For a study, it was determined that identifying trends and emerging and declining themes was an essential source of information for academic and innovative management in research and policies. Even though policy analysis currently primarily employs qualitative research methods, the following article examines and compares various approaches, such as questionnaire-based trend analysis, quantitative bibliometric surveys, computer-linguistic techniques and machine learning, and qualitative research. In light of this, the research investigated digital applications in cultural heritage, mainly built legacy, using a variety of investigative frameworks to uncover relevant topics and trends, primarily for EU-based research and policies. Furthermore, against the backdrop of data-driven vs data-guided analytical frameworks, this paper exemplifies and examines the unique opportunities and limitations of the various systematic approaches. The critical outlines by researchers illustrate that the availability of new technologies drives both research and regulations linked to electronic innovations for cultural heritage. The research descriptions were more granular because policies focus on meta-topics like digitalization, openness, and automation. In general, data-driven methodologies can find topics and trendlines and predict the development of relatively close patterns. In contrast, qualitative methods can answer “why” questions about whether cases arise from disruptive breakthroughs or new terminologies or whether topics are becoming obsolete due to general awareness like the term “internet” has accomplished.
Link:built-heritage.springeropen.com/articles/10.1186/s43238-021-00045-7
