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This study aims to focuse on the flow behavior of a specific nanofluid composed of blood-based iron oxide nanoparticles, combined with motile gyrotactic microorganisms, in a ciliated channel with electroosmosis.

This study applies a powerful mathematical model to examine the combined impacts of bio convection and electrokinetic forces on nanofluid flow. The presence of cilia, which are described as wave-like motions on the channel walls, promotes fluid propulsion, which improves mixing and mass transport. The velocity and dispersion of nanoparticles and microbes are modified by the inclusion of electroosmosis, which is stimulated by an applied electric field. This adds a significant level of complexity.

To ascertain their impact on flow characteristics, important factors such as bio convection Rayleigh number, Grashoff number, Peclet number and Lewis number are varied. The results demonstrate that while the gyrotactic activity of microorganisms contributes to the stability and homogeneity of the nanofluid distribution, electroosmotic forces significantly enhance fluid mixing and nanoparticle dispersion. This thorough study clarifies how to take advantage of electroosmosis and bio convection in ciliated micro channels to optimize nanofluid-based biomedical applications, such as targeted drug administration and improved diagnostic processes.

First paper discussed “Numerical Computation of Cilia Transport of Prandtl Nanofluid (Blood-Fe₃O₄) Enhancing Convective Heat Transfer along Micro Organisms under Electroosmotic effects in Wavy Capillaries”.

As sustainable performance has a central role in the small and medium enterprises (SMEs) performance literature, this study aims to examine the influence of networking capabilities in enhancing sustainable performance through knowledge workers’ productivity and digital innovation. It also examines the sequential mediating role of knowledge workers’ productivity and digital innovation on networking capabilities and SMEs’ sustainable performance relationship.

Data were collected from 308 knowledge workers in the information technology sector and analyzed using the Hayes Process Macro bootstrapping method to test the proposed hypotheses.

Results indicate that knowledge workers’ productivity and digital innovation individually and sequentially mediate the relationship between networking capabilities and SME’s sustainable (economic and environmental) performance, surprisingly, they do not act as a mediator between networking capability and SME’s social performance. SMEs should prioritize investments in the professional development of their knowledge workers through training and skill enhancement programs. This investment equips knowledge workers with the tools to effectively use the knowledge and resources acquired through networking. Thus, knowledge workers may improve performance by using these resources to tackle challenges.

Although this research focused on this specific context, it is prudent to acknowledge that additional factors may also exert influence on sustainable performance within SMEs, factors that managers may consider when making decisions. Methodologically, the cross-sectional design of this research poses a potential limitation, as it does not allow for the complete elimination of endogeneity concerns. However, it is worth noting that scholars have endorsed the use of cross-sectional data in cases where management researchers aim to expand beyond well-documented and longitudinal data sets.

This research offers practical recommendations for SMEs to improve their sustainable performance through networking. SMEs should seek partnerships with complementary knowledge to improve operations and for other performance-oriented benefits.

This study adds significantly to the literature on sustainable SME performance by studying the interdependent effects of networking capabilities. It also represents the individual and sequential mediation mechanism that links networking capabilities to SME success through knowledge worker productivity and digital innovation.

Recently, both researchers and practitioners have been very interested in the impact of leadership on employee engagement. Thus, I aimed to examine the relationship between spiritual leadership and work engagement through the mediating role of spiritual well-being at work.

I assessed spiritual leadership, engagement, and well-being in an empirical study based on a sample of 223 employees. I collected data through a survey-based method and analyzed them using structural equation modeling (SEM).

The present study contributes to the existing knowledge in the leadership field, especially spiritual leadership. The results revealed that spiritual leadership impacts employees’ work engagement by indirectly influencing employees’ spiritual well-being.

Theoretically, the findings imply that spiritual well-being can be one of the factors considered in enhancing work engagement through spiritual leadership.

Finding evidence that spiritual leadership, like other leadership styles, can foster employee engagement. Therefore, leaders should take care of employees’ spiritual needs.

Many researchers have indicated that well-being is associated with employee engagement. However, they overlooked employees’ spiritual well-being in the research. The study confirmed the unexplored mediating role of spiritual well-being between spiritual leadership and employee engagement.

The purpose of this research is to examine the relationship between high- performance work systems (HPWS) and organisational innovation in hospital settings, examining the role of employee engagement as a mediator in this relationship. Additionally, the study aims to investigate the moderating role of perspective-taking between HPWS and employee engagement as well as the moderating effect of trust in leader on the connection between employee engagement and organisational innovation.

A quantitative-deductive causal method, along with a cross-sectional approach, was utilized. Structural equation modelling was applied to analyse data from a sample of 530 doctors employed in hospitals, practicing human resources management in the public and private sectors of Sindh province, Pakistan.

The findings show positive effects of HPWS on employee engagement and organisational innovation. Additionally, employee’s engagement partially mediates the relationship between HPWS and organisational innovation, while the moderating role of perspective-taking significantly influences the link between HPWS and employee engagement.

While HPWS are recognized for enhancing organisational innovation, this study confirms their positive effects on individual and organisational outcomes, particularly within the healthcare sector in Pakistan. This study suggests that when HPWS are effectively perceived and implemented, these integrated practices can be beneficial for both employees and organizations, even in challenging situations.

Drawing an inference from institutional theory and dynamic capabilities view, this study empirically examined the impact of three institutional dimensions (regulative, normative and cognitive) and green entrepreneurial orientation (GEO) on a business firm’s performance. In addition, the moderating effect of dynamic capabilities on the relationship between GEO and firm performance was also explored.

The data were collected from 527 information technology (IT) firms in Pakistan using paper–pencil questionnaires, and the hypotheses were tested using structural equation modeling.

The findings showed that the regulative and normative institutional dimensions enhance GEO and firm performance in the selected developing country. However, the cognitive institutional dimension fails to report any substantial influence on GEO and firm performance. The findings raised concerns about lower individual accountability as well as the promotion of green practices and firm performance. In addition, dynamic capabilities positively moderate the GEO influence on firm performance.

With the interplay of institutional dimensions, GEO (as mediator) and dynamic capabilities (as moderator), this study developed and tested a unique framework to understand their influence on firm performance. Specifically, we extended the literature by giving evidence that among the three institutional dimensions, only regulative and normative are considered more important because of their direct and indirect (through GEO) positive effect on firm performance. In contrast, the cognitive institutional dimension failed to report any significant direct or indirect impact on firm performance in our study.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

Environmentally Responsible Behavior (ERB) refers to actions and practices aimed at minimizing negative environmental impact through conservation, waste reduction and sustainable practices driven by concern for environmental well-being. This study aims to explain the effect of environmental attitudes of hotel guests visiting green and non-green hotels on their ERB. In the research, hotels with different concepts were handled separately and a comparative analysis was made between both customer types.

The study was assessed using two separate models: one for green hotel guests and another for non-green hotel guests. Data were collected from 205 participants from green hotel guests in Turkiye and 206 participants from hotel guests visiting non-green hotels in Turkiye, using a survey form with a convenience sampling method. The hypotheses were analyzed with the structural equation model and multi-group analysis (MGA) in the Smart PLS statistical program.

The ecocentric attitude positively affects political and community activism but does not impact recycling, educational behavior, green consumerism or overall ERB. The technocentric attitude negatively influences recycling, educational behavior, green consumerism, community activism and ERB but does not negatively affect political activism. The dualcentric attitude positively impacts all ERB sub-dimensions. The multi-group analysis (MGA) revealed that dualcentric attitudes have a stronger influence on green consumerism, political activism and recycling behavior among green hotel guests compared to non-green hotel guests.

These findings directly impact hotel managers, underscoring the strategic significance of adopting and advocating green practices. Implementing environmentally friendly initiatives not only appeals to environmentally conscious consumers but also holds the potential to cultivate ERB among all guests. By integrating sustainability into marketing strategies, hotels can communicate their commitment to environmental stewardship, positively influencing guests’ environmental attitudes and behaviors.

The paper’s key contribution is its comparative analysis of ERB between guests at green and non-green hotels. It reveals how tourists’ environmental attitudes influence their hotel choices and behaviors, offering insights into sustainable tourism. Additionally, it explores the differences between ecocentric, technocentric and dualcentric attitudes, enhancing our understanding of how varying environmental concerns shape tourists’ behaviors and choices and contributing to the broader discussion on environmental psychology in tourism.

The emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified theory of acceptance and use of technology to explore how effort expectancy (EEC) and performance expectancy (PEE) individually, jointly, congruently and incongruently affect higher education students’ intentions and actual uses of ChatGPT for their learning.

An advanced methodology – polynomial regression with response surface analysis – and a sample of 1,461 higher education students recruited in Vietnam through three-phase stratified random sampling approach were adopted to test developed hypotheses.

Both EEC and PEE were found to have a direct positive impact on the likelihood of higher education students’ intention to use ChatGPT, which in turn promotes them actually use this tool for learning purposes. Conversely, a large incongruence between EEC and PEE will lower the level of intentions and actual uses of ChatGPT for learning. However, when there is a growing incongruence between EEC and PEE, either in a positive or negative direction, the likelihood of students’ intentions to use ChatGPT for learning decreases.

Some practical implications are subsequently recommended to obtain advantages and address potential threats arising from the implementation of this novel technology in the education context.

This study shed the new light on the educational setting by testing how higher education students’ intentions to use ChatGPT and subsequent actual uses of ChatGPT are synthesized from the balance between high EEC and PEE.

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).

To achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.

The empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.

One of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.

This study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.