Forthcoming and Online First Articles

International Journal of Intelligent Enterprise

International Journal of Intelligent Enterprise (IJIE)

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International Journal of Intelligent Enterprise (7 papers in press)

Regular Issues

  • Factor exploration for creating an agile workforce: an Indian experience   Order a copy of this article
    by Sharada Prasad Sahoo, Biswajit Prasad Chhatoi, Suman Kalyan Chaudhury 
    Abstract: In this paper, an attempt has been made to explore the factors that describe the concept of an agile workforce. By analysing the responses obtained through a structured questionnaire from 318 managers from cross-functional industries, the factor exploration was made by deploying exploratory factor analysis. The findings of the study reveal that the five factors of agile strategy, structural agility, agile process, agile capability and agile technology were found significant. It is found that the influence of technology on workforce agility has been perceived differently concerning the degree of experience possessed by professionals. The respondents' perception of structure as a contributor to agility is influenced by their level of educational attainment. Similarly, the impact of technology on agility has been influenced by the experience possessed by the executives.
    Keywords: workforce agility; innovation; competitive advantage; factor analysis.
    DOI: 10.1504/IJIE.2024.10064082
  • A study on perceptions of students on intelligent virtual learning environment during and post COVID-19   Order a copy of this article
    by S. Santha Kumari, M. Nagalakshmi, Srinivasa Rao Angati 
    Abstract: Most nations implemented an emergency lockdown during the COVID-19 epidemic in 2020, which dramatically impacted all aspects of employment and social contact for everyone. During the pandemic, almost all physical classes are cancelled and started through virtual mode in all educational institutions. Along with other fields, education faced many difficulties due to this COVID-19. This paper's key objective is to know various college students' perceptions about studying through virtual mode. The learning and teaching communities valued students' viewpoints, and the questions in the online survey were meant to aid in understanding students' learning experiences and perceptions of the online delivery form. To know students' opinions, the researcher conducted a small survey through a semi-structured questionnaire made with Google Forms. Students and most faculties were new to this virtual mode and needed some training. In this survey, the opinions are collected from 230 students and used statistical tools for analysis. Analytical papers investigate all questions and develop inferences from the outcomes. The literature has tried to understand students' views of virtual learning and identify success factors.
    Keywords: post COVID-19; e-learning; electronic learning; internet; online platforms; perceptions; students; virtual learning; intelligent virtual learning environment; education landscape; teaching communities.
    DOI: 10.1504/IJIE.2024.10061833
  • Optimised hybrid CNN bi-LSTM model for stock price forecasting   Order a copy of this article
    by Deepti Patnaik, N.V. Jagannadha Rao, Brajabandhu Padhiari, Srikanta Patnaik 
    Abstract: Financial markets are considered the backbone of a country's economy. This article focuses on the stock price forecasting using deep learning models. Here, a hybrid model, i.e., convolutional neural network, bidirectional long short-term memory network has been proposed and its parameters are optimised by self-adaptive multi-population elitist JAYA algorithm. Stock prices of more than 13 years of various challenging stock exchanges of the globe such as: Standard & Poor 500, NIFTY 50, Nikkei 225, Dow Jones are used here for analysis purposes. The performance parameters such as root mean square error, mean absolute percentage error and mean absolute error are used for analysing the model. The proposed hybrid model is also compared with state-of-art models and it is found that this proposed model out performs the existing models.
    Keywords: forecasting; convolutional neural network; bidirectional long short-term memory; LSTM; hybrid model; evolutionary computation; SAMPE Jaya algorithm; RMSE; MAE.
    DOI: 10.1504/IJIE.2024.10061912
  • Selection of performance parameter for optimisation of availability of industrial system using particle swarm optimisation   Order a copy of this article
    by Ajay Kumar, Devender Singh Punia 
    Abstract: Today automation has become the necessity of a highly reliable industrial system which leads to the use of highly complex systems. In the present paper the performance modelling of complex multi-state system of industrial system for optimisation of availability is analysed using particle swarm optimisation (PSO). The steady state analysis (SSA) and transient state analysis (TSA) is done using Markov mathematical modelling. The decision support system for the considered system for repair priority of subsystem is presented for increasing the availability of system. The selection of various failure and repair rate for maximisation of availability is done using PSO. It is observed that the results obtained by PSO are beneficial for deciding the optimised failure rate as well as repair rate for maximum availability of system thus the increase in availability and decrease in failure rates decrease the overall cost of production.
    Keywords: availability; particle swarm optimisation; PSO; maintenance; decision support system; failure rate; repair rate; steady state analysis; SSA; transient state analysis; TSA.
    DOI: 10.1504/IJIE.2024.10064080
  • Comparison: Taguchi method and full factorial design in WACC   Order a copy of this article
    by Amir Ahmad Dar, Zameer Gulzar, Ramesh A. Babu, Layak Ali, G. Shreedevi 
    Abstract: To examine the effectiveness of two different experimental design techniques, Taguchi and full factorial design (FFD), in optimising the weighted average cost of capital (WACC). Several input variables, including total equity, total debt, tax rates, cost of debt, and cost of equity, have an impact on WACC. To set up trials and determine the optimal combination of these input elements for WACC, the Taguchi methodology was used. The Taguchi method is being applied for the first time to find the optimal combination for the cost of capital. The study uses the Taguchi L16 DOE, regression analysis, and the analysis of means (ANOM), all of which are carried out using the MINITAB 18 Software, to examine the effects of various input components. The experiment's results show that the Taguchi method and the FFD produce comparable performance results.
    Keywords: Taguchi method; full factorial design; weighted average cost of capital; WACC; analysis of means; ANOM; regression.
    DOI: 10.1504/IJIE.2024.10064079

Special Issue on: Digital Technologies for Enterprise Transformation

  • Advancing sustainable e-waste management systems integrated with artificial intelligence in developing countries   Order a copy of this article
    by Varun Kumar, Om Ji Shukla 
    Abstract: Industry 5.0 represents a paradigm shift in manufacturing and industrial processes, fostering collaboration between humans, robots, and smart devices. This evolution leverages advanced technologies such as artificial intelligence to boost productivity. In the context of e-waste management in developing countries, this study addresses the various influencing factors as drivers for enhancing the efficiency. These drivers were refined through an inter-consistency test and categorised using Fuzzy-DEMATEL. Notably, public awareness and technological advancement emerged as the most influential factors in integrating AI for sustainable e-waste management. Among these drivers, training and empowerment was found to be the most effective in driving positive change, while government policy and regulation played a pivotal role as the primary causal driver. This research offers valuable insights that can empower policymakers and environmental agencies to facilitate the adoption of AI and promote cleaner and more efficient e-waste management in developing countries.
    Keywords: artificial intelligence; Cronbach’s alpha; DEMATEL; e-waste management; influencing factors; sensitivity analysis.
    DOI: 10.1504/IJIE.2024.10063448
  • Artificial intelligence in higher education: the challenges, opportunities and the road ahead   Order a copy of this article
    by Maureen Primrose Lal, Ramji Nagariya, Man Mohan Siddh 
    Abstract: This paper investigates to deliver an overview of literature from 2012 to 2023 on the phenomena of implementing artificial intelligence in education (AIEd). With the help of the Scopus indexing database, data from 441 articles were extracted, analysed based on the keywords and preliminary reading and synthesised according to explicit inclusion and exclusion criteria and article compilation was on the parameters of scientific procedures and rationales for systematic literature review protocol (SPAR4SLR). Drawing on the recent literature depicts that the inception of artificial intelligence in education is still in its initial stage and much research is required. This article implies that although there are benefits and challenges talked about in the article delving into the application of AIEd in higher education’s system of teaching and learning that shall lead the education system to newfound intelligence and automation, however, things are at the very initial stage and filled with conjectures. The findings demonstrate that the artificial intelligence-based teaching and learning phenomenon has a bright future as educational institutes understand its upcoming impact. The greatest challenge for educational institutes now is to start planning, designing, developing and implementing artificial intelligence-based courses for multidisciplinary and holistic training for future employees.
    Keywords: artificial intelligence; higher education; education; systematic literature review protocol; SPAR4SLR; artificial intelligence in education; AIEd.
    DOI: 10.1504/IJIE.2024.10063894