International Journal of Enterprise Network Management (8 papers in press)

Regular Issues

• The impact of strategic alliance participation on firm growth  
  by George H. Tompson, Aaron D. Wood, Andrea Cardoni 
  Abstract: Theories explaining the relationship between strategic alliance participation and organisational performance suggest that interfirm collaboration will enable organisations to produce results that otherwise would have been unlikely. However, scholarly research on strategic alliances has frequently produced inconsistent results. Among the reasons results have been inconsistent is that researchers rarely have longitudinal data from multiple alliance partners. In our research, we examined a panel of 49 strategic alliances comprised of 147 firms in Italy over a six-year period. Each strategic alliance was formed midway through the time period, so we could compare each firm’s financial performance before and after joining the alliance. The results show that participation in an alliance is associated with significant positive increases in revenues and assets. These increases in revenues and assets are positively related to the duration of a strategic alliance and negatively related to the number of industries represented within an alliance.
  Keywords: strategic alliances; organisational performance; firm growth; longitudinal data; panel data.
  DOI: 10.1504/IJSBA.2023.10054964
   
  
• 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
   
  
• 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
   
  
• 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
   
  
• A Conceptual Model of Integrated Agile Methodology in Software Development Process  
  by Andrew Jeyathilagar, Ramakrishnan M, Palpandi Ammavasai 
  Abstract: Agile methodology is an iterative approach intended to be used in industries importantly in manufacturing and process industries in order to enhance the productivity and the profitability. Several researchers confluence to investigate critical success factors having the power of agility and their influence in the agile software development (ASD) process. In recent times, the ASD got more importance than the others in software engineering. Therefore, the present authors have developed a model, integrated agile methodology (IAM) for software development process. IAM is a compound system of quality management practice such as ISO 9001:2015 QMS, ISO 45001: 2018 EMS, Occupational Health and Safety Assessment Systems (OHSAS 18001: 2018), Lean Thinking, Six-Sigma (DMAIC), Corporate Social Responsibility (CSR) and TQM. The prime objective of this study is to develop and propose the conceptual framework and research model of IAM and its implementation for better performance in software development process. Based on extant literature reviews, ten important intervening critical factors are identified under IAM and a research model is proposed to examine the extent of its practice in software industries. Further, the present authors suggest that, the IAM model can be tested and validated by empirical means for hypotheses testing.
  Keywords: Agile Framework Methodology; People Factors; Critical Factors; Management Standard Systems; Integration.
  DOI: 10.1504/IJENM.2025.10069605
   
  
• Evolving Novel Classification measures, Optimising and Evaluating the Weights in Weighted Average Random Forest using Quadratic Programming  
  by Kala Nisha G, Punniyamoorthy Murugesan, Priyadharsan M, Dharanidharan L. J 
  Abstract: Attribute selection measures are used in decision trees to select the feature that best splits the data into homogeneous parts. There are four existing measures: the Gini index, entropy, information gain, and gain ratio. In this paper, two novel attribute measures were proposed and were tested on 10 different datasets using decision trees to find their effectiveness. Statistical tests conducted on the proposed novel attribute selection measures showed that they were able to achieve the same level of performance as the existing measures, while eliminating the limitations in the existing measures. In the weighted average random forest, the weights were optimised by minimising the objective function of mean squared error (MSE). This has been done through Quadratic Programming, and the computed optimal weights were used with the decision trees in the weighted average random forest. Overall, it was determined that the weighted average random forest outperformed the random forest.
  Keywords: Gini Impurity; Entropy; Information gain; Gain ratio; New Gain ratio; Purity index.
  DOI: 10.1504/IJENM.2025.10069615