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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.

This study aims to investigate the influencers’ credibility dimensions (i.e. attractiveness, trustworthiness, expertise) on purchase intention (PI) through the mediating role of cognitive and affective online engagement among the aesthetic dermatology consumers in Jordan.

The population of this study entails all followers of aesthetic dermatology clinics on their Instagram accounts. However, only three influencers from the aesthetic dermatology industry were selected and approved the request of sharing the survey instrument on their official platforms. Overall, 600 surveys were distributed, but only 384 were completed fully, constituting a 64% response rate.

The data analysis revealed an excellent fit for the data and indicated an impact of attractiveness and expertise on online engagement and PI. Moreover, a mediating influence was also found for online engagement on the path between influencer credibility and PI.

This study has a limitation of collecting the data from only three influencers; consequently, collecting data from the followers of more than four influencers would get more generalizable results. Second, considering further, examining the mediating role of other variables such as electronic word-of-mouth (EWOM) and loyalty programs could also provide further insights onto the nature of the factors affecting the PI. In addition, future studies should examine the differences of using more than one social media platform.

The main findings of this study have a number of managerial implications for marketing management that hint at liking the influencers who are highly trusted owing to their extensive expertise in the area they are marketing rather than only depending on their physical attractiveness. The Jordanian culture does not focus only on the image shared by the social media as the reviews can either support or decline the influence of even the celebrity. Significantly, a set of managerial implications come from the current research.

Two major areas are the most important; these are the trustworthy issue and the EWOM. The marketers should encourage their customers to openly talk about their experiences as they have an imperative role in liking influencers in a way that improves their PI. The second implication is related to social media platforms management that marketing managers should resolve any negative EWOM caused and to enhance followers’ satisfaction levels of the services. The increase in satisfaction positively affects PI, and the service makes the influencer role become more effective.

This study was able to add a value to the current understanding of the main antecedents of customer engagement by looking at these dimensions of perceived credibility. Another contribution was captured in this study by successfully validating the meditating impact of customer engagement between influencers’ credibility dimensions and PI, especially in the absence of the studies that have addressed such relationship.

This paper aims to discuss the potential of al-Wasatiyyah (hereafter, AW) value concept for human sustainability (hereafter, HS) values and its applicability to civil service practice (CSP) in Malaysia. AW is a derivative of an Arabic word that means excellent, moderate, just and professional.

This paper explains the potential of AW concept for HS both in epistemological and ontological perspectives with a focus on CSP. It focuses on the AW value concept according to Ibn Miskawaih and its applicability in CSP in Malaysia. Using content analysis and “integration perspective” between AW and contemporary Malaysia CSP practice, several questions were unlocked in this paper. They include the following: what are AW values for HS? What is the standard of value for sustainable CSP in Malaysia? And how HS from AW perspective can generate quality CSP in Malaysia?

It is found that AW can extend and enrich the meaning of value for HS and subsequently the civil service quality in Malaysia. Findings showed that AW can offer a holistic approach through four AW core values. The study also showed that AW is embedded indirectly in Malaysia CSP before it was highlighted under Najib Razak administration. This study suggests continuous emphasis on AW values in three service areas, namely, service requirements (SR), service priorities (SP) and service quality (SQ).

The limitation is to bring the value concept into the HS parameters. HS is indexed by quantitative indicators including economy, health and education achievements, rather than value concept. This study tries to expand the potential and importance of value as an indicator for HS. That is through a concept named as AW.

AW values and approaches are perceived to be a potential focus system for HS and resource building in Malaysian civil service.

Extending the meaning of HS through the application of AW concept is perceived to enrich social values and development.

This paper argues that current discussion on HS is not fit for all purposes because it lacks human great values. This paper subsequently argues AW concept as potential HS values, especially for CSP, in Malaysia.

The purpose of this paper is to analyze the surface integrity, including recast layer thickness, surface crack density, X-ray diffractions study and microhardness for Ni53.49Ti46.51 shape memory alloy (SMA) during wire-spark erosion machining.

Four persuasive process parameters, that is, spark on time (SON), spark off time (SOFF), wire feed (WF) and spark gap voltage (SV), have been chosen for the current investigation. Efforts have been done to explore the effects of above said parameters on the machined surface of Ni-Ti SMA by embracing box Behnken design of response surface methodology (RSM). Cutting speed and ten-point mean roughness (Rz) has been taken into account as response variables. Analysis of variance test was also performed for both response parameters with the coefficient of determination (R²) 0.9610 for cutting speed and 0.9252 for ten-point mean Rz.

The recast layer thickness from 7.83 to 12.13 µm was developed near the machined surface at different parametric settings. The least surface crack density was found at the lowest value of ten-point mean Rz, while most surface crack density was identified at the highest value of cutting speed. The microhardness near the machined surface was increased by approximately 1.8 times bulk-hardness of Ni53.49Ti46.51 SMA.

Some researchers have done a study on average surface roughness, but very few investigators concentrated on ten-point mean Rz. Surface crack density is an essential aspect of machined parts; other researchers have seldom reported it. The novelty of this research work is that the influence of SON, SV, WF and SOFF on cutting speed, Rz, recast layer thickness, micro-hardness and surface crack density proximate the machined surface while machining workpiece material.

This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.

In view of dynamic and widespread economic transformation in emerging economies, managing organizational change and growth in this context deserves more research attention. The purpose of this paper is to examine how three organizations in different industries manage change, growth and transformation in their organizational ecosystem.

The authors conducted in-depth interviews with the leadership of three organizations in different economic sectors in India, a country representing an emerging economy. The authors also reviewed historical data from these organizations. Three case studies illustrating the evolution of these organizations were developed from the data collected.

Lessons and implications from the three case studies suggest the following key elements of effective organizational change mechanisms in an emerging economy: visionary entrepreneurial leadership; program quality excellence; scale growth and scope expansion; network capabilities; and sustainable stakeholders’ engagement. At the same time, this study also shows how these organizations manage change, growth and transformation in the context of a society with strong traditions and cultural norms.

Results and conclusions may be limited by the fact that the study is based on three case studies. Additional studies from a variety of industries with large numbers of participants will be helpful in more fully understanding the ways in which change, growth and transformation can best be developed and deployed in different organizational settings.

The proposed model of organizational change in an emerging economy may assist organizational leadership in designing and sustaining their change efforts.

This study highlights the role of visionary entrepreneurial leadership and the impact of organizational growth mechanisms on organizational value delivery capabilities and organizational reputation.

Lessons and implications of five growth steps of outstanding organizations in an emerging economy context provide valuable insight for organizational change, growth and transformation in other emerging contexts.

The appalling abuse healthcare workers have endured from patients is long documented in the popular press and social media. Less explored in the healthcare management literature is workplace abuse that professional nurses experience from their coworkers.

The authors use text-based first-hand accounts from nurses posting on Reddit (N = 75) to better understand the types and context of abusive acts endured by their coworkers in the contemporary healthcare setting. Each account is content analyzed using two raters, and thematic analysis is utilized to summarize findings.

Findings indicate that nurse workplace abuse frequently targets new entrants to a work unit (e.g. recent grads), typically is ongoing, takes verbal and nonverbal forms, mainly stems from coworkers (i.e. lateral mistreatment), and frequently takes place in front of other coworkers, mainly in hospital settings.

By applying the lens of mindfulness, healthcare organizations can transform these harmful interactions within the nursing profession. The authors offer administrators and frontline workers practical implications for mitigating workplace abuse, including reshaping the culture, bystander interventions and explicit leadership support.

First-hand accounts from nurses in the frontlines of healthcare provide a rich voice that reveals the reality of ongoing verbal and nonverbal peer abuse in hospitals and healthcare settings.

This study explores the influence of employee empathy on brand love, which subsequently affects customer advocacy, willingness to pay more (WTPM) and tolerance of failure. Additionally, it investigates the mediating role of customer delight and gratitude in connecting employee empathy with brand love and the moderating effect of power distance belief (PDB) between employee empathy and customer delight and gratitude.

A total of 751 usable data were gathered through scenario-based online surveys within a hotel context. The proposed conceptual model used Partial Least Squares - Structural Equation Modeling (PLS-SEM) for testing.

The results affirm the impact of employee empathy on brand love through the pathways of customer delight and gratitude, ultimately influencing positive customer behaviors, such as advocacy, WTPM and tolerance of failure. Moreover, the findings suggest that PDB diminishes the effect of employee empathy on customer gratitude, although it does not affect customer delight.

The study introduces novel insights into the significance of employee empathy as an antecedent of brand love. It contributes to the literature by concurrently conceptualizing customer delight and gratitude as mediators between employee empathy and brand love, consequently leading to favorable consumer behaviors. Furthermore, it advances our theoretical comprehension of an individual customer’s PDB and its psychological impact.

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).

The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.

The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.

The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.