International Journal of Intelligent Enterprise (12 papers in press)

Regular Issues

• Mixed method analysis on teacher readiness regarding technology  
  by S. Shruthi, B.R. Aravind 
  Abstract: Poetry and prose are important components of many literary curriculums, yet many aspiring pre-service language teachers find it difficult to teach these genres. Pre-service language instructors can easily get ready with prose and poetry with the use of technology designed for teaching prose and poetry. There is a gap in English prose and poetry preparatory technology, which needs novel solutions to prepare through technology. The purpose of this study is to find out the possibility that technology is necessary for future language instructors to effectively prepare prose and poetry. This study employs a mixed method, combining qualitative and quantitative research techniques. Ninety-three pre-service teachers from the Virudhunagar district under 1922 years old participated in this study. Prose preparation requires more technology than poetry preparation for pre-service language teachers. A comprehensive analysis of pre-service language instructors preparatory technology, assessment, and lesson planning techniques makes the study important. Designing technology for pre-service teachers to prepare prose and poetry improves English language acquisition. Future research could create and develop prose and poetry instruction technologies to save teachers time and provide them with new ideas to engage students.
  Keywords: mixed method; pre-service teachers; PST; teacher readiness technology; language acquisition; technological needs; student’s proficiency.
  
  
• Examining the field of protean career and unfolding future research directions through bibliometric and content analysis  
  by Durga Prasad Nayak, Sharada Prasad Sahoo, Shilpa Jain 
  Abstract: Protean career orientation (PCO) as a career philosophy has gathered scholarly attention in the last three decades, both from industry and academia. This study delves deep into protean career orientations through a knowledge mapping exercise. The study included 288 articles published between 1990 and 2023, obtained from Scopus and the Web of Science database for the review process. A four-stage analysis, i.e., descriptive, bibliometric, network, and content analysis, has been conducted to portray the research trend, intellectual structure, and scholarship pattern. The content analysis revealed four major clusters: conceptual dynamics, underlying dynamics, protean career mechanism, and career outcomes. We have diagnosed major constraints that inhibit the PCO and furnished future research directions. Our findings have immense managerial implications for crafting human resource strategies to identify and manage employees with PCO. This study also helps frame policies on entrepreneurship and promotes achievement motivation among individuals with a protean career focus.
  Keywords: protean career; bibliometric analysis; Web of Science; Scopus; citation analysis; content analysis.
  
  

Special Issue on: Digital Technologies for Enterprise Transformation

• Advancing sustainable e-waste management systems integrated with artificial intelligence in developing countries  
  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  
  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
   
  
• Leveraging social media to mitigate information asymmetry during humanitarian relief operations  
  by Deepak Srivastav, Anand Gurumurthy 
  Abstract: This study investigates social media’s role in mitigating information asymmetry during humanitarian relief operations (HROs). The 2015 Chennai Flood is used as a case study, and social media analytics (SMA) is utilised to understand how social media can be leveraged to engage people with high social capital for rapid and accurate information diffusion. It uses well-established techniques for SMA, such as topic modelling, sentiment analysis, etc., to gain critical insights from the text corpus obtained from Twitter (now called X). The results show that affected people in a disaster use social media to communicate their concerns/needs to the government or humanitarian organisations (HO) through celebrities and media, which is a unique finding. Moreover, this study shows that organisations serving disaster-affected populations, such as HO and the government, can utilise celebrities not only to get feedback about HROs from the affected people but also to disseminate crucial information, such as alerts, to the affected population at increased speed. Hence, this study recommends that the HO and the government explore utilising the services of celebrities effectively during HROs, as they possess significant social influence.
  Keywords: social media; information asymmetry; 2015 Chennai Floods; celebrities; humanitarian supply chain management; HSCM; relief/response; case study.
  
  
• Modelling fluctuating market size based adoption of technological products: an alternative formulation  
  by Jyotish N. P. Singh, Adarsh Anand, Deepti Aggrawal, Chanchal 
  Abstract: Customers in the ever-changing digital advanced marketplace often demonstrate unpredictable behaviours that can significantly impact the potential market size. Some satisfied customers may exhibit loyalty and choose to repurchase the product, while others may be driven by indecision and impatience, resulting in lost sales. These contrasting actions of repeat purchasing and balking behaviour can result in fluctuating market size, underscoring the importance of considering such scenarios when studying technological adoption models. The authors have proposed an alternative formulation of the innovative technological product adoption model to address these ever-changing market dynamics. Notably, this formulation simultaneously accounts for customers balking and repurchasing behaviour. By incorporating these factors, managers can gain insights into predicting market performance amidst fluctuations and navigate market volatility more effectively. The proposed alternative formulation is validated using real-life datasets and the obtained results support its practical application in predicting market outcomes. These findings lend credibility to the application of the alternative formulation, empowering managers with the means to anticipate and adapt to market fluctuations with greater precision.
  Keywords: balking behaviour; market dynamics; technological adoption; repeat purchase.
  
  
• An interdisciplinary examination of the evolution of e-commerce recommendation systems: perspectives from management, social science, and psychology  
  by Aman Mathan, Deepak Verma, Divesh Kumar 
  Abstract: The rapid incorporation of digital technologies in businesses poses significant challenges for businesses entrenched in traditional approaches. Conventionally, e-commerce gained popularity for its extensive customer reach, which is no longer sufficient in the current digital era. Nowadays, most e-commerce platforms utilise recommendation systems (RS) supported by complex algorithms and models that influence customers’ online product searching and purchase experiences. RS is predominantly associated with information systems (IS) and computer science (CS) research, despite it being a multi-disciplinary field. The majority of the research on RS is concentrated in IS and CS, with a primary focus on methodology and algorithms. This study examines the existing literature on RS in the domains of management, social sciences, and psychology to identify developments that extend beyond methodology and algorithms. The aim is to broaden the scope of the research domain.
  Keywords: recommendation systems; recommender agents; e-commerce; product recommendation systems; thematic evolution; literature review; bibliometrics.
  DOI: 10.1504/IJIE.2025.10070511
   
  
• Data-driven approach for analyzing the impact of external factors on pearl millet and stover yield  
  by Nikita Dhankar, Srikanta Routroy, Satyendra Kr. Sharma 
  Abstract: The objective of the study is to explore the relationship between external factors such as linkage with self-help groups (SHGs), crop diseases, ground water quality, soil testing, rainfall, and temperature during cultivation impact with pearl millet yield and Stover yield. The responses from 473 farmers are collected through structured survey questionnaire. The outcomes reveal that the relationship of all external factors is statistically significant for both yields except the temperature during cultivation. The analysis of variance (ANOVA) and Pearson coefficient of correlation indicates a statistically significant positive correlation between pearl millet yield and Stover yield. The current study is first of its kind related to impact of external factors on pearl millet yield and Stover yield. The outcomes of the study will help policymakers in developing strategies for enhancing pearl millet and Stover yield which in turn will increase farmer income. In addition to having access to an incentive scheme for free testing of their ground water quality and soil health, the farmers have to be linked to self-help groups (SHGs).
  Keywords: pearl millet supply chain; analysis of variance; ANOVA; pearl millet yield; Stover yield; self-help group linkage; soil testing; Pearson coefficient of correlation.
  
  
• Artificial intelligence and vaccine supply chain: analysis of the adoption challenges in the Indian context  
  by Ambuj Kumar, Om Ji Shukla, Shailesh Mani Pandey 
  Abstract: Integrating artificial intelligence (AI) with the vaccine supply chain (VSC) can generate a robust VSC model, making it easier to supply chain live-saving vaccines and drugs. This study aims to explore challenges in integrating VSC and AI through an extensive literature survey and then validation by a team of experts through the Delphi method. The study takes into account the opinions of important groups, such as government agencies, hospitals, drug companies, and tech companies. The challenges are modelled by a multi-criteria decision-making method integrated with a fuzzy method named as fuzzy decision-making trial and evaluation laboratory (FDEMATEL) technique. The results indicate that long-term sustainability, adoption and skill gap and cost and resource constraints are the most prominent challenges that must be addressed for the successful integration of AI in VSC. The information learned from this study can help the supply chain for vaccines in India.
  Keywords: artificial intelligence; AI; vaccine supply chain; VSC; DEMATEL; robust model.
  
  
• Use of machine learning for classifying manufacturing companies based on their digital transformation levels  
  by Ece Acar, Gorkem Sariyer 
  Abstract: The transformative role of machine learning technology in promoting technological innovation leading sustainable growth is becoming increasingly significant in todays business era. In this study, we implemented machine learning technology to classify the companies according to their digital transformation levels. We used manufacturing companies in Borsa Istanbul (BIST) index as the sample. We constructed a digital transformation level index based on text analysis to measure the frequency of keywords related to digital transformation. We used the sampled companies financial, sustainability, corporate governance performance and research & development (R&D) expenditures to model their digitalisation levels. We observed that between the various machine learning algorithms, with 82% accuracy, Random Forest outperformed the others. We also showed that while R&D expenditure was the most important feature, financial performance-related features were also significant. Thus, we concluded that companies with higher financial performances, especially those making more expenditures for R&D activities, have higher digital transformation levels.
  Keywords: digital transformation; financial performance; R&D expenditure; machine learning; classification.
  
  
• Predicting emotional intelligence, creative performance and knowledge management in higher education using multiple regression  
  by Amar Kumar Mishra, Megha Ojha, Saumya Sharma, Smita Jain, Mayank Kumar, Archana Singh 
  Abstract: Higher education institutions are paramount in emerging nations like India. Post-globalisation, India witnessed the growth of HEIs, especially in the private sector. However, today most of the institutions are struggling for their existence. One of the most vital reasons for such a staggering performance is the absence of creativity. It will not be an exaggeration to say that the present era is the era of creativity and performance and organisations that cant perform are bound to perish. Creativity can be nurtured and yield success only if it is supported by the emotional intelligence (EI) of the employees and knowledge management (KM) processes. The current paper explored the nexus between emotional intelligence, knowledge management processes and creative performance in HEIs in India and implied that though emotional intelligence affects creative performance, the impact gets manifolded in the presence of the knowledge management process.
  Keywords: emotional intelligence; creative performance; knowledge management process; higher educational institutions.
  
  
• Interpretive structural modelling approach for barrier analysis in agent technology enabled smart manufacturing system  
  by Om Ji Shukla, Devesh Kumar, Bharti Ramtiyal, Abhijeet Joshi 
  Abstract: Over the past some years global challenge, flexibility, robustness, re-configurability and customised production have become the most desired needs of the modern manufacturing system. Thus, the utilisation of agent technology (AT) can have a crucial role in developing manufacturing systems that are exceptionally adaptable. The present study analyses the barriers for the implementation of AT which consists of two phases; identification of barriers and qualitative analysis. This research consists of three steps: 1) identification of most relevant barriers for AT implementation from the literature; 2) survey of Indian steering wheel manufacturing industry, rank the identified barriers; 3) establish the interrelationships among barriers. Interpretive structural modelling (ISM) and MICMAC analysis were embraced to understand the interdependency among barriers. This study seeks to identify the most dominant barrier for AT implementation in Indian manufacturing industry.
  Keywords: barriers; interpretive structural modelling; ISM; MICMAC analysis; manufacturing system; agent technology.