning of a review. Moreover, the moderating roles of reviewer reputation and review sentiment are investigated.

The review sentiment of 144,982 online hotel reviews was computed at the sentence level by considering the presence of adverbs and negative terms. Then, the authors quantified the impact of variables that were pertinent to review content structures on online review helpfulness in terms of review sidedness, information factuality and emotional intensity at the beginning of a review. Zero-inflated negative binomial regression was employed to test the model.

The results reveal that review sidedness negatively affects online review helpfulness, and reviewer reputation moderates this effect. Information factuality positively affects online review helpfulness, and positive sentiment moderates this impact. A review that begins with a highly emotional statement is more likely to be perceived as less helpful.

Using attribution theory as a theoretical lens, this study contributes to the online customer review literature by investigating the impact of review content structures on online review helpfulness and by demonstrating the important moderating effects of reviewer reputation and review sentiment. The findings can help practitioners develop effective review appraisal mechanisms and guide consumers in producing helpful reviews.