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A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.

This study investigated the effects of reward system (RS) and job conditions (JC) on employee retention (ER). In particular, this study addressed the mediating effect of employee engagement (EE) on the relationship between RS, JC and ER.

This paper employed descriptive survey approach and the unit of analysis consisted of public hospital nursing staff. Data were collected using questionnaires with a sample of 370 nurse respondents. Structural equation modelling with Smart-Partial Least Squares (PLS) 3.3.8 was used in a statistical analysis.

The results revealed that RS and JC significantly related to ER. The study also showed the direct effect of RS and JC on EE. These findings indicate that (EE) has a partial mediating role in the relationship between RS, JC and ER.

The study offers important policy insights for public nursing stakeholders who seek to increase retention of skills among their nursing staff. The findings are also crucial because they may help the health sector improve their ER strategies, especially in dynamic and competitive business situations where organisations are challenged to retain personnel from a limited skilled workforce.

The findings of this study contribute to the literature on retention of nursing employees by enhancing the understanding of the influences of EE, RS and JC on ER among public hospitals.

This study aims to examine the impact of website design, reliability and perceived ease of use as an engagement motivational factors on customer e-trust and e-retention in online shopping.

By using deductive approach, quantitative methods and purposive sampling technique, this study has collected the data from 295 young online customers to enhance an understanding of website design, reliability and perceived ease of use in an online shopping context.

The findings revealed interesting insights where reliability is the most significant predictor of customer e-trust in online shopping, followed by perceived ease of use and website design. In addition, a significant mediating effect of e-trust is found between customer e-retention, website design, reliability and perceived ease of use.

Future research is recommended to predict the antecedents of online engagement motivational factors with value co-creation and co-creation experience in online shopping context.

This study offers fresh insights about driving elements and impediments of online customer retention. Customer engagement comprising of website design, reliability and perceived ease of use appear to influence the online customer retention through direct and indirect effect.