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The importance of e-literacy of staff in the digital life is fundamentally very crucial, to such an extent that it is considered as one of the primary conditions for successful utilization of knowledge management processes using social technologies within organizations. This study aims to explain and test a novel conceptual model to show the impact of applying social technologies on knowledge management (KM) processes in the context of Iranian organizations, considering the moderator role of e-literacy of employees.

Based on an in-depth study of the relevant literature, a questionnaire was designed. The sound questionnaires obtained from our sample size was 207 and respondents were experts in the field of information technology (IT) within the Central Office of Insurance companies in Tehran. The collected data were analyzed using structural equation modeling and path analysis.

The results indicate that the use of social technologies with the factor loading of 0.57 has the highest impact on knowledge exchange and 0.61 on knowledge utilization. In addition, the results indicate that e-literacy with the factor loadings of 0.69 and 0.74 has the highest impact on knowledge exchange and knowledge utilization, respectively. In addition, the impact of social technologies with the factor loading of 0.82 has the highest impact on e-literacy.

One of the limitations of this study was the generalizability of the findings, which may be limited, as it is focused on one developing country. Also, the lack of full implementation of KM and the use of social technologies in the insurance industry may affect the obtained results.

To the best knowledge of the authors, this study is among the first of its kind which examines the impact of social technologies usage on the KM processes considering an important variable, i.e. e-literacy of employees.

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.