International Journal of Enterprise Network Management (14 papers in press)

Regular Issues

• 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
   
  
• Supplier relationship management maturity: a scale development study  
  by Luay Jum’a, Karam Al Mandil 
  Abstract: Mature supplier relationship management (SRM) practices have an impact on all sourcing processes in manufacturing firms. This paper aims to develop a scale for evaluating and improving supplier relationship management maturity (SRMM). In Study One, data was collected by surveying 202 managers sampled from different industrial sectors in Jordan. The study used exploratory and confirmatory factor analysis to validate the suggested dimensions. Study Two was conducted to cross-validate the resulting scale and to provide evidence of consistency across various industrial sectors based on a different sample of 334 managers. With 20 items, the SRMM scale developed involves five dimensions, including: purchasing department structure, supplier evaluation system, adoption of technology, collaboration with strategic suppliers, and corporate social responsibility goals compliance. This scale provides academics and managers with a multi-dimensional tool for measuring and evaluating SRMM. Moreover, it offers a useful framework to explore the antecedents and consequences of mature SRM.
  Keywords: Supplier relationship management; Supply chain; Supplier relationship maturity scale; Supplier collaboration; Manufacturing firms.
  DOI: 10.1504/IJENM.2025.10065119
   
  
• Antecedents of Economic and Non-Economic Satisfaction in a Franchise Context  
  by Margaret T. Constantaras, Pieter Gerhardus Mostert, Göran Svensson 
  Abstract: Despite the interpersonal and interdependent relationship between franchisors and franchisees where both parties act and work together, the franchisor-franchisee relationship is often strained due to differing individual goals and roles between the franchisor and the franchisee. Although it would seem logical to assume that relationship marketing would, accordingly, be very relevant to franchising, there is a lack of research exploring relationship dynamics in a franchise context and specifically so from a franchisees point of view. This paper addressed this gap by exploring the antecedents of economic satisfaction and non-economic satisfaction, because franchisees satisfaction will determine whether they stay in, or leave, a franchise relationship. Data were collected from 415 South African franchisees operating in 17 franchise sectors. Results revealed franchisees non-economic satisfaction was positively influenced by their service quality, information exchange and economic satisfaction perceptions; and that service quality also predicts economic satisfaction. Positive relationships were furthermore established between service quality and information exchange, with franchisees sense of autonomy, in turn, influencing both their service quality and information exchange perceptions.
  Keywords: autonomy; service quality; information exchange; economic satisfaction; non-economic satisfaction; franchising.
  DOI: 10.1504/IJENM.2025.10065633
   
  
• Assessment of the Relationship between Business Network and Geographical Indication  
  by Thais Braga, Nelson Casarotto Filho 
  Abstract: The adoption of geographical indication (GI) in emerging countries, such as Brazil is growing over the last few years, pursuing the success obtained by global collective. A GI is related to the network of actors in its region of coverage. This paper aims to develop a business network maturity model (BNMM) to support networks to understand the factors influencing its development in order to evaluate the benefits obtained by cooperation and GI. The proposed framework consists of key factors: management, relationships, knowledge, process and services, that trigger 15 constructs of an assessment instrument. In order to provide an initial verification of the developed framework, the questionnaire was applied with three selected networks of wineries and brewers. Findings show that having a GI does not necessarily result in a deep use of it, and a process of collective learning and joint actions would improve the potential benefits obtained with it.
  Keywords: geographical indication; business networks; maturity model.
  DOI: 10.1504/IJENM.2025.10065918
   
  
• Sustainability Assessments of Alternative Procurement Strategies during Supply Chain Disruptions using System Dynamics Approach  
  by Vasanth Kamath, Gurudutt Nayak, Giridhar B. Kamath, Shiva Kumar R, Harsha B 
  Abstract: Disruptions in supply chains (SC) force the professionals globally to ponder on alternate procurement strategies to remain profitable. In this study, we consider a two stage supply chain network, with an objective to study the feasibility of different procurement strategies with the objective of maximising profit. System dynamics (SD) modelling has been used to model and simulate the supply chain design system. The article starts with a literature review on supply chain disruptions and alternative strategies, followed by a case of an enterprise facing SC disruption. Various scenarios are simulated and analysed using SD. The simulation results are expected to provide the organisation an insight into the effects of using alternative procurement strategies. Further, it is expected to add to the body of knowledge in SC disruptions and provide a new methodology to study the behaviour of various procurement strategies for small-scale firms.
  Keywords: supply chain network design; disruption; system dynamics; procurement strategies.
  DOI: 10.1504/IJENM.2025.10065946
   
  
• Machine Learning Based Approach to Identify Human Push Recovery Using GAIT Analysis  
  by Niranjani Seethapathy, Punniyamoorthy Murugesan, Lakshmi G 
  Abstract: The authors have performed empirical mode decomposition for a process to arrive at the human push recovery data using features that are obtained from intrinsic mode functions (IMFs). For the above purpose data related to leg joint angles (hip, knee and ankle) are collected. Three kinds of pushes were applied to analyse the recovery mechanism, in the field of robotics. The classification was performed using deep neural network (DNN) and other classification methods like KNN, naive Bayes, decision tree, random forest and support vector machine. The results have been compared to find the best method for further analysis. Use of five different classification technique and extraction of two additional features which improved the accuracy of the system are some of the unique features of this article.
  Keywords: human push recovery; deep neural network; DNN; K nearest neighbour; decision tree; random forest; support vector machine; SVM; intrinsic mode functions; IMFs.
  DOI: 10.1504/IJENM.2025.10066361
   
  
• An Analysis of the Inter-Relationships among Thermal Comfort, Acceptability, Sensation, Perception, and Preference in Residences  
  by Thirumaran Kesavaperumal, Marliya K, Ramasamy Murugesan 
  Abstract: The energy consumption in residences is significantly impacted by thermal sensation and comfort requirements, warranting careful analysis of factors affecting Thermal comfort. This study aims to analyse the inter-relationships and comparative effects among Thermal comfort and thermal acceptability, thermal sensation, thermal perception, and thermal preference of thermal environments with air, light, temperature, and humidity, in Indian residences. A field study was conducted with a questionnaire that collected data from 261 respondents in Pudukkottai district, Tamil Nadu, India. To determine the inter-relationships between Thermal comfort, acceptability, sensation, perception, and preference in Indian residences, the study uses Path models and Structural Equation Models. The study endorses the importance of four thermal environments called air, light, temperature, and humidity in Indian residences. The study also finds a significant and robust relationship between Thermal comfort, acceptability, preference, sensation, and perception with statistical significance.
  Keywords: Thermal comfort; Thermal Acceptability; Thermal Sensation; Thermal Preference; Thermal Perception; Thermal Environment; Structural Equation Model.
  DOI: 10.1504/IJENM.2025.10066543
   
  
• Sentiment Analysis of Hotel Reviews: An Application of Deep-learning Based Model  
  by R. Murugesan, Rekha AP, Eva Mishra 
  Abstract: Research demonstrates that researchers both from academia and industry are investigating profoundly for successful implementation of sentiment analysis from the uncountable number of hotel reviews being posted per second. Literature finds some constraints in the most frequently used machine learning techniques, BoW, N-grams, and highly effective word embedding methods, Word2vec and Glove warranting an effective model to fill the gap. As suggested by the research, our study applied the BERT-based CRRNN model for sentiment analysis of online hotel reviews which first of its kind for hotel reviews. Our model has exhibited good performance in comparison to most popular machine learning and word embeddings techniques. The evaluation metrics, prediction accuracy, recall, precision, and f-score including graphical representation for ROC and confusion matrix was evaluated to ensure the efficiency of the proposed model. Our sentiment analysis on hotel reviews using BERT-CBRNN will be immensely helpful for all the stakeholders.
  Keywords: SA; hotel reviews; deep-learning-based model; word embeddings; BERT-CBRNN.
  DOI: 10.1504/IJENM.2025.10066578
   
  
• The role of Green Organisational Culture in Moderating the Relationship between Environmental CSR Activities and Green Brand Image of Organisations  
  by Rajalakshmi Subramaniam, Margaret S, Sanjay Mohapatra 
  Abstract: In a competitive environment, firms must use available resources wisely for long-term gains. Green organisational culture (GOC) in an organisation creates an awareness among its employees about the various ways that the firm has taken to develop its attitude towards environmental changes. The current study empirically investigates the moderating role that green organisational culture creates on the relationship between the environment friendly corporate social responsibility activities (EFCSRA) carried out by an organisation and its green brand image. In this research environment friendly corporate social responsibility activities (EFCSRA) is measured through three constructs namely E-customer well-being, E-philanthropy and E-community involvement. The relationship between the proposed variables is tested through the analysis of primary data collected from 646 HR professionals working at organisations belonging to the Indian manufacturing sector. The results of the analysis reveal that E-community involvement and E-customer well-being creates an impact on the green organisational culture. Further it is clear that Green organisational culture moderates the relationship between E-Customer wellbeing and green brand image.
  Keywords: green organisational culture; GOC: environment friendly corporate social responsibility; EFCSRA; green brand image; GB; Indian manufacturing sector; sustainability.
  DOI: 10.1504/IJENM.2025.10066792
   
  
• Refurbished Products in the Circular Economy: Understanding Perceived Risks and Vendor Scepticism  
  by P. Sridevi, Sathish Thamburasa, Manoraj Natarajan, Subhashree Prabhakaran 
  Abstract: A circular economy is an appropriate technique for addressing the scarcity of raw materials and the rise of hazardous waste. It has enormous potential to recover value with used products and promote environmental sustainability. Refurbished products are part of the circular economy. Refurbished products are used products that are repaired, rebuilt, and tested by the company to perform as intended by the manufacturer. This study aims to fill the gap in the literature by examining the effect of perceived risks on vendor scepticism regarding refurbished products, as well as the impact of vendor scepticism on purchase embarrassment and concealment. The study uses the theory of perceived risk to understand how consumers perceive the risks. The results of the study suggest that many risk factors do not significantly impact vendor scepticism, except illegitimate product risk and displeasure risk, vendor scepticism has a significant impact on purchase embarrassment and concealment.
  Keywords: refurbished products; scepticism; circular economy; closed-loop supply chain; CLSC; perceived risk.
  DOI: 10.1504/IJENM.2025.10066901
   
  
• Incubation Performance and Incubator Profiles: A Study on Benchmarking Indian Incubation Units  
  by M.S. Karthicanand, Prakash Sai L. 
  Abstract: Technology business incubators are support ecosystems that enable technopreneurs to launch start-ups based on deep technologies and new business models. In developing countries like India, governments progressively establish and support incubation units to propel entrepreneurship and innovation. The proliferation of business incubators makes it imperative to evaluate their performance and examine the factors that have a bearing on their performance. Using data envelopment analysis (DEA), 53 major business incubation units in India were analysed, revealing avenues for improvement that can enable developing incubation units move closer to the frontier incubation units. Subsequent decision tree analysis revealed incubator profiles that are more likely to influence the classification as a frontier incubation unit, highlighting differences between university incubators and other incubators in terms of the thrust areas they support and the nature of services offered. The attributes that emerge as key differentiators are discussed in the context of supporting Deep-Tech start-ups.
  Keywords: business incubation; Deep-Tech; incubation performance; data envelopment analysis; DEA; decision tree.
  DOI: 10.1504/IJENM.2025.10066974
   
  
• Bitcoin Price Prediction with other Commodity Prices as Exogenous Inputs using Machine Learning Techniques  
  by B. Azhaganathan, R. Murugesan, Shanmugargaraja V, Manasvin Surya B. J, Umesh Shinde 
  Abstract: This study addresses a gap in the literature by predicting Bitcoin prices using commodity prices as exogenous variables, a focus previously unexplored. Bitcoin, often referred to as digital gold, has gained significant attention from investors worldwide due to its resilience during financial distress. Prior research primarily utilized macroeconomic indicators, technical indicators, or combinations of commodity prices and macroeconomic factors. However, our study exclusively examines the predictive power of commodity prices gold, silver, copper, crude oil, and iron ore on Bitcoins price, employing machine learning techniques such as random forest, K-nearest neighbours, decision tree, extreme gradient boost, and linear regression. All models showed strong performance when evaluated against 11 error metrics. The findings underscore a robust correlation between Bitcoin and these commodities, with the machine learning models achieving high accuracy in forecasting Bitcoin price fluctuations. These insights hold v
  Keywords: Bitcoin price prediction; exogenic inputs; Random forest; K nearest neighbours; Extreme gradient boost; Decision tree; Linear regression; Error metrics; Commodity prices.
  DOI: 10.1504/IJENM.2025.10067236
   
  
• The Impact of Entrepreneurial Marketing on SME Performance: Exploring the Mediating Role of Green Innovation and Moderating Role of Networking Capability in Indian SMEs using IPMA analysis  
  by Manigandan R 
  Abstract: This research investigates the impact of entrepreneurial marketing (proactiveness, innovativeness, risk-taking, resource leveraging, opportunity-focused, customer intensity, value creation) on SME performance through the mediating effect of green innovation (green product innovation, green process innovation) and the moderating role of networking capability in Indian SMEs. a conceptual model is proposed with the support of Diffusion innovation theory and resource-based view theories and literature. The data were gathered from 265 owners, Managers and employees in 150 SMEs in south India. The data analysis was employed through SPSS and Smart PLS software. Additionally, a study performed IPMA analysis. The unique research model with the four hypotheses is validated with less than two-tailed 0.001 significance: entrepreneurial marketing positively affects SME performance while moderating networking capability. This research also reveals that green innovation mediates and networking capability moderates the association between entrepreneurial marketing and SME performance. Based on our unique results, managers should adopt entrepreneurial marketing strategies to boost SME performance.
  Keywords: Entrepreneurial marketing; SME performance; Green innovation; Networking capability; Resource-based view theory; Diffusion innovation theory; Indian SMEs.
  DOI: 10.1504/IJENM.2025.10067411
   
  
• A Comprehensive Study on Airline Passengers' Satisfaction and Sentiment using Machine Learning Techniques  
  by R. Murugesan, Rekha AP, Nithish Nithish 
  Abstract: This study aims to integrate the sentiment of the passengers' reviews and other ratings the passengers had provided, such as food, entertainment, and many more, and predict if the passenger recommends the flight. Our research analyses the airline passengers' satisfaction and sentiment using machine learning techniques on the Skytrax Airline Reviews dataset, which contains data on 81 airlines and 64,440 reviews. This data is utilised to compare and contrast different airlines and parameters that determine passenger satisfaction and recommendation. One significant research gap is the insufficient combination of machine learning methods to predict passenger satisfaction and sentiment, which is the primary objective of this study. Most studies have primarily concentrated on specific airlines or routes, neglecting to conduct a comparative analysis of the same. The results show that all the classifiers achieved reasonably high accuracy, with LightGBM performing the best with an accuracy of 97%. These conclusions can help airlines better understand their passengers needs and improve their services accordingly.
  Keywords: passengers’ satisfaction prediction; sentiment analysis; Skytrax Airline Reviews; recommendation; visualisation; comparison of airlines; methods to improve services.
  DOI: 10.1504/IJENM.2025.10067593