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Green, lean and six sigma (GLSS) practices are widely used and well accepted techniques that have the capability to improve the economic, social and environmental performance of Pakistani small and medium enterprises (SMEs). However, implementation of these practices in an integrated approach has not yet been witnessed in Pakistani SMEs due to a variety of challenges. To overcome the implementation challenges, this study has analyzed the impact of GLSS implementation over the sustainability in Pakistani SMEs.

This study consists of two phases. In phase 1, interviews were conducted to scrutinize the elements of GLSS implementation to simplify the model and in phase 2, a questionnaire survey was conducted to collect the data from the SMEs. The partial least squares structural equation modeling (PLS-SEM) approach is used to analyze the relationships among the latent variables and constructs.

Results showed that, leadership for GLSS, understanding GLSS techniques and technology upgradation are considered the most important elements for GLSS implementation in Pakistani SMEs. The environmental and social perspectives have been given more weightage compared to economical perspective. This inferred that there is a need to focus more on environmental and social perspectives in SMEs as compared to economic perspectives to achieve sustainable growth. Moreover, the results of the hypothesis testing revealed that GLSS implementation has a significant positive impact over the sustainability in SMEs considering the Pakistani scenario (β = 0.529, STDEV = 0.078, t = 6.81, p = < 0.001).

This study is the first of its kind for Pakistani SMEs. The structural model developed in this study for Pakistani SMEs will help practitioners to understand the important elements of GLSS implementation and sustainability dimensions.

Studies have shown that customer orientation has a substantial impact on a business's success. This study examines the effects of positive personality on salespeople's proactive customer orientation (PCO) and responsive customer orientation (RCO) by incorporating the effects of job enthusiasm and transformational leadership.

A questionnaire survey is conducted. A total of 511 questionnaires are received from Taiwan's life insurance salespeople. Partial least squares (PLS) regression is used to test the hypotheses.

The results show that positive personality influences PCO and RCO both directly and indirectly through job enthusiasm. The effect of transformational leadership is also found to be significant. Financial service companies should be concerned about the important role of positive personality and transformational leadership in promoting job enthusiasm, PCO and RCO among salespeople.

Previous studies mostly focused on the direct relationship between customer orientation and organizational outcomes, neglecting the role of individual personality. This gap leaves us wondering how a positive personality influences a salesperson's proactive and responsive customer orientation. To the authors' knowledge, this is the first study to examine the mechanisms of a positive personality, job enthusiasm, and transformational leadership on salespeople's PCO and RCO.

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.