International Journal of Enterprise Network Management (21 papers in press)

Regular Issues

• Supplier selection and evaluation using Alteryx tool AHP in gear manufacturing industry  
  by Balamurugan Ramasamy, Arun R, Arunkumar N, Ganesan R 
  Abstract: One of the most important problems for many firms is the decision-making process in supplier selection. The most inclusive process in the decision-making process is the analytical hierarchy process. This study gives instructions for setting supplier selection criteria for university procurement department purchasing activities. The decision-making method of the AHP is based on a multi-criteria examination of cost, flexibility, quality, delivery, and polish or machining property. This work provides clarity in the classification of supplier attributes that have been emphasised in the model business. It gives an idea about the AHP and also how to select the supplier using with necessary criteria. The data collection is done in the gear manufacturing industry. Totally 20 suppliers were considered for our evaluation. Out of 20 suppliers, three suppliers got the highest priority rank of 96.9% among all suppliers by considering various factors such as price, quality, on-time delivery, etc.
  Keywords: supplier selection and evaluation; supplier ranking; analytic hierarchy process; AHP; Alteryx tool.
  DOI: 10.1504/IJENM.2024.10052444
   
  
• Financial Inclusion on Women Entrepreneurs-Review  
  by Vosuri Sandhyarani, N. Sundaram 
  Abstract: A nation’s development depends on women’s growth in society Women were previously given increasing responsibilities but without recognition. The development of women’s empowerment started 100 years ago, which is still a question for many women, whereas many started to place their feet in full swing on many platforms. Women manage and adapt themselves by working as housewives, employees, and employers, and eventually as promising entrepreneurs. Women’s economic empowerment leads to the country’s economic boom. Especially in India, we have half of the population is female. Therefore, the development and involvement in the industry play a significant role in the Indian economy. The main roadblocks for female entrepreneurs there are numerous. Managing financial investments is one of them. Here we discuss the financial inclusion of women entrepreneurs and the improvement of women as entrepreneurs. This review will also provide adequate knowledge about financial inclusion for women to build up their businesses.
  Keywords: empowerment; women entrepreneurs; financial inclusion; education; economy; scheme for women MSMEs.
  DOI: 10.1504/IJENM.2024.10052493
   
  
• Promotion of Entrepreneurship through Accessibility of Formal Micro Credit in Emerging Countries  
  by Vosuri Sandhyarani, N. Sundaram 
  Abstract: Entrepreneurship is an economic phenomenon that makes unemployed people self-employed in society. Microfinance is the practice of providing small amounts of essential financial services to the unbanked or impoverished population in a nation, such as credit, deposits, and insurance, and is a growth-driving factor of entrepreneurship in the economy. The present study is aimed at an analysis of India’s largest formal microcredit scheme, which accounts for one fourth of the nation’s population, namely the Pradana Mantry Mudra Yojana for the period of 2015
  Keywords: micro credit; entrepreneurship; PMMY; MUDRA; formal micro credit.
  DOI: 10.1504/IJENM.2024.10052602
   
  
• Hybrid sparse and block-based compressive sensing algorithm for industry based applications  
  by Sekar R, Manikanda Devaraja N, Ravi G, Rajasekaran B, Chidambaram S 
  Abstract: Image reconstructions are a challenging task in MRI images. The performance of the MRI image can be measure by following parameters like mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Compromising the above parameters and reconstructing the MRI image leads to false diagnosing. To avoid the false diagnosis, we have combined sparse based compressive sensing and block-based compressive sensing algorithm, and we introduced the hybrid sparse and block-based compressive sensing algorithm (HSBCS). In compressive stage, however, image reconstruction performance is decreased, hence, in the image reconstruction module, we have introduced convex relaxation algorithm. This proposed algorithm is obtained by relaxing some of the constraints of the original problem and meanwhile extending the objective function to the larger space. The performance is compared with the existing algorithm, block-based compressive sensing algorithm (BCS), BCS based on discrete wavelet transform (DWT), and sparse based compress-sensing algorithm (SCS). The experimentation is carried out using BRATS dataset, and the performance of image compression HSBCS evaluated based on SSIM, and PSNR, which attained 56.19 dB, and 0.9812.
  Keywords: MRI image; block compressive sensing; sparse compressive sensing; image reconstruction.
  DOI: 10.1504/IJENM.2024.10052992
   
  
• Effectiveness of Digital Forensic Investigation through Excavation Methods of Various Linux Based Tools  
  by Bhraguram TM, Rajakumar PS, Arshia Arjumand Banu 
  Abstract: Digital forensic is a process of pre-processing, identification, modelling, extraction, and documentation of computer evidence. The forensic investigations in today’s human life are more important due to the high-level cyber crime activities and other proof less investigations happening under various public and private domains. The computer world updates various method to do the investigation activities and most of the methods are working based on the existing activity monitoring and proof-based content available for the processing. Various computer platforms give many procedures to continue the investigation process, but the effectiveness and accuracy is completely depending on the tools and data proof used while processing the data. Linux is one of the most eligible and rich tools providing platform with various proofreading mechanisms. We are trying to furnish the most effective methods used for digital forensic investigations in Linux platform which are proved with high level of accuracy and integrity. This article can provide various mechanism used in the tools and its effectiveness through an excavation method.
  Keywords: digital forensic; cyber crime; excavation method; Linux platform; platform based.
  DOI: 10.1504/IJENM.2024.10053188
   
  
• Edge Controller Based Deep Learning Deep Learning Framework for Data Driven View in 5G Cellular Network  
  by Shermin Shamsudheen, Anne Anoop, Anjali Appukuttan, Praveetha Gopinathan 
  Abstract: The emergence of the 5G portable network has brought plenty of advantages. Notwithstanding, it provoked new difficulties in the 5G organisation’s online protection guard framework, resource management, energy, and reserve, along these lines making the current methodologies out of date to handle the new difficulties. This paper brings an effective edge-based DL model for a 5G cellular network. It gives insights about cloud controller managing RAN for transferring data from user devices to the core network, for example, network strength, security capacities, and network versatility. The proposed engineering comprises four unique layers recognised as network orchestration layer, RAN controllers layer, distributed units layer, and service layer. It uses a DCNN-based model and also further converges with feed-forward organisations to learn the effect of organisation designs and other outside factors. To enhance the safety features of the proposed model, we have used AES methods besides DCNN on the edge. Experimental studies state that while evaluating our DL incorporated model with other techniques, the proposed model outperforms under measures like accuracy, memory utilisation, sensitivity, etc.
  Keywords: edge; 5G; cellular network; deep learning; DL; controller.
  DOI: 10.1504/IJENM.2024.10055936
   
  
• Role of supply chain resilience capacity in Chinese auto business relationships during disruption.  
  by Balakrishnan AS, Jayshree Suresh, Harindranath R.M. 
  Abstract: To empirically examine the supply chain resilience capacity (SCRC) practices that emerged in Chinese automotive business relationships during disruption and to measure the impact of the business relationships on the firms business performance and long-term B2B relationships. On the basis of literature review an integrated framework was developed. Using questionnaire survey, 202 Chinese automotive practitioners responded the existing relationships. As per the findings, SCRC recovery during disruption situation exhibited the following effects: a) Promising and dark-side of relationships have curvilinear effect or U-shaped relation with business performance and long-term B2B relationships, b) direct effect of SCRC interdependencies with business performance and long-term B2B relationships, c) SCRC interdependencies positively moderate the non-linear relationship between both the promising and dark-side of business relationships with business performance, and only dark-side of non-linear relationships with long-term B2B relations, and d) SCRC interdependencies doesnt moderate the promising side of non-linear relationships with long-term B2B relations.
  Keywords: B2B relationships; disruption; supply chain resilience capacity; Chinese automotive business; business performance.
  
  
• An Analysis of RFID Implementation in MSMEs Supply chain  
  by Binkey Srivastave, Amit Arora, Vijay Gupta 
  Abstract: Now days business enterprises are using wide applications in their business operations and supply chain management. The Radio Frequency Identification Technology (RFID) is a smart tag with an intelligent device embedded with a chip attached to the inventory units which helps in a transaction. A wireless system of RFID helps in tracking the inventory and getting the market value of the products. But somewhere the adoption of RFID particularly for MSMEs in the supply chain is a challenge and achiev
  Keywords: RFID tags; MSMEs; Supply chain; RFID implementation; RFID Technology Adoption.
  
  
• Novel Design and Implementation of Irregular Fractal Arrow Head Structure Microstrip Antenna for sub SUB 6GHz 5G Applications  
  by Silamboli J, Pradeepa P 
  Abstract: This article is based on a novel compact irregular Fractal shape antenna with an arrow-head structure (IFAS). The presented antenna is devised for 3.2GHz operating frequency and its implementation is mainly focused on 5G mobile applications. Though many antenna types are available, a fractal antenna has the advantages of having a compact size, good multiband performance with wide bandwidth, and provides consistent performance. The Antenna design involves two iterations which uses Fractal Techniques. The dimension of the proposed antenna is 17.8
  Keywords: 5G technology; micro-strip fractal antenna; arrow-head shape; millimetre-wave communication.
  DOI: 10.1504/IJENM.2024.10056268
   
  
• Mining customer reviews to evaluate the contact center agent performance using custom kernel functions  
  by A. Santhosh Kumar, Punniyamoorthy Murugesan, Ernest Johnson 
  Abstract: In today's digital world, the exponential growth of unstructured text data necessitates businesses to rethink their organisational strategies based on the insights extracted from data using text or opinion mining. To extract opinions from text documents, various machine learning algorithms are utilised, with support vector machine (SVM) being a popular one due to its ability to efficiently classify non-linear data using the Kernel trick (Kernel function). This function implicitly transforms the input to a higher dimensional vector space, making it easier to classify data linearly. In our study, we have applied the dissimilarity kernel function, which is suitable for sparse data. We evaluated the performance of the new kernel function in classifying opinions from customer feedback in the business to consumer (B2C) contact centre industry and ranked contact centre agents based on the customer feedback data.
  Keywords: opinion mining; Jaccard dissimilarity kernel; custom kernel functions; contact centre agent performance.
  DOI: 10.1504/IJENM.2024.10057130
   
  
• An analysis on the time-varying correlation among selected agricultural commodities: A DCC-GARCH Model-based approach  
  by Eva Mishra, R. Murugesan 
  Abstract: As per the literature survey, very few studies analyse the dynamics of conditional correlation and spillover effects between agricultural commodity prices. This research aims at finding the dynamic correlation among agricultural commodity prices. The knowledge of the dynamic correlation between agriculture crop prices is of great significance to consumers, government agencies, investors, farmers, and policymakers. The DCC-GARCH model is used on the agricultural commodity prices such as rice, wheat, gram, banana, groundnut, onion, potato, and sugarcane, spanning 20002020, collected from the Indian agricultural market. Our research confirms the presence of a dynamic correlation between agricultural commodity prices. The DCC-GARCH model was found to be efficient in evaluating conditional correlation. There was a conditional correlation among gram and other agricultural crops (banana, groundnuts, onion, and potato) prices for a long period. The change in the price of rice crops alters the prices of other agricultural commodities considered in our research.
  Keywords: dynamic correlation; spillover; DCC-GARCH; agricultural crop prices; agricultural market.
  DOI: 10.1504/IJENM.2024.10057821
   
  
• A Bibliometric Analysis of Skill Development Training and Employability: Towards a Future Research Agenda  
  by S. Lakshmi Devi, Simanchala Das, Biswajit Acharjya 
  Abstract: This paper presents a bibliometric analysis of skill development training and employability, to identify emerging research trends and proposes a future research agenda in this area. A comprehensive analysis of extant literature retrieved from 284 articles and 2,353 citations from the Scopus database for the period from 19742022 was made for getting an overall understanding of the trends and patterns of research on skill development. The study explored a growing trend toward publications on skill development research over the years and provides ample scope for future research. Furthermore, this thematic analysis of the publication history identifies the principal dimensions and directions of the research on skill development. By synthesising and organising research on skill development training, the present study would also help policymakers to redesign the interventions aimed at enhancing employability and promoting sustainable economic growth.
  Keywords: skill development; Research; publications; bibliometric analysis; employability; sustainable economic growth.
  DOI: 10.1504/IJENM.2024.10058121
   
  
• Ensemble Classifiers for Bankruptcy Prediction Using SMOTE and RFECV  
  by T. Shahana, V. Lavanya, Aamir Bhat 
  Abstract: This research investigates the impact of preprocessing strategies, namely feature selection (utilising correlation and recursive feature elimination with cross-validation) and class imbalance handling (employing synthetic minority oversampling technique), on the performance of prediction models using ensemble-learning techniques (random forest, AdaBoost, gradient boosting decision tree, extreme gradient boosting, bagging, LightGBM and extra tree classifier). The study focuses on the Polish bankruptcy dataset to assess the effectiveness of these preprocessing approaches. Experimental results demonstrate that adopting class imbalance handling significantly influences classifier performance compared to feature selection alone. Interestingly, hyperparameter tuning and feature selection exhibit limited impact on classifier performance. Among the ensemble-learning techniques tested, the adaptive boosting classifier shows consistently poor performance throughout the study period, followed by the bagging classifier with statistical significance. These findings shed light on the importance of selecting appropriate preprocessing strategies to improve the performance of ensemble-based prediction models in bankruptcy prediction tasks.
  Keywords: bankruptcy prediction; ensemble classifiers; missing value imputation; SMOTE; correlation; RFECV.
  DOI: 10.1504/IJENM.2024.10058997
   
  
• Performance improvement in inventory classification using the Expectation-Maximization algorithm  
  by Kathirvel Selvaraju, Punniyamoorthy Murugesan 
  Abstract: Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.
  Keywords: expectation-maximisation algorithm; Gaussian mixture model; GMM; multi-criteria inventory classification; MCIC; ABC classification; fill rate; total relevant cost.
  DOI: 10.1504/IJENM.2024.10059173
   
  
• Uncovering the factors influencing employees' intention to quit in hospitality industry  
  by Nivethitha Santhanam, Vaijayanthee Anand, Srinivasan S 
  Abstract: The purpose of this study is to examine the role of human resource practices (training and career development opportunities) in explaining employees' turnover intentions, as well as the interaction effects of organisational identification on this relationship. Data were gathered from 410 frontline employees working in India's four- and five-star hotels. The hypothesised relationships were tested using structural equation modelling. The findings indicate that employees' perceptions of human resource practices in their organisation had a significant impact on their intention to quit. Furthermore, the interaction of organisational identification with human resource practices significantly moderated the investigated relationship, with higher organisational identification decreasing the intention to leave. By emphasising the impact of organizational-level practices on individual-level outcomes, the study contributes to the academic discourse on human resource practices. The findings of the study also support the utility of aligning organizational goals and values with those of employees.
  Keywords: Employee turnover intention; human resource practices; organizational identification; hospitality industry.
  DOI: 10.1504/IJENM.2024.10059184
   
  
• An empirical study on the nexus among the prices of commodities: An ARDL and Bound test approach  
  by Shanmugargaraja V, R. Murugesan, Ishita Khartad 
  Abstract: This study investigates the nexus among the commodities: Bitcoin, Copper, Gold, Silver, Crude oil, and Iron ore.Previous studies on establishing the plausibility and the dynamic nexus among commodities are rare.This research attempts to fill this gap. This study investigates whether there are long-term and short-term links between commodities for the period 2010-2022 by applying the bounds testing method to co-integration and ECM, built using an ARDL model and establishing both short-term and long-term relationships among the economic variables analyzed. The ECM confirmed the presence of some co-integration relationship for all the variables, both in the short and long term. A strong correlation was discovered among the commodities, which were greatly influenced by their lagged values. The results of this study provides an opportunity for policymakers and researchers to understand the nature of the relationship between the analyzed variables and further support the development of new policies for economic sustainability.
  Keywords: ARDL; NARDL; Bound test; nexus among commodities; ECM; ECT; bitcoin. Copper; Gold; Silver; Crude oil; Iron ore.
  DOI: 10.1504/IJENM.2024.10059264
   
  
• Factors influencing the consumer intention to recommend the adoption of the near field communications: A partial least square- structural equation modelling approach  
  by Rishabh Shekhar, Tangala Venkateswarlu 
  Abstract: The study aims to investigate the factors influencing the consumer intention to recommend the adoption of the near field communications. Constructs, namely relative advantages, trust, hedonic motivations, personal innovativeness, customisations, and, are included in the technology acceptance model. The influence and role of relative advantages, trust, hedonic motivations, personal Innovativeness, customisations, and were investigated. The study's findings unveil that trust and perceived ease of use significantly influence perceived usefulness. Another interesting result of this study is that relative advantages, trust, hedonic motivations, personal Innovativeness, and customisation affect perceived ease of use. Theoretical, practical implications and future avenues are discussed.
  Keywords: near field communications; NFCs; intention to recommend; mobile wallet payment services; technology acceptance model; TAM; structural equation modelling.
  DOI: 10.1504/IJENM.2024.10059352
   
  
• Mobile wallet payments- A Systematic literature review with Bibliometric and Network Visualization analysis over two decades  
  by Arijit Das, Rishabh Shekhar 
  Abstract: The study aims to review the literature on mobile wallet payment and align research trends using a systematic literature review with bibliometric and network visualisation analysis over two decades. It uses bibliometric analysis of the literature research retrieved from the Web of Science database. The study period was from 2001 to 2021, with 1,134 research papers. It also provides the indicators like citation trends, cited reference patterns, Authorship patterns, subject areas published on the mobile wallet, top contributing authors, and highly cited research articles using the database. Furthermore, network visualisation analysis, like the cooccurrence of author keywords and keywords plus terms, has also been examined using VOS viewer software. The bibliometric analysis shows that the Republic of China dominates mobile wallet payment, and India is a significant contributor. Furthermore, the constructions of the network map using a co-citation analysis and bibliographic coupling shows an interesting pattern of mobile wallet payment.
  Keywords: bibliometric analysis; MWPS; citation analysis; VOS viewer; network analysis; visualisation analysis.
  DOI: 10.1504/IJENM.2024.10059490
   
  
• A Qualitative Analysis of Customer Acquisition in Online Fitness Communities  
  by Yamini S., Gajanand M. S 
  Abstract: Physical activity and exercise are important for all age groups. The virtual fitness sector has experienced a boom post-COVID-19 pandemic due to changes in lifestyle. Research studies that analyse the effect of the usage of fitness applications are very scarce. To bridge this research gap, the impact of these factors in creating awareness and increasing the download of fitness apps is studied. We conduct an exploratory study to identify the factors that influence the online fitness sector post-COVID-19 pandemic and find alternatives that can attract more customers to use fitness applications. The results of the study show that advertisements in social media play a major role in marketing a product to a larger audience, but it is not necessary that a celebrity will make an impact on the product to the audience. The results from this study will help managers of fitness apps and directors of online fitness programs.
  Keywords: virtual fitness applications; healthcare; digital healthcare start-up; exploratory study; customer acquisition.
  DOI: 10.1504/IJENM.2025.10059713
   
  
• Cognitive biases in decision making during the pandemic: Insights and viewpoint from people behaviour  
  by Yamini S., Gajanand M. S 
  Abstract: In this article, we have attempted to study the ways in which the COVID-19 pandemic has gradually increased and impacted the world. The authors integrate the knowledge from cognitive psychology literature to illustrate how the limitations of the human mind might have a critical role in the decisions taken during the COVID-19 pandemic. The authors show the correlation between different biases in various contexts involved in the COVID-19 pandemic and highlight the ways in which we can nudge ourselves and various stakeholders involved in the decision-making process. This study uses a typology of biases to examine how different patterns of biases affect the decision-making behaviour of people during the pandemic. The presented model investigates the potential interrelations among environmental transformations, cognitive biases, and strategic decisions. By referring to cognitive biases, our model also helps to understand why the same performance improvement practices might incite different opinions among decision-makers.
  Keywords: behavioural economics; cognitive biases; pandemic; irrationality; information related biases; loss aversion.
  DOI: 10.1504/IJENM.2024.10059792
   
  
• The impact of market momentum towards the Initial Public offerings: Evidence from an emerging market.  
  by P. Sridevi, Senthil Arasu Balasubramanian, Nivetha S, LakshmiNarasimhan Chari 
  Abstract: This study developed a model to predict the measures that impact the initial day return of initial public offerings and explain the importance of market momentum in predicting the initial day return of IPOs using OLS regression and random forest. This study analysed 239 mainline IPOs, issued and traded on the National Stock Exchange from 20092020. This study developed three models to identify the measures that influence the initial return of IPOs and to prove the predictive power of different market momentum towards the initial return of IPOs. The outcomes show that market momentum has a high impact on Indian IPOs and the Information asymmetry variable is highly crucial to predicting the performance. Random forest results indicate a low out-of-bag error for the model incorporating MSLO and MS21. Firm size (FS), offer price (OP) and earning per price were also primary predictors of initial return.
  Keywords: India; IPO performance; market momentum; OLS regression; random forest; cyclical behaviour theory.
  DOI: 10.1504/IJENM.2024.10060763