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This study investigates how performance pressure affects feedback-seeking and innovative work behaviors. The study also examines the effect of extraversion on the performance pressure–FSB relationship.

The hypotheses in this study were tested by analyzing two-wave data collected from a sample of employees in the information technology sector in India using the PLS-SEM approach.

Our findings revealed that individuals possessing extraverted personality traits exhibited a positive response to performance pressure, thereby enhancing their FSB. Moreover, our results demonstrated that FSB mediates the relationship between performance pressure and IWB.

The results underscore the importance of individual variations in personality traits, particularly extraversion, in influencing how employees respond to performance pressure. By providing insights into the mediating mechanism of feedback-seeking behavior, our study contributes to a deeper understanding of the interplay between performance pressure, feedback-seeking behavior and innovative work behavior.

Managers should consider extraversion as a factor in the relationship between performance pressure and FSB, adapting strategies and support systems accordingly. Creating a feedback-oriented culture and providing resources for extroverts during high-pressure periods can enhance their coping mechanisms.

Previous research has provided a limited exploration of the mechanisms that establish the connection between job demands and innovative work behaviors. This study contributes by uncovering the previously unexplored relationship between performance pressure, extraversion, feedback-seeking behavior and, subsequently, innovative work behavior.

Green HRM practices in the hospitality sector are now receiving growing interest. However, the extent to which these practices contribute towards employee non-green workplace outcomes remains largely unknown. This study explores the relationships among green HRM practices, happiness at work, employee resilience, and feedback-seeking behaviour.

The study employs two-wave data from a sample of 306 five-star hotel employees in India. Using partial least square-structural equation modelling, the relationships are tested.

The study’s results demonstrate that green HRM practices positively impact happiness at work, employee resilience, and feedback-seeking behaviour. Additionally, the relationship between green HRM practices and feedback-seeking behaviour and employee resilience is mediated by happiness at work.

Drawing on the Job Demands-Resources Theory, Social Exchange Theory, and Broaden and Build theory, this paper proposes that green HRM practices can contribute to happiness at work, employee resilience, and feedback-seeking behaviour.

To establish a positive connection between green HRM practices and employee outcomes, organizations must recognize the vital role played by happiness at work as a mediator. This means that organizations must implement green HRM practices and ensure their positive impact on employee happiness at work.

The originality of this research lies in its holistic approach to green HRM outcomes, suggesting that the benefits of these practices extend beyond environmental impacts to influence the psychological and behavioural dimensions of employees.

The purpose of this paper is to understand the role of entrepreneurial intention in promoting women entrepreneurship in Indian micro, small and medium enterprises (MSMEs). This study seeks to clarify the construct of entrepreneurial intention and then reports the validation of the entrepreneurial intention instrument.

An instrument has been designed and administered on a sample of 103 respondents across India from women entrepreneurs to understand the entrepreneurial intention by using cluster and snowball sampling. The data has been streamlined and then analyzed using descriptive analysis for validity and reliability checks.

This research was aimed to determine the constructs of entrepreneurial intention. Through data analysis, it has been observed that the reliability coefficients reveal the adequacy of the sample. The Cronbach’s alpha values for all the items in the instrument were found to be greater than or equal to 0.6. Strong correlations were also found between direct and indirect measures of entrepreneurial intention and hence confirmed that all the measures in the instrument were well constructed. Analysis has also explained the relationship between various constructs of entrepreneurial intention by using Pearson’s correlation coefficients. Strong and positive values of correlation explain the existence of the convergent and discriminant validity of the instrument.

The research results obtained from the analysis of reliability and validity tests not only provides the establishment of the relationship among the various constructs but also suggests that the model provides a promising potential to measure entrepreneurial intention. This study will contribute to new knowledge of the conditions of women entrepreneurship from different perspectives by developing and validating an analytic model for promoting the women entrepreneurship in MSMEs of India.

From a government perspective, this model will help in designing training programmes for promoting women entrepreneurship in India. The obtained result also brings significant implications for practice as well as raises a broad future direction for other researchers

Extended SCCT model has recently suggested an inclusive framework of factors affecting the entrepreneurial intention, there is not much attempt made in research using this theory as background for predicting intention in the context of women entrepreneurship. This paper attempts to fill this gap by formulating a conceptual model for measuring entrepreneurial intention among women entrepreneurs by integrating and adapting the constructs of extended social cognitive career theory model and entrepreneurial potential model.

The purpose of this study was to map the quantity (frequency), quality (impact) and structural indicators (correlations) of research produced on cloud computing in 48 countries and 3 territories in the Asia continent.

To achieve the objectives of the study and scientifically map the indicators, data were extracted from the Scopus database. The extracted bibliographic data was first cleaned properly using Endnote and then analyzed using Biblioshiny and VosViewer application software. In the software, calculations include citations count; h, g and m indexes; Bradford's and Lotka's laws; and other scientific mappings.

Results of the study indicate that China remained the most productive, impactful and collaborative country in Asia. All the top 20 impactful authors were also from China. The other most researched areas associated with cloud computing were revealed to be mobile cloud computing and data security in clouds. The most prominent journal currently publishing research studies on cloud computing was “Advances in Intelligent Systems and Computing.”

The study is the first of its kind which identified the quantity (frequencies), quality (impact) and structural indicators (correlations) of Asian (48 countries and 3 territories) research productivity on cloud computing. The results are of great importance for researchers and countries interested in further exploring, publishing and increasing cross country collaborations related to the phenomenon of cloud computing.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

This scientific article aims to evaluate the efficacy of the drug Doxorubicin for treating hepatocellular carcinoma (HCC) in Egypt. The study analyzes data from patients referred to a multi-disciplinary consultation at the National Cancer Institute, Cairo University. The study includes 40 intermediate-stage HCC patients who underwent treatment with either Doxorubicin-Lipiodol or Doxorubicin-loaded drug-eluting beads-trans-arterial chemoembolization (DEB-TACE).

Patients referred to a multi-disciplinary consultation at the National Cancer Institute, Cairo University with a possible diagnosis of HCC in the intermediate stage were eligible for the study.

The study finds that the plasma peak concentration of Doxorubicin is significantly higher in patients treated with Lipiodol compared to those treated with DEB-TACE. The median plasma peak concentration of patients treated with Lipiodol was significantly higher 424 (202.5–731) than the peak level of patients treated with beads 84.95 (26.6–156.5) with p-value = 0.036. However, there is no significant difference in other pharmacokinetic parameters between the two treatment groups. The research article also investigates the genetic polymorphisms in HCC patients treated with Doxorubicin-Lipiodol and Doxorubicin-loaded DEB-TACE. It identifies a significant association between the ABCB1 gene (C3435T) and the concentration of Doxorubicin in plasma. Patients with the CCand computed tomography (CT) genotypes of ABCB1 have higher concentrations of Doxorubicin compared to those with the TT genotype. Furthermore, the study examines the progression-free survival rates and tumour response in the two treatment groups. It demonstrates that DEB-TACE patients have a higher progression-free survival rate compared to cTACE patients. DEB-TACE also leads to better tumour regression.

The current study helps to increase the understanding of the genetic factors that may contribute to HCC susceptibility in the Egyptian population. However, it is essential to consider that genetic polymorphism is just one aspect of HCC risk, and other factors such as environment, lifestyle and viral infections also play crucial roles. Further research is needed to elucidate the complex interactions between genetic and environmental factors in HCC development among Egyptians.

In keeping with global developments rendering online shopping as an emerging trend among consumers, the present study extends the unified theory of use and acceptance of technology (UTAUT2) comprising the digital payment mode (DPM) as a new driver of online shopping and with the mediation of attitudes toward technology (ATTs) to gauge a better and deeper understanding of behavioral intention (BI).

This study used a survey instrument with snowball sampling from 600 consumers in northern India. Partial least squares structural equation modeling was used to find the association between drivers using UTUAT2, along with DPM and ATTs. The data were divided into a test group (20%) and validated through a training group (80%).

DPM was shown to be directly associated with BI. The mediation of ATTs was also validated through the model. The predictability of the model was 67.5% for the test group (20%) and 69.6% for the training group (80%). The results also indicated that facilitating conditions is a critical driver of BI.

This study enhances the understanding of the roles that DPM and ATTs play in BI during online shopping, suggesting that Indian managers need to adopt DPM as a support service to make online shopping a worthwhile experience.

The purpose of this paper is twofold: first, to present a bibliometric analysis and systematic literature review of industry convergence and value innovation to understand the current research status; second, to provide a coherent theoretical research framework for future research.

This study adopts a two-step analysis approach by combining bibliometric analysis and systematic literature review to explore the research topic of industry convergence and value innovation. Besides, two bibliometric tools, HistCite and VOSviewer, were applied to this study.

This study found that Stefanie Bröring and Fredrik Hacklin are the top two most influential authors among all authors in the sample publications. Technological Forecasting and Social Change is one of the top-ranking journal that often publishes this topic of articles. Germany and the University of Munster are the most influential country and institutions, respectively. Besides, five core research themes were identified based on keywords co-occurrence map, theoretical lenses, factors promoting industry convergence, indicators of industry convergence, the impact of industry convergence and emerging research directions. Based on the above analysis, this paper constructed a theoretical research framework of industry convergence and value innovation.

This paper only draw data from one database – Web of Science – which cannot provide broad coverage of the research topic. Besides, the bibliometric method of this paper is based on high local citation score and high-frequency words, articles in the skirting subjects’ area may not be analyzed.

With the rapid development of technology, such as nanotechnology, radio - frequency identification (RFID), etc., the iterative upgrading of products also comes. As a result, the boundary between industries is gradually blurred, and the phenomenon of industry convergence appears. Therefore, managerial decision-makers are facing challenges of how to respond to the convergence phenomena. From the firm level, firms are facing the problem of value innovation of the existing product, new product development and core competence improvement. Industries are facing the problem of transformation and upgrading. This paper provides certain theoretical insights for both firms and industries to guide the practice accordingly.

This paper is the first to use a bibliometric method to examine the topic of industry convergence and value innovation. In addition, this paper presents an in-depth analysis of this topic and provides a comprehensive theoretical research framework for future study.

This study aims to investigate consumer behaviour towards online shopping, which further examines various factors limiting consumers for online shopping behaviour. The purpose of the research was to find out the problems that consumers face during their shopping through online stores.

A quantitative research method was adopted for this research in which a survey was conducted among the users of online shopping sites.

As per the results total six factors came out from the study that restrains consumers to buy from online sites – fear of bank transaction and faith, traditional shopping more convenient than online shopping, reputation and services provided, experience, insecurity and insufficient product information and lack of trust.

This study is beneficial for e-tailers involved in e-commerce activities that may be customer-to-customer or customer-to-the business. Managerial implications are suggested for improving marketing strategies for generating consumer trust in online shopping.

In contrast to previous research, this study aims to focus on identifying those factors that restrict consumers from online shopping.

The innovation in big data is increasing day by day in such a way that the conventional software tools face several problems in managing the big data. Moreover, the occurrence of the imbalance data in the massive data sets is a major constraint to the research industry.

The purpose of the paper is to introduce a big data classification technique using the MapReduce framework based on an optimization algorithm. The big data classification is enabled using the MapReduce framework, which utilizes the proposed optimization algorithm, named chicken-based bacterial foraging (CBF) algorithm. The proposed algorithm is generated by integrating the bacterial foraging optimization (BFO) algorithm with the cat swarm optimization (CSO) algorithm. The proposed model executes the process in two stages, namely, training and testing phases. In the training phase, the big data that is produced from different distributed sources is subjected to parallel processing using the mappers in the mapper phase, which perform the preprocessing and feature selection based on the proposed CBF algorithm. The preprocessing step eliminates the redundant and inconsistent data, whereas the feature section step is done on the preprocessed data for extracting the significant features from the data, to provide improved classification accuracy. The selected features are fed into the reducer for data classification using the deep belief network (DBN) classifier, which is trained using the proposed CBF algorithm such that the data are classified into various classes, and finally, at the end of the training process, the individual reducers present the trained models. Thus, the incremental data are handled effectively based on the training model in the training phase. In the testing phase, the incremental data are taken and split into different subsets and fed into the different mappers for the classification. Each mapper contains a trained model which is obtained from the training phase. The trained model is utilized for classifying the incremental data. After classification, the output obtained from each mapper is fused and fed into the reducer for the classification.

The maximum accuracy and Jaccard coefficient are obtained using the epileptic seizure recognition database. The proposed CBF-DBN produces a maximal accuracy value of 91.129%, whereas the accuracy values of the existing neural network (NN), DBN, naive Bayes classifier-term frequency–inverse document frequency (NBC-TFIDF) are 82.894%, 86.184% and 86.512%, respectively. The Jaccard coefficient of the proposed CBF-DBN produces a maximal Jaccard coefficient value of 88.928%, whereas the Jaccard coefficient values of the existing NN, DBN, NBC-TFIDF are 75.891%, 79.850% and 81.103%, respectively.

In this paper, a big data classification method is proposed for categorizing massive data sets for meeting the constraints of huge data. The big data classification is performed on the MapReduce framework based on training and testing phases in such a way that the data are handled in parallel at the same time. In the training phase, the big data is obtained and partitioned into different subsets of data and fed into the mapper. In the mapper, the features extraction step is performed for extracting the significant features. The obtained features are subjected to the reducers for classifying the data using the obtained features. The DBN classifier is utilized for the classification wherein the DBN is trained using the proposed CBF algorithm. The trained model is obtained as an output after the classification. In the testing phase, the incremental data are considered for the classification. New data are first split into subsets and fed into the mapper for classification. The trained models obtained from the training phase are used for the classification. The classified results from each mapper are fused and fed into the reducer for the classification of big data.