Journal Articles
Permanent URI for this collectionhttp://10.0.100.92:4000/handle/123456789/21
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Item Public and scholarly interest in social robots: An investigation through Google Trends, bibliometric analysis, and systematic literature review(Technological Forecasting and Social Change, 2024-09) Mishra, Nidhi; Bharti, Teena; Tiwari, Aviral Kumar; Pfajfar, GregorThe COVID-19 pandemic has expedited the integration of social robots within the healthcare sector. This research employs a tripartite methodology, combining Google Trends analysis, bibliometric analysis, and a systematic literature review, to gauge both public and research interest in social robots within the healthcare domain. In the Google Trends analysis, search query data for “Social Robots” was retrieved, encompassing both “all categories” and the specific “health” category. Seasonal effects on relative search volumes (RSV) were assessed through the cosinor model. The analysis confirmed statistically significant seasonal patterns in RSV for “social robots” within the “health” category. Conversely, for the broader “all categories,” only the intercept showed significance, while sinw and cosw were deemed insignificant. For bibliometric analysis, the global literature on “robotics” and “healthcare” was examined in the SCOPUS database. From the extensive pool of publications, 144 relevant studies were identified out of 4037 publications. These studies were further analyzed using VOSviewer, providing insights into recent trends and hot topics concerning social robots in healthcare. The systematic literature review focused on 46 articles published from 2019 to the end of 2023. The findings revealed a lack of consensus on the drivers, barriers, and outcomes associated with social robot acceptance and human-robot interaction (HRI). The study systematically maps the existing research on these aspects, introducing a novel categorization and presenting the concept of a “robot user's ecosystem.” This concept emphasizes the imperative involvement of all stakeholders in the development and understanding of social robots. Ultimately, this methodological approach not only identifies nine research gaps in the current literature but also formulates numerous research questions to guide future researchers in this domain.Item Evaluation of sustainable cold chain suppliers using a combined multi-criteria group decision-making framework under fuzzy ZE-numbers(Expert Systems with Applications, 2024-07) Ecer, Fatih; Haseli, Gholamreza; Krishankumar, Raghunathan; Hajiaghaei-Keshteli, MostafaHealth 4.0 actively focuses on cold supply chains (CSCs) due to digital transformation and technological advancements in refrigeration. With the ill effects of climate change and greenhouse gas emissions, countries globally seek sustainable supply chain management. In this context, integrating sustainability with CSCs becomes crucial. Health sectors actively consider CSC for their activities. In this context, to gain potential suppliers for healthcare management, the present work focuses on grading suppliers from the CSC with sustainability factors. Previous related studies on sustainable cold chain supplier selection (CSSS) (i) have not modeled uncertainty effectively, and (ii) have yet to use any approach under fuzzy Z-numbers information. Therefore, this paper developed an integrated framework, including the Logarithm Methodology of Additive Weights (LMAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods based on extended fuzzy Z-numbers. The practicality of the framework is exemplified via a case example within the Indian health sector, and results indicate that job creation, value price, and productivity via renewable resources are the most critical factors. Moreover, regarding the findings, the economic dimension is the foremost pillar for effective selection. Sensitivity and comparison analyses are performed to understand the efficacy of the integrated framework as well. The proposed framework could contribute to the relevant literature due to its capacity to cope with complex decision-making problems and find many application areas in engineering.