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The primary objective of this investigation was to explore how employees’ utilization of social media for work-related purposes impacts their service innovation behavior, both directly and through the intermediary mechanisms of knowledge management and employees’ risk-taking.

In developing its conceptual framework, this study has drawn upon the stimulus-organism-response (SOR) theory. To test its hypotheses, this study has surveyed 241 financial analysts from ten Iranian financial companies and has employed variance-based structural equation modeling (specifically, PLS-SEM) with the assistance of “WarpPLS 8.0 software.”

The findings revealed that employees’ work-related use of social media positively influences their service innovation behavior using knowledge management, encompassing knowledge sharing and acquisition capability as well as employee risk-taking. However, this influence is not directly significant.

To the best of our knowledge, this study marks the first instance in which the effect of work-related use of social media on employee service innovation behavior directly and through the mediating roles of knowledge management and risk-taking has been investigated through the lens of the SOR paradigm, especially in the financial sector.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

This study aims to assess the impact of personality traits, risk perception and perceived coronavirus disease 2019 (COVID-19) disruption on the investment behavior of individual investors in the Indian stock market.

This study adopts a survey approach. The sample comprises 315 active retail investors investing in the Indian stock exchange. Two-stage analysis technique regression and Artificial Neural Network (ANN) were used for data analysis. Study hypotheses were tested through regression and ANN was adopted to validate the regression results.

Two regression models were modeled to test the research hypotheses. Findings showed that risk perception and COVID-19 disruption have a significant positive and neuroticism has a significant negative impact on short-term investment decisions, while the role of conscientiousness in determining short-term investment decisions was not found significant. Results also showed a positive impact of neuroticism and conscientiousness and a negative impact of risk perception on long-term investment decisions. The role of COVID-19 disruption was found negative but insignificant in predicting long-term investment decisions.

This study has practical implications for many parties like retail investors, financial advisors and policymakers. This study will assist the investors to realize that they do not always take rational financial decisions. This study will suggest the financial advisors to use the knowledge of behavioral finance in making the advisors' advisory and wealth management decisions. This study will also assist the policymakers to outline behaviorally well-informed policy decisions to protect the interests of investors.

India is one of the fast-growing economies in the world. India has a vast population of active investors and determining investors' investment behavior adds novelty to this study as developed economies have remained the main focus of previous studies. The other novel feature of this study is that this study tries to assess the impact of COVID-19 disruption along with personality traits and risk perception on investment behavior. The other valuable factor of this study is the use of ANN to predict the relative importance of the exogenous variables.

This study aims to show that employees' excessive work-related use of enterprise social networks (ESN) can be accompanied by some work-related strains, hindering them from continuing utilization of ESN at work. To this end, the impact of employees' excessive work-related utilization of ESN on their discontinuous usage intentions by mediating roles of employees' impression management concerns, privacy concerns and ESN fatigue will be evaluated.

Stimulus-organisms-response (S-O-R) framework has been drawn to support the design of this research. Using an entirely random data collection, 173 ESN users from 10 Iranian organizations were surveyed. The model was assessed using partial least squares structural equations modeling (PLS-SEM).

The results of the study confirm that employees' excessive work-related use of ESN positively affects impression management and privacy concerns, resulting in ESN fatigue. Furthermore, ESN fatigue plays a predicting role in ESN discontinuous usage intention.

According to the obtained results, if work-related use of ESN exceeds a normal threshold (i.e. excessive usage), employees will stop using ESN in their work due to the work-related strains delivered to them, revealing the dark side of ESN usage in organizations.

This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.

In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.

The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.

The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

To attain green economic efficiency, small, micro and medium-sized firms must follow environmental paradigms. This research aims to discover the relationship between green intellectual capital, green entrepreneurial orientation, green marketing, green organizational culture and competitive advantage strategies to attain sustainable manufacturing business success.

Today, many companies have accepted their responsibilities, that their operations should not harm the environment. This paper aims to discover the relationship between green factors and competitive advantage strategies to succeed in sustainable manufacturing. The employees of the large manufacturing firms in China are the sample population of the present investigation. Through simple random sampling, surveys were distributed via email. The present study was a quantitative analysis. The analytical tool utilized here was structural equation modeling and SmartPLS program applications.

The empirical outcomes find that green intellectual capital positively influences competitive advantages and sustainable success in business. In addition, the impact of green entrepreneurial orientation on competitive advantages and sustainable success is positive and significant. The findings illustrated that green marketing is an essential factor in competitive advantage and sustainable success in business. Another point is that green organizational culture positively affects competitive advantages and sustainable success. Finally, competitive advantages have significantly affected sustainable success in business.

The outcomes help specialists enhance their practices to reflect sustainable business efficiency and competitive advantages.

This is the first study that examined businesses' sustainable success and green factors in a comprehensive model and using a specific sample of manufacturing companies based on green technologies.

The practice of cronyism is a pervasive problem for most businesses and a great hindrance for employees, but empirical literature on its outcomes is scant. In light of such gaps, the objective of this study is to examine the relationship between organizational cronyism and employees' silence behavior through the mediating role of felt violation and the moderating role of continuance commitment.

A time-lagged cross-sectional survey comprising 226 respondents is carried out in a metropolitan city of a developing country (Lahore, Pakistan). The respondents were selected using the convenience sampling technique.

The findings reveal that organizational cronyism influences employees' silence (acquiescent and quiescent) both directly and indirectly (via felt violation). However, continuance commitment was noted to work as a boundary condition only between felt violation and quiescent silence.

Although the study deals with common method bias by collecting data in two waves, it may restrict causality. The findings not only have implications for the academicians, but also contribute to the conservation of resources theory. This study suggests organizations develop and implement a comprehensive intervention strategy that focuses on both prevention and damage control as a result of organizational cronyism.

Drawing upon the conservation of resources theory, this study adds value to the literature by empirically investigating the outcomes of cronyism at work. Moreover, the outcomes and mechanisms under consideration have largely been ignored in the literature.

Recently, work-related social media use (WSMU) in organisations and its association with employee outcomes have received considerable research attention. This study examines the association between WSMU, psychological well-being (PW) and innovative work performance (IP). In addition, it explores the mediating role of PW and the moderating role of fear of missing out (FoMO).

A sample of 233 employees working in different organisations was recruited from India to complete the survey. Structural equation modelling was applied to analyse the data.

The result reveals that WSMU has a positive and direct effect on IP. Moreover, the indirect effect via PW among the association was positive and significant. Furthermore, FoMO moderates the indirect relationship between WSMU and IP.

This research is a pioneering work that has contributed to the scarce literature by exploring the relationship between employees' social media use, PW and IP. This research has important theoretical and management contributions because it examines the impact of WSMU on IP, mediating role of PW and moderating role of FoMO among the association between WSMU and employee outcomes.

This study aims to explore the latent potential of alternative-responsible tourism and ecotourism leadership in sustainable tourism and the sustenance of rural communities in Musina Municipality in Limpopo Province, South Africa. It also sought to compare this potential with other African pastoral areas and to integrate it with various rural sites elsewhere abroad.

Narrative literature, document reviews, interviews and focus group discussions were used to garner relevant data and were analyzed through cross-tabulation analysis and manually. On this account, probable climate change-bound environmental consequences, rural, tourism, farming and ecological administration issues came to the forefront.

The examination correlates the potential position of proper ecotourism exercises and responsible tourism as an agreeable path to promote sustainability in rural localities. Subsequently, upon apparent countryside products, ecotourism and farming exercises glimpsed in the study area, environmental degradation poses a conceivable hazard to natural resource governance. Hereafter, it aggravates the possibilities for climate change effects and poor subsistence.

The rich biodiversity in the study area provides platforms for sustainable rural tourism enterprises and addresses any pertinent concerns. Thus, the study has manifested a demand for a creative tourism approach and innovations against environmental change outcomes and to capacitate Musina Municipality residents and probably others elsewhere on the continent to engage in ethical tourism initiatives and sustainable livelihoods.