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Joule heating effect is a pervasive phenomenon in electro-osmotic flow because of the applied electric field and fluid electrical resistivity across the microchannels. Its effect in electro-osmotic flow field is an important mechanism to control the flow inside the microchannels and it includes numerous applications.

This research article details the numerical investigation on alterations in the profile of stream wise velocity of simple Couette-electroosmotic flow and pressure driven electro-osmotic Couette flow by the dynamic viscosity variations happened due to the Joule heating effect throughout the dielectric fluid usually observed in various microfluidic devices.

The advantages of the Joule heating effect are not only to control the velocity in microchannels but also to act as an active method to enhance the mixing efficiency. The results of numerical investigations reveal that the thermal field due to Joule heating effect causes considerable variation of dynamic viscosity across the microchannel to initiate a shear flow when EDL (Electrical Double Layer) thickness is increased and is being varied across the channel.

This research work suggest how joule heating can be used as en effective mechanism for flow control in microfluidic devices.

The characteristics of fluid motions in micro-channel are strong fluid-wall surface interactions, high surface to volume ratio, extremely low Reynolds number laminar flow, surface roughness and wall surface or zeta potential. Due to zeta potential, an electrical double layer (EDL) is formed in the vicinity of the wall surface, namely, the stern layer (layer of immobile ions) and diffuse layer (layer of mobile ions). Hence, its competent designs demand more efficient micro-scale mixing mechanisms. This paper aims to therefore carry out numerical investigations of electro osmotic flow and mixing in a constricted microchannel by modifying the existing immersed boundary method.

The numerical solution of electro-osmotic flow is obtained by linking Navier–Stokes equation with Poisson and Nernst–Planck equation for electric field and transportation of ion, respectively. Fluids with different concentrations enter the microchannel and its mixing along its way is simulated by solving the governing equation specified for the concentration field. Both the electro-osmotic effects and channel constriction constitute a hybrid mixing technique, a combination of passive and active methods. In microchannels, the chief factors affecting the mixing efficiency were studied efficiently from results obtained numerically.

The results indicate that the mixing efficiency is influenced with a change in zeta potential (ζ), number of triangular obstacles, EDL thickness (λ). Mixing efficiency decreases with an increment in external electric field strength (Ex), Peclet number (Pe) and Reynolds number (Re). Mixing efficiency is increased from 28.2 to 50.2% with an increase in the number of triangular obstacles from 1 to 5. As the value of Re and Pe is decreased, the overall percentage increase in the mixing efficiency is 56.4% for the case of a mixing micro-channel constricted with five triangular obstacles. It is also vivid that as the EDL overlaps in the micro-channel, the mixing efficiency is 52.7% for the given zeta potential, Re and Pe values. The findings of this study may be useful in biomedical, biotechnological, drug delivery applications, cooling of microchips and deoxyribonucleic acid hybridization.

The process of mixing in microchannels is widely studied due to its application in various microfluidic devices like micro electromechanical systems and lab-on-a-chip devices. Hence, its competent designs demand more efficient micro-scale mixing mechanisms. The present study carries out numerical investigations by modifying the existing immersed boundary method, on pressure-driven electro osmotic flow and mixing in a constricted microchannel using the varied number of triangular obstacles by using a modified immersed boundary method. In microchannels, the theory of EDL combined with pressure-driven flow elucidates the electro-osmotic flow.

This study aims to synthesize the existing literature with insights gained from interviews conducted with regulatory experts. The objective is to analyse the challenges associated with incorporating cryptocurrencies into regulatory frameworks and to explore constraints in the regulatory institutionalization of cryptocurrencies.

The study methodology consists of two steps. The first step is to identify regulatory constraints in the literature review and in the next step, interviews are conducted with officials of the State Bank of Pakistan (SBP). The study used a qualitative case study methodology, in which a single case (regulatory constraint) was selected as a unit of analysis.

The findings show that lack of traceability, legal status, lack of governmental control due to decentralization, difficulty enforcing laws, volatility, lack of skills with regulators and difficulty integrating cryptocurrencies into the current financial system are the main obstacles to the introduction of a regulatory framework. Thus, on a broader conceptual level, the findings can be grouped into opportunism, lack of strategic capability and fragmented global laws.

This study could inform global cryptocurrency regulation discussions, sharing a developing country’s views on balancing the government, central banks, the financial sector and public interests. This could guide countries to consider cryptocurrency adoption in similar situations. This could affect the cryptocurrency market, impacting demand, supply and investor trust in Pakistan.

The study has implications for policy making officials. The research aims to offer valuable insights to the SBP and other regulatory authorities, helping them identify potential risks and create an effective regulatory framework for cryptocurrencies.

The study has implications for society in knowing about the volatile nature of cryptos and anonymity of their issuers, which poses regulatory constraints. This then implies its harmfullness to its traders and the huge losses that may arise from their trading due to its volatile nature.

This study contributes to the literature on the constraints, responsibilities and consultation framework of cryptocurrency regulations.

At present, the adoption of cryptocurrency investment has brought consideration to the globe. The present paper attempts to investigate the intention to adopt cryptocurrency (IACR) among the potential investors of Pakistan.

The theory of planned behavior (TPB) is applied to underpin the conceptual framework. The study uses a quantitative approach. The study collects cross-sectional data through an online survey questionnaire. In the last, the authors utilized 334 samples for outcomes.

Findings of the SEM reveal a significant positive effect of attitude, subjective norms (SNs), perceived behavioral control (PBC) and trust on IACR.

The outcomes of an investigation would develop further intention and trust towards cryptocurrency adoption. The results would support developing favorable policies regarding the reduction of the ban on cryptocurrency in Pakistan to make easier transactions of the investors further. Possibly, it brings several opportunities in all segments of society in making the digital transaction modes through cryptocurrency. Finally, the findings would further validate the TPB in the context of cryptocurrency.

The study provides a better understanding of cryptocurrency and investors IACR. The empirical evidence further develops the other individuals' intentions towards cryptocurrency usage.

This research aims to examine the effect of Tier-1 capital, risk management, and profitability on performance of Indonesia commercial banks.

The research population consisted of all commercial banks listed in the Indonesia Stock Exchange periods of 2010 to 2014 with a total of 42 companies. The statistical analysis for testing the hypothesis using structural equation modeling (SEM) covariance based using WarpPLS.

Research result shows that Tier-1 capital has a positive effect on capital on risk management; risk management has a positive effect on performance, but risk management does not have an effect to profitability; profitability has a positive effect on performance; and Tier-1 capital has a negative effect on profitability. On the other hand, profitability has a negative effect on Tier-1 capital and performance has a positive effect on Tier-1 capital, whereas Tier-1 capital does not have an effect on performance.

The originality of this research can be seen from the causal relationship between the effects of Tier-1 capital, risk management and profitability on performance of commercial banks in the context of stock performance among Indonesia commercial banks. In addition, previous research findings remain inconsistent between one another. By conducting this research, it is expected that more consistent research findings than the previous ones can be generated. Sluggish global economic conditions which result in declined bank performance are an interesting topic to investigate.

This paper aims to examine the effects of Tier-1 capital toward risk management and profitability on the performance of Indonesian Commercial Banks.

The research population consisted of all commercial banks listed on the Indonesia Stock Exchange. The data were in the form of financial statements of commercial banks for the periods of 2012 to 2016 with a total of 42 companies (bank). From a total of 42 commercial banks listed in the Indonesia Stock Exchange, not all of them met the criteria. Commercial banks that meet these criteria are as many as 28 banks are sampled research.

Tier-1 capital has a positive direct effect on risk management, Tier-1 capital has a positive indirect effect on profitability with risk management as a mediation variable, risk management has a positive direct effect on profitability, Tier-1 capital has a positive indirect effect on performance with risk management and profitability as mediation variables, risk management has a positive indirect effect on performance with as mediation variable and profitability has a positive impact on performance.

The originality of this research can be seen from the causal relationship between the effects of Tier-1 capital, risk management and profitability on the performance of commercial banks in the context of stock performance among Indonesia commercial banks. Also, the analysis tools using multiple fixed effect panel data models in this research as a novelty in this research. In addition, previous research findings remain inconsistent with one another. By conducting this research, it is expected that more consistent research findings than the previous ones can be generated. Sluggish global economic conditions, which result in declined bank performance are an interesting topic to investigate. The paper uses an original sample, 28 Indonesian banks in 2012-2016. Also, it links Tier 1 capital with risk management and performance in a novel theoretical framework.

Co-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige of the co-cited articles. Despite its quantitative nature, it is found effective in retrieving and evaluating documents, signifying its linkage with the related documents' contents. To better understand the dynamism of the citation network, the present study aims to investigate various content features giving rise to the measure.

The present study examined the interaction of different co-citation features in explaining the co-citation frequency. The features include the co-cited works' similarities in their full-texts, Medical Subject Headings (MeSH) terms, co-citation proximity, opinions and co-citances. A test collection is built using the CITREC dataset. The data were analyzed using natural language processing (NLP) and opinion mining techniques. A linear model was developed to regress the objective and subjective content-based co-citation measures against the natural log of the co-citation frequency.

The dimensions of co-citation similarity, either subjective or objective, play significant roles in predicting co-citation frequency. The model can predict about half of the co-citation variance. The interaction of co-opinionatedness and non-co-opinionatedness is the strongest factor in the model.

It is the first study in revealing that both the objective and subjective similarities could significantly predict the co-citation frequency. The findings re-confirm the citation analysis assumption claiming the connection between the cognitive layers of cited documents and citation measures in general and the co-citation frequency in particular.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.

Customer relationships and transforming customers into evangelists are imperative in today's world. Therefore, this study aimed to examine the influence of online brand experience (OBE) on brand evangelism (BEM) via relationship quality (trust-TRT, satisfaction-SAT, and commitment-CMT) by integrating the moderating effects of brand reputation (BR), particularly in the Vietnamese banking sector.

This research obtained data from 486 Vietnamese customers who routinely used online banking services. The analysis was performed using structural equation modeling.

The findings demonstrate that OBE directly/indirectly positively affects BEM via relationship quality (TRT, SAT, CMT). Likewise, this study identified relationship quality (TRT, SAT, CMT) as an important mediator. Finally, the findings demonstrate that the moderation effects of BR significantly improved relationship quality (TRT, SAT, CMT) in the banking industry.

This study showcases the significance of OBE in increasing brand evangelists in the financial sector. Thus, this study assists Vietnamese bank managers in creating new branding strategies to foster long-lasting customer relationships.

This original study contributes to the commitment-trust theory and signaling theory by examining the impact of the OBE on brand evangelism via relationship quality by considering the moderating effect of brand reputation in the Vietnamese banking sector.

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.