Browsing by Author "Arici, N.C."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Web of Science
    Corporate social responsibility in hospitality and tourism: a systematic review
    (2024.01.01) Arici, H.E.; Saydam, M.B.; Sökmen, A.; Arici, N.C.
    Scholars have increasingly focused on the importance of corporate social responsibility (CSR) in the hospitality and tourism (H&T) industry due to its rapid growth and expansion. This study examined 192 CSR-focused empirical studies that were published in H&T journals. The report provides a comprehensive analysis by categorizing the data into several parts, such as publishing trends of CSR, segmentation by journals, theoretical frameworks, techniques, and CSR measurement scales. This study also develops a complex nomological network of CSR, examining its connections with the precursor factors, mediating variables, moderating impacts, and outcome effects. Key themes were identified using advanced thematic analysis techniques, providing crucial insights into the current status and future directions of CSR using Leximancer software. The results provide significant information for sustainable practices and influence prospective studies in the crucial field of corporate responsibility.
  • No Thumbnail Available
    Web of Science
    Does data curation matter in citation and co-citation analysis? Evidence from a top service journal
    (2023.01.01) Koseoglu, M.A.; Arici, H.E.; Arici, N.C.
    Bibliometric scholars have primarily evaluated massive data without refining any potential typing and/or spelling errors, resulting in two constraints: misinterpretation of findings and misleading future research in the knowledge domain. Thus, this study aims to introduce the data curation approach in order to reduce these restrictions. Utilizing a renowned service journal (Journal of Service Research) as the study sample, we first acquired all published papers and then constructed raw and clean datasets. We ran citation and co-citation analyses on these datasets separately. Our investigation reveals that clean data yielded more trustworthy and valid results than raw data with redundant references. This study provides an answer to how and why data in bibliometric analysis needs to be cleaned. It thus contributes to the literature by suggesting a new route for scholars to improve the accuracy and reliability of their bibliometric findings.