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This study aims to assess the effect of job insecurity, customer incivility and work–life imbalance on Lebanese bank employee workplace well-being (EWW), while investigating the moderating role that positive and negative affect might have.

Quantitative data was collected from 202 respondents and analyzed using structural equation modeling system through IBM SPSS and AMOS.

Results revealed that each of the independent variables has a negative, statistically significant effect on Lebanese bank EWW. The positive affect and the negative one are shown to have a moderating effect that lessens and boosts, respectively, these negative effects.

The study adds to the literature on EWW while highlighting the high-power distance and collectivist society that the research took place in.

Limitations include the sample size that was hoped to be larger, in addition to the self-reporting issue and what it entails in the data collection process.

The study has many practical implications, including the validation of a questionnaire in a developing Arab country, hence providing a reliable tool for researchers. HR specialists should lean toward applicants with positive affect, ensuring that their workplace is occupied by members with enhanced resilience. Furthermore, employers should support their employees’ professional growth, thus, boosting their employability during turmoil and consequently making them less vulnerable in times of economic recession.

The study’s unique context, depicted in the harsh economic and financial crisis, makes the findings on EWW of a high value.

On April 2, 1987, IBM unveiled a series of long‐awaited new hardware and software products. The new computer line, dubbed the Personal Systems 30, 50, 60, and 80, seems destined to replace the XT and AT models that are the mainstay of the firm's current personal computer offerings. The numerous changes in hardware and software, while representing improvements on previous IBM technology, will require users purchasing additional computers to make difficult choices as to which of the two IBM architectures to adopt.

Recounts how medieval English Jewry began when Jews were invited to immigrate by William I and ended with their expulsion by Edward I in 1290. The Jewish community was important and for most of its existence it was prosperous, owing to its particular social function – being the bankers, moneylenders and financiers of the time. Concentrates on a relatively little known aspect of the medieval Jewish community: the role played by its women. Jewish women played a significant part in business, not just as the wives or widows of businessmen, but as entrepreneurs on their own account. This was in sharp contrast to the position of women in wider English society. Using contemporary documents, the article examines the scale and nature of the business activities of Jewish women in medieval England, sketches the activities of some of these female entrepreneurs, and attempts to investigate the factors which enabled them to play such a prominent role.

The purpose of this paper is twofold. First, this paper seeks to formalize a definition of activist entrepreneurship and differentiate it from social entrepreneurship. Second, this paper proposes a model that explains how the storytelling process, in the form of the message and means of communication, influences the activist identity process and consequently the legitimacy of the activist entrepreneur.

This paper explains the historical method and offers an overview of the unique case of Madam C.J. Walker and analyzes how she gained legitimacy as an activist entrepreneur by conveying psychological capital (Psycap) concepts in her message and political skill in the means of her communication. The paper also analyzed books being written on her and also letters that were exchanged between herself and her lawyer F.B. Ransom.

The authors have found out that Madam Walker used Psycap elements such as self-efficacy, hope, resiliency and optimism as message and elements of political skill such as social astuteness, interpersonal skill, networking ability and apparent sincerity as means to communicate the message toward her followers and built a legitimate social identity where she had won the trust of them.

The primary limitation of this paper is that it is theoretical in nature and uses only one case study to support the theoretical model. However, when analyzing complex relationships, historical cases offer a wealth of insight to solve the problem at hand.

By using the elements of the model discussed in the research paper properly, people could create a legitimate identity for themselves where any message they give to their employees, colleagues and sub-ordinates would be viewed as a selfless one and that would increase the chances of their messages or orders being accepted and obeyed by the followers.

Birding, the active seeking out and identification of birds, is a wide‐spread and fast growing avocation on this continent, and indeed throughout the world. Jon Rickert's A Guide to North American Bird Clubs lists 17 national/continental organizations for both professional ornithologists and amateur birders and 844 state, provincial, and local associations. In addition, there are those legions of “unorganized” bird watchers and occasional, inquisitive discoverers of backyard birds. Members of this diverse congregation of birders have at least one thing in common — the need for a reliable identification tool enabling them to correctly label the just‐seen, unfamiliar bird. A field guide is just such a tool.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

This study aims to investigate the relationship between earnings quality and corporate voluntary disclosure among Malaysian listed companies. Moreover, it examines the moderating effect of the ownership structure on the relationship between earnings quality and corporate voluntary disclosure.

This study covers 300 companies listed on Bursa Malaysia. It has used strategic, financial and non-financial information to measure voluntary disclosure; earnings management, persistence and smoothness to measure earnings quality; and institutional and managerial shareholders to measure ownership structure. Hierarchical regression analysis was used to investigate if ownership structure moderates the relationship between earnings quality and corporate voluntary disclosure.

The results in this work imply that companies with high earnings quality are more likely to disclose voluntary information to help stakeholders. Furthermore, this study provides original evidence that institutional ownership and managerial ownership play a main role as moderating variables that influence management motives toward practices of voluntary disclosure and earnings quality.

Because of the limited number of empirical studies on the relationship between voluntary disclosure and earnings quality, this study fills a gap in the literature by investigating the relationship between them. In addition, a lack of research exists on the effects of ownership structure on the relationship between voluntary disclosure and the earnings quality. Therefore, this study makes an original contribution to the literature by using institutional and managerial ownership as moderating variables to investigate the effects of the ownership structure on the relationship between voluntary disclosure and earnings quality in Malaysian companies.

The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.

This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).

The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.

This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.

This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

This paper aims to propose a novel active SLAM framework to realize avoid obstacles and finish the autonomous navigation in indoor environment.

The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. The localization of the robot is based on FastSLAM algorithm.

Simulation results of avoiding obstacles using traditional Q-learning algorithm, optimized Q-learning algorithm and FOQL algorithm are compared. The simulation results show that the improved FOQL algorithm has a faster learning speed than other two algorithms. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.

The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.