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The case study follows Ann’s journey towards entrepreneurship, focusing on the challenges she faced and how early educational interventions influenced her life decisions. Despite numerous obstacles, Ann’s perseverance, bolstered by her family’s support and her passion, led to her successful reintegration into academia and the launch of an entrepreneurial venture in the UK. Her story highlights the dilemma of balancing educational attainment with entrepreneurial aspirations, especially for at-risk students. Ann’s experience prompts critical discussions about the intersection of education and entrepreneurship, the importance of experiential learning and the role of mentorship in realizing business ideas. The nurturing environment of her business school, through guest lectures and real-world success stories, played a significant role in shaping her academic and professional outlook. This case raises essential questions about the role of higher education in fostering entrepreneurial skills and integrating experiential learning within academic curricula. Ann’s journey exemplifies the power of resilience and determination in overcoming systemic and entrepreneurial challenges, particularly for women facing similar struggles. Her story illuminates the multifaceted process of turning a personal experience into an entrepreneurial opportunity, emphasizing the critical role of mentorship and support networks in developing a viable business idea.

This case study is best suited to undergraduate and graduate students enrolled in management and business-related courses that focus on entrepreneurship and entrepreneurial education. The case study is relevant in various business disciplines as it informs students of the process and challenges related to business start-ups and acquiring related capabilities. Instructors are encouraged to have students read the extensive reference list provided at the end to broaden their understanding and knowledge of entrepreneurship, including its processes, context and practices.

The complexity of citizenship behavior in organizations has long been a focus of research. Traditional methodologies have been predominantly used to address this complexity. This paper aims to tackle the problem using a cutting-edge technological tool: business process mining. The objective is to enhance citizenship behaviors by leveraging primary data collected from 326 white-collar employees in the Indian service industry.

The study focuses on two main processes: training and creativity, with the ultimate goal of fostering organizational citizenship behavior (OCB), both in its overall manifestation (OCB-O) and its individual components (OCB-I). Seven different machine learning algorithms were used: artificial neural, behavior, prediction network, linear discriminant classifier, K-nearest neighbor, support vector machine, extreme gradient boosting (XGBoost), random forest and naive Bayes. The approach involved mining the most effective path for predicting the outcome and automating the entire process to enhance efficiency and sustainability.

The study successfully predicted the OCB-O construct, demonstrating the effectiveness of the approach. An optimized path for prediction was identified, highlighting the potential for automation to streamline the process and improve accuracy. These findings suggest that leveraging automation can facilitate the prediction of behavioral constructs, enabling the customization of policies for future employees.

The findings have significant implications for organizations aiming to enhance citizenship behaviors among their employees. By leveraging advanced technological tools such as business process mining and machine learning algorithms, companies can develop more effective strategies for fostering desirable behaviors. Furthermore, the automation of these processes offers the potential to streamline operations, reduce manual effort and improve predictive accuracy.

This study contributes to the existing literature by offering a novel approach to addressing the complexity of citizenship behavior in organizations. By combining business process mining with machine learning techniques, a unique perspective is provided on how technological advancements can be leveraged to enhance organizational outcomes. Moreover, the findings underscore the value of automation in refining existing processes and developing models applicable to future employees, thus improving overall organizational efficiency and effectiveness.

The overarching goal of the study was to formulate an integrated research model to empirically demonstrate the complex interplay between heuristics, project characteristics, information system usage quality, empathy, and mindfulness in predicting users'/donors' donation behaviour and well-being in the context of donation-based crowdfunding (DBC) mobile apps.

The data were collected from 786 respondents and analysed using the multi-stage SEM-ANN-NCA (Structural equation modelling-artificial neural network-necessary condition analysis) method.

Increased perceived aesthetics, narrative structure, self-referencing, project popularity, project content quality, and initiator reputation would foster empathy. Empathy and mindfulness lead to donation behaviour, and, ultimately emotional well-being.

This study offers a clear framework by ranking the key contextual predictors and assessing the model’s necessity logic to facilitate crowdfunders' donation behaviour and well-being on DBC platforms. This research provides practical insights for bank marketers and further aids financial service providers in formulating an optimal DBC mobile app strategy.

To achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.

A robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.

This paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.

This study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

This study aims to explore how social status recognition, perceived value and immersive enjoyment drive attachment to influencers and endorsements, thus triggering consumers’ hedonic buying towards influencer endorsements in social media.

By following a purposive sampling strategy and collecting cross-sectional data from 379 valid responses in the UK, this study adopts structural equation modelling, artificial neural networks and fuzzy set qualitative comparative analysis (SEM-ANN-fsQCA) as integrated methods for analysis.

This study reveals that social status recognition, immersive enjoyment, gamified incentives, attachment to influencers and endorsements are critical antecedents that drive hedonic buying.

In knowledge, this study concurrently adopts the perceived value theory and attachment theory that can enrich the inner elements and reveal the underlying connections under the theories. In method, the integrated analytical approach can explore deeper and more convincing results without the limitations of a single approach. In practice, this study helps practitioners ascertain customer perceptions of influencer endorsements and their attachment in the context of buying hedonically, thus developing effective strategies for employing influencers and marketing strategies to foster consumers’ hedonic buying behaviours.

This research paper aims to explore the link between financial innovation (FINV), green finance (GRF) and sustainability performance (SUSP) with the overarching objective of driving sustainable growth. The purpose is to understand how the integration of FINV and GRF can contribute to improved SUSP for businesses and organizations.

The study adopts a survey-based approach, synthesizing existing scholarly works, empirical studies and industry reports. It examines the theoretical foundations and empirical evidence to understand the relationship between FINV, GRF and SUSP.

The findings highlight a positive relationship between GRF and SUSP. GRF acts as a catalyst for FINV by providing the necessary financial resources and incentives for organizations to invest in sustainable technologies and practices. It enables businesses to enhance their SUSP by adopting environmentally friendly processes, reducing carbon emissions and promoting resource efficiency. The integration of FINV and GRF fosters sustainable growth by aligning economic, environmental and social objectives.

This research paper contributes to the existing literature by offering a comprehensive examination of the link between FINV, GRF and SUSP. It consolidates and synthesizes previous studies, providing a holistic view of the topic. The paper also presents practical implications for businesses and policymakers, emphasizing the need for strategic integration of GRF and FINV to drive sustainable growth. The identification of future research directions adds originality to the study, guiding scholars and practitioners toward areas of further investigation.

In the existing era, international trade is boosted by maritime freight movement. The academicians and Government are concerned about environmental contamination caused by maritime goods that transit global growth and development. Digital technologies like blockchain help the maritime freight business to stay competitive in the digital age. This study aims to illuminate blockchain technology (BCT) adoption aspects to alleviate early industry adoption restrictions.

This study adopts a two-stage approach comprising of structural equation modeling (SEM) with artificial neural networks (ANN) to analyze critical factors influencing the adoption of BCT in the sustainable maritime freight industry.

The SEM findings from this study illustrate that social, organizational, technological and infrastructual and institutional factors affect BCT execution. Furthermore, the ANN technique uses the SEM data to determine that sustainability enabled digital freight training (S3), initial investment cost (O5) and trust over digital technology (G1) are the most essential blockchain deployment factors.

The hybrid approach aims to help decision-makers and policymakers examine their organizational blockchain adoption goals to construct sustainable, efficient and effective maritime freight transportation.

The purpose of this paper is to explore the domain of metaverse commerce and conduct a thorough examination of the complex dynamics that contribute to impulsive purchasing behavior. This study aims to examine the impact of vividness, interactivity and effectiveness on social presence and telepresence within the metaverse, a digital landscape. Specifically, it seeks to understand how these factors influence consumers' impulsive buying behavior.

The methodology used in this study consisted of distributing self-administered questionnaires via a survey. Data collection was conducted among a targeted sample of 348 participants in Vietnam who had direct experience with metaverse commerce services. Then, the collected data was subjected to analysis using two distinct methodologies: partial least squares structural equation modeling and artificial neural networks.

The findings of this study provide significant insights into the correlation between social presence, telepresence and impulsive buying behavior within the field of metaverse commerce. The research findings also indicate that the impact of social presence and telepresence on impulsive purchasing behavior is contingent upon the enhanced vividness, effectiveness and interactivity of the virtual environment.

The present investigation unveiled a range of linear and non-linear mechanisms that elucidate the functions of effectiveness, vividness and interactivity in facilitating the complex interplay between social presence, telepresence and impulsive buying behavior in the context of metaverse commerce. The study provides both theoretical and practical contributions to the existing body of literature on Metaverse commerce.

The purpose of this research was to develop and evaluate a machine learning (ML) algorithm to accurately predict bamboo compressive strength (BCS). Using a dataset of 150 bamboo samples with features such as cross-sectional area, dry weight, density, outer diameter, culm thickness and load, various ML algorithms including artificial neural network (ANN), extreme learning machine (ELM) and support vector regression (SVR) were tested. The ELM algorithm outperformed others, showing superior accuracy based on metrics like R2, MSE, RMSE, MAE and MAPE. The study highlights the efficacy of ELM in enhancing the precision and reliability of BCS predictions, establishing it as a valuable tool for assessing bamboo strength.

This study experimentally created a dataset of 150 bamboo samples to predict BCS using ML algorithms. Key predictive features included cross-sectional area, dry weight, density, outer diameter, culm thickness and load. The performance of various ML algorithms, including ANN, ELM and SVR, was evaluated. ELM demonstrated superior performance based on metrics such as coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), establishing its robustness in predicting BCS accurately.

The study found that the ELM algorithm outperformed other ML algorithms, including ANN and SVR, in predicting BCS. ELM achieved the highest accuracy based on key metrics such as R2, MSE, RMSE, MAE and MAPE. These results indicate that ELM is a highly effective and reliable tool for predicting the compressive strength of bamboo, thereby enhancing the precision and dependability of BCS evaluations.

This study is original in its application of the ELM algorithm to predict BCS using experimentally derived data. By comparing ELM with other ML algorithms like ANN and SVR, the research establishes ELM’s superior performance and reliability. The findings demonstrate the significant potential of ELM in material strength prediction, offering a novel and robust approach to evaluating bamboo’s compressive properties. This contributes valuable insights into the field of material science and engineering, particularly in the context of sustainable construction materials.