Search results

As university–industry collaboration (UIC) is associated to transfer of knowledge and technology, this collaboration is an extremely important field of study for the world's economies that helps industries become more competitive. UIC will assist universities in fine-tuning universities' educational programs to match with the industrial demand. This study, thus, presents a systematic literature review related to UIC in technology development process and technology commercialization.

The Scopus database is used to extract the relevant articles. First, in presenting the articles, four scientometric analyses are used to visualize the bibliometric clusters, namely articles and journals co-citation analysis, countries collaboration analysis and keywords co-occurrence analysis. Next, a qualitative approach is used to classify the articles according to the methodology used and type of research. Finally, a research trend and keywords' evolution based on keywords are also provided.

Results of this study reveal that majority of the articles used qualitative approach and descriptive analysis to explain the knowledge flow between industries and universities. According to the research trend analysis, researchers in this field were moving from the knowledge-based economy topic (from 2010–2013) to product development (2014–2015), technology commercialization (2016–2017), open innovation (2018–2019) and then currently are focusing on the green entrepreneurship topic.

This study is expected to facilitate scholars to uncover gaps in the literature of UIC.

This study extends the use of scientometric analysis. The combination of “bibliometrix” R-package tool and VOSViewer software to perform the analysis is expected to give a new insight of doing the systematic literature review.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.