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The negative influence of gamification on online communities has received little attention in the available literature. The study examines the adverse effects of gamification during engaging in online communities.

Gap-spotting methods were used to develop the research questions, followed by model development using the social exchange and social-network theories. Data were collected from 429 samples. The study applied partial least squares structural equation modeling to test the research hypotheses followed by ANN application.

The study identified five factors related to gamification that have a significant adverse effect on the mental and emotional well-being of the users. Furthermore, the results of PLS-SEM were then compared through an artificial neural network (ANN) analytic process, revealing consistency for the model. This research presents a theoretical contribution by providing critical insights into online gamers' mental and emotional health. It implies that gamification can even bring mental and emotional disturbance. The resulting situation might lead to undesirable social consequences.

The result highlights the managerial and social relevance from the perspective of a developing country. As respondents are becoming more engrossed in online gaming, managers and decision-makers need to take preventive measures to overcome the dark side of online gaming.

The present study shows that the dark side of gamification has some adverse effects on human mental and emotional health. The study's findings can be used to improve gamification strategies while engaging online communities.

This research aims to investigate critical success factors, barriers and initiatives of total productive maintenance (TPM) implementation in selected manufacturing industries in Addis Ababa, Ethiopia.

This study built and looked into a conceptual research framework. The potential barriers and success factors to TPM implementation have been highlighted. The primary study techniques used to collect relevant data were a closed-ended questionnaire and semi-structured interview questions. With the use of SPSS version 23 and SmartPLS 3.0 software, the data were examined using descriptive statistics and the inferential Partial Least Square Structural Equation Modeling (PLS-SEM) techniques.

According to the results of descriptive statistics and multivariate analysis using PLS-SEM, the case manufacturing industries' TPM implementation initiative is in its infancy; break down maintenance is the most widely used maintenance policy; top managers are not dedicated to the implementation of TPM; and there are TPM pillars that have been weakly and strongly addressed by the case manufacturing companies.

The small sample size is a limitation to this study. It is therefore challenging to extrapolate the research findings to other industries. The only manufacturing KPI utilized in this study is overall equipment effectiveness (OEE). It is possible to add more parameters to the manufacturing performance measurement KPI. The relationships between TPM and other lean production methods may differ from those observed in this cross-sectional study. Longitudinal experimental studies and in-depth analyses of TPM implementations may shed further light on this.

Defining crucial success factors and barriers to TPM adoption, as well as identifying the weak and strong TPM pillars, will help companies in allocating their scarce resources exclusively to the most important areas. TPM is not a quick solution. It necessitates a change in both the company's and employees' attitude and their values, which takes time to bring about. Hence, it entails a long-term planning. The commitment of top managers is very important in the initiatives of TPM implementation.

This study is unique in that, it uses a new conceptual research model and the PLS-SEM technique to analyze relationships between TPM pillars and OEE in depth.