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The purpose of this study is to examine the simultaneous effects of Hafnium particles and partially submerged metallic particles for the flow of bi-phase coupled stress fluid over an inclined flat plane.

An unflinching free stream flow that stretches far from the surface of the plane with the possibility of containing some partially submerged metallic particles is considered. Innovative model has been proposed and designed using Runge–Kutta–Fehlberg method.

The findings show that the drag force resists the couple stress fluid, whereas the Newtonian flow is supported by increasing the velocity. For both types of flows, movement of the particle is retarded gradually against the drag force coefficient.

To the best of the authors’ knowledge, this model is reported for the first time.

This paper aims to investigate the tacit knowledge-sharing framework among Pakistani academicians. The objective is to study trust and social networks as antecedents to foster tacit knowledge sharing with the mediating role of commitment. Furthermore, the moderating role of organizational knowledge-sharing culture is also examined.

The study applied a survey-based quantitative research design to test the proposed model. The nature of data are cross-sectional and collected with stratified random sampling among public sector higher education professionals of Pakistan. The total sample size for the present research is 247 respondents. The variance-based structural equation modeling technique by using Smart_PLS software is used for analysis.

Data analysis and results reveal that trust and social networks are significant predictors of tacit knowledge sharing among Pakistani academicians while commitment positively mediated the relationships. While the moderating role of organizational knowledge-sharing culture is also established.

The current research explains tacit knowledge sharing among academics with fewer antecedents i.e. social network and trust with limited sample size and specific population. There is still a great deal of work to be done in this area. Hence, the study provides direction for including knowledge-oriented leadership and knowledge governance in the current framework. Moreover, the framework can be tested in different work settings for better generalization.

The study gives an important lead to practitioners for enhancing tacit knowledge sharing at the workplace through a robust social network of employees, building trust and boosting employees’ commitment, as well as through supportive organizational knowledge sharing culture.

The research comprehends the tacit knowledge sharing framework with theoretical arrangements of trust, social networks, commitment and culture in higher education workplace settings under the umbrella of social capital theory.

This study aims to investigate the outcomes of customers’ co-creation experience in a realistic and routinely performed co-creation setting, a restaurant. To fulfill this purpose, the current study links the branding literature to hospitality research and offers a novel framework by incorporating customers’ co-creation experience, customer brand engagement, emotional brand attachment and customer satisfaction in an integrated research model.

Data were collected from 421 diners at Chinese hotpot restaurants via a self-administered questionnaire. The reliability and convergent and discriminant validities were established through confirmatory factor analysis, and then hypotheses were tested through structural equation modeling.

This study demonstrates that customers’ co-creation experience with a restaurant brand positively impacts customer brand engagement, emotional brand attachment and customer satisfaction. In addition, current study examines these relational paths at the dimensional level by taking the co-creation experience and customer brand engagement as multidimensional constructs. The resulting in-depth investigation reveals that the hedonic, social and economic experience dimensions of co-creation experience positively influence customer satisfaction, emotional brand attachment and customer brand engagement’s buying, referring, influencing and feedback dimensions.

This study helps relationship and brand managers better understand customer experience in co-creation settings and paves the way for managers to devise engagement strategies.

The current study marks an initial attempt to delineate the outcomes of customers’ co-creation experience in a realistic co-creation setting. Furthermore, the study is first of its kind that investigates the relationship of co-creation experience and customer brand engagement at the dimensional level.

Drawing upon the theoretical underpinning of knowledge worker productivity, this study aims to examine the relationship between abusive supervision and knowledge management (KM) process (creation, application and sharing of knowledge) and its impact on the knowledge worker productivity in knowledge-intensive organizations.

Hypothesis were tested through PROCESS Macro in IBM SPSS v.26 on a sample of 204 employees working in banking sector of Pakistan. Confirmatory factor analysis was conducted to test the model fitness through AMOS v. 26.

The results showed that the relationship between abusive supervision and KM process (creation, application and sharing of knowledge) is negative and highly significant, i.e. greater the abusive supervision in the banking sector, the lower is the engagement in KM processes. Furthermore, there is a positive and highly significant relationship between the KM process and knowledge worker productivity. Finally, the study indicates the negative impact of abusive supervision on the knowledge worker productivity through the mediating mechanism of knowledge management processes.

A key limitation is that the study is cross-sectional, and the findings may only be generalizable to developing countries context.

Previous studies have focused on supervisor–employee relationship but not in the context of knowledge worker productivity. This article fulfills this gap through understanding the impact of abusive supervision on the knowledge worker productivity in relation to KM processes (knowledge creation, sharing and application) by drawing upon the theoretical underpinning of knowledge worker productivity.

The purpose of this paper is twofold: first, to estimate the water demand elasticities using a parametric linear programming (LP) model to provide an insight into the accurate and flexible pricing policy of irrigation water in the Jordan Valley; and second, to highlight key risk aspects, related to water demand, which are likely to impact the community.

A parametric LP model was used in this research. Primary and secondary data were collected.

Results revealed that the demand elasticity is high in Spring and Summer than in Fall and Winter, meaning that during Spring and Summer farmers are willing to forgo larger amounts of water than in other months. This is because of areas planted during Spring seasons are much less than those of Autumn and Winter.

The Jordan Valley suffers from water scarcity risk, and consequently the area to be planted is not fully utilized, leading to lower cropping intensities. Responsible authorities in Jordan need to address these issues and propose proper solutions in order to reduce further escalation of this risk and subsequent impact on local communities. Insight into the value of water demand elasticities is essential to support and mitigate policy decision making under risk conditions, concerning investments in water supply systems; investments in the water distribution and irrigation systems; efficient allocation of water with competing sectors; setting water pricing and tariffs; setting cost recovery mechanisms, and the risks encountered under lack of mitigated policy decision making.

This is one of few studies that addresses in detail using a parametric LP model the issue of water scarcity, related risks and subsequent impact on society in Jordan. It is expected to help policy and decision makers better formulate future estimates and demand which subsequently reduce related risks.

This study aims to investigate the effect of healthy food advertising (HFA) in preventing obesity (measured using the healthy eating attitude and perceived self-regulatory success) through the meta-cognitive role of consumer wisdom (CW). The meta-cognitive role of CW to better promote healthy eating attitude and behavior is relevant and underexplored.

Data were collected from 310 young consumers through an online survey. Reliability and validity were established using confirmatory factor analysis, and hypotheses were analyzed through structural equation modeling using MPlus V8.3.

The results reveal that HFA has a positive influence on all dimensions of CW: responsibility, purpose, perspective, reasoning and sustainability. All dimensions but one augment a positive healthy eating attitude, but only responsibility and sustainability enhance consumers’ self-regulatory success. The findings show that HFA does not directly prevent obesity, but CW mediates the relationship between that advertising and obesity prevention. These findings show that CW establishes a mindful connection between HFA and obesity control.

This research extends the theory of CW in the context of healthful eating and contributes significantly to the advertising, hospitality and obesity literature.

This study also has implications for multiple stakeholders, including consumers, restaurant operators, hospitality managers, brand managers, the government and society in general.

To the best of the authors’ knowledge, this study marks the first attempt to investigate the role of CW in preventing obesity. It is also the first study to examine the relationships of HFA with CW and a healthful attitude toward eating.

The purpose of this paper is to assess the level of digital information literacy (DIL) skill and use of electronic resources by humanities graduate students at Kenneth Dike Library, University of Ibadan, Nigeria.

This paper uses the survey research design and 200 graduate students from the 12 departments that made up the Faculty of Arts in the University of Ibadan participated in the study. A self-devised structured questionnaire was used as instrument for data collection. A pilot questionnaire was first sent to a small random sample of the respondents, with feedback used to fine-tune the final questionnaire. Respondents were requested to rate their level of proficiency in the use of digital devices, web-based tasks, information finding, evaluation and utilisation of available e-resources and challenges encountered. Ethical consideration of informed consent, institutional permission, confidentiality and anonymity of participants was strictly followed. Data collected were analysed and result presented using descriptive statistics including frequencies, percentage, mean and inferential statistics such as regression analysis and Pearson’s product moment correlation coefficient were used to test the research question and hypothesis, respectively.

Humanities graduate students at the University of Ibadan possessed high level of DIL skills in respect of digital devices usage, web-based tasks, information finding and evaluation, but low in e-resources utilisation. This study identified inadequate knowledge of e-resources availability, irregular internet access, inadequate training on e-resources utilisation, inadequate staff assistance, lack of continuity in e-resources subscription and paucity of local contents in the e-resources as main challenges encountered by graduate students in the use of e-resources. To ensure that those who can most benefit from e-resources utilisation are not further marginalised, this study recommends that active steps should be taken to increase e-resources awareness, regular internet access, training/support, continuity of e-resources subscription and increased local content so that all may benefit from the opportunities of the information age.

This paper has demonstrated that DIL skills can enhance effective utilisation of e-resources if users have adequate knowledge of e-resources availability, regular internet access, adequate training and assistance on e-resources utilisation, continuity in database subscription and adequate local contents e-resources.

The productivity of knowledge workers is crucial not only for organizational innovation and competitiveness but also for sustainable development. In the context of knowledge-intensive firms, implementation of knowledge management is likely to increase knowledge worker productivity. Therefore, the purpose of this paper is to examine the influence of knowledge management on knowledge worker productivity.

A research framework on the effects of knowledge management processes on knowledge worker productivity is established and empirically tested with data from 336 knowledge workers at five mobile network operator companies in Pakistan.

The results indicate that knowledge creation and knowledge utilization impact knowledge worker productivity positively and statistically significantly. However, knowledge sharing does not have statistically significant impact on knowledge worker productivity. Demographic factors (gender, managerial position and formal education level) do not moderate the relationship between knowledge management and knowledge worker productivity statistically significantly.

The key limitations are the cross-sectional nature of the data and the geographic limitation to telecom companies in Pakistan.

Irrespective of gender, education and managerial position, implementation of knowledge management can increase knowledge worker productivity. Therefore, knowledge management practices should be implemented to enhance the knowledge worker productivity via fostering the knowledge worker’s engagement in and propensity to knowledge management processes.

This study is among the first to examine the likely influence of knowledge management on the productivity of knowledge workers conclusively while controlling for three individual demographic factors. This study also addresses the effectiveness of knowledge management in the little-explored cultural context of Pakistan.

The purpose of this paper is to analyze the historical gas allocation pattern for seeking appropriate arrangement and utilization of potentially insufficient natural gas supply available in Pakistan up to 2030.

This study presents Markov chain-based modeling of historical gas allocation data followed by its validation through error evaluation. Structural prediction using classical Chapman–Kolmogorov method and varying-order polynomial regression in the historical transition matrices are presented.

Markov chain model reproduces the terminal state vector with 99.8 per cent accuracy, thus demonstrating its validity for capturing the history. Lower order polynomial regression results in better structural prediction compared with higher order ones in terms of closeness with Markov approach-based prediction.

The data belongs to a certain geographic region with specific gas demand and supply profile. The proposition may be tested further by researchers to check the validity for other comparable structural predictions/analyses.

This study can facilitate policy-making in the field of natural gas allocation and management in Pakistan specifically and other comparable countries generally.

Two major literature gaps filled through this study are: first, Markov chain model becomes stationary when projected using Chapman–Kolmogorov relation in terms of a fixed, average transition matrix resulting in an equilibrium state after a finite number of future steps. Second, most of the previous studies analyze various gas consumption sectors individually, thus lacking integrated gas allocation policy.

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.

The methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).

Machine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.

All machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.

The study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.

The research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.