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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 global emphasis on sustainability is driving organizations to embrace financial technology (Fintech) solutions as a means of enhancing their sustainable performance. This study seeks to unveil the intermediary role played by green finance and competitiveness, along with the moderating impact of digital transformation (DT), in the intricate relationship between Fintech adoption and sustainable performance.

Drawing on existing literature, we construct a comprehensive conceptual framework to thoroughly analyse these interconnected variables. To empirical validate of our model, a dual structural equation modelling–artificial neural network) SEM–ANN approach was employed, adding a robust layer of validation to our study’s proposed framework. A sample of 438 banking employees in Pakistan was collected using a simple random sampling technique, with 411 samples deemed suitable for subsequent analysis. Initially, data scrutiny and hypothesis testing were carried out using Smart-PLS 4.0 and SPSS-23. Subsequently, the ANN technique was utilized to assess the importance of exogenous factors in forecasting endogenous factors.

The findings from this research underscore the direct and significant influence of Fintech adoption and DT on the sustainable performance of banks. Notably, green finance and competitiveness emerge as pivotal mediators, bridging the gap between Fintech adoption and sustainable performance. Moreover, DT emerges as a critical moderator, shaping the relationships between Fintech adoption and both green finance and competitiveness. The integration of the ANN approach enhances the SEM analysis, providing deeper insights and a more comprehensive understanding of the subject matter.

This study contributes to the enhanced comprehension of Fintech, green finance, competitiveness, DT and the sustainable performance of banks. Recognizing the importance of amalgamating Fintech adoption, green finance and transformational leadership becomes essential for elevating the sustainable performance of banks. The insights garnered from this study hold valuable implications for policymakers, practitioners and scholars aiming to enhance the sustainable performance of banks within the competitive business landscape.

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.

Despite the growing interest in micro-celebrities in destination marketing, their role in transferring cognitive, emotional and behavioral outcomes to destination psychological ownership (DPO) is underexplored. This study aims to address this void by investigating how the perceived characteristics of micro-celebrities influence travel intentions through interactive engagement, perceived information quality and DPO. It highlights three pathways for fostering DPO.

A survey-based design was developed with 302 samples collected and analyzed using partial least squares structural equation modeling and artificial neural network to examine the hypothesized model.

Findings reveal that the expertise and attractiveness of micro-celebrities enhance their perceived personal trustworthiness. This perception encourages social media users to view travel information from micro-celebrities as higher quality and to engage more with them, leading to DPO. Consequently, when social media users experience this psychological ownership, they show a stronger intention to travel to the destination, influenced partly by micro-celebrity marketing.

This study provides a psychological–developmental perspective on micro-celebrity marketing-induced travels. It underscores the importance of fostering micro-celebrity-induced DPO to establish a sustained, mutually beneficial relationship between tourists and destinations.

尽管微名人在目的地营销中的作用已引起学术界的关注, 但他们在传递认知、情感和行为结果至目的地心理所有权中的作用尚未得到充分探索。本研究通过调查微名人的感知特征如何通过互动参与、感知信息质量及目的地心理所有权影响旅行意图, 填补了这一研究空白。此外, 本研究还强调了促进目的地心理所有权的三条途径。

本研究采用基于调查的设计, 收集并分析了302个样本, 并使用偏最小二乘结构方程模型（PLS-SEM）和人工神经网络（ANN）对假设模型进行了检验。

研究结果表明, 微名人的专业知识和吸引力增强了其感知个人可信度。这一感知促使社交媒体用户将微名人分享的旅行信息视为更高质量的信息, 并与他们进行更频繁的互动, 从而促进了目的地心理所有权的形成。由此, 当社交媒体用户体验到这种心理所有权时, 他们表现出更强烈的旅行意图, 这在一定程度上受到微名人营销的影响。

本研究从心理发展的角度探讨了微名人营销引发的旅行意图, 强调了通过微名人激发目的地心理所有权的重要性, 以建立游客与目的地之间持续且互利的关系。

A pesar del creciente interés por las micro-celebridades en el marketing de destinos, su papel en la transferencia de resultados cognitivos, afectivos y conductuales a la apropiación psicológica del destino (DPO) está poco explorado. Esta investigación aborda esta laguna de investigación analizando cómo las características percibidas de las micro-celebridades influyen en las intenciones de viaje a través del compromiso interactivo, la calidad de la información percibida y la DPO. Se destacan tres vías para fomentar la DPO.

Se desarrolló un diseño basado en encuestas, recogiéndose y analizándose 302 observaciones. Se utilizó modelización de ecuaciones estructurales por mínimos cuadrados parciales y redes neuronales artificiales para analizar el modelo propuesto.

Los resultados revelan que la experiencia y el atractivo de las micro-celebridades mejoran su credibilidad personal percibida. Esta percepción anima a los usuarios de redes sociales a considerar la información sobre viajes proporcionada por las micro-celebridades como de mayor calidad y a interactuar más con ellas, lo que conduce a la formación de la apropiación psicológica del destino. Como resultado, cuando los usuarios de redes sociales experimentan esta apropiación psicológica, muestran una mayor intención de viajar al destino, influenciados en parte por el marketing de micro-celebridades.

Este estudio aporta una perspectiva de desarrollo psicológico sobre los viajes inducidos por el marketing de micro-celebridades. Subraya la importancia de fomentar la propiedad psicológica del destino inducida por micro-celebridades para establecer relaciones sostenidas y mutuamente beneficiosas entre los turistas y los destinos.

The present study aims to propose a framework elucidating the attributes of mobile augmented reality (AR) shopping apps (i.e., spatial presence, perceived personalization and perceived intrusiveness) and how they translate to downstream consumer-related outcomes (i.e., immersion, psychological ownership and stickiness to the retailer).

By conducting a questionnaire-based survey, 308 responses were collected, and the data were submitted to partial least square structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses.

A few important findings were generated from the present study. First, attributes of mobile augmented reality shopping apps (i.e., spatial presence, perceived personalization and perceived intrusiveness) influence stickiness to the retailer through immersion and consumer empowerment in serial. Second, immersion positively influences psychological ownership. Third, the optimum stimulation level moderates the relationship between spatial presence and immersion. Lastly, a post-hoc exploratory finding yielded by the multigroup analysis uncovered the moderating effect of gender.

This study offers a novel contribution to the smart retail literature by investigating the role of mobile AR shopping apps in predicting consumers' stickiness to the retailer. A holistic framework elucidating the serial mediating effect of immersion and consumer empowerment, and the moderating roles of optimum stimulation level and gender were validated.

Blockchain, a groundbreaking technology that recently surfaced, is under thorough scrutiny due to its prospective utility across different sectors. This research aims to delve into and assess the cognitive elements that impact the integration of blockchain technology (BT) within library environments.

Utilizing the Stimulus–Organism–Response (SOR) theory, this research aims to facilitate the implementation of BT within academic institution libraries and provide valuable insights for managerial decision-making. A two-staged deep learning structural equation modelling artificial neural network (ANN) analysis was conducted on 583 computer experts affiliated with academic institutions across various countries to gather relevant information.

The research model can correspondingly expound 71% and 60% of the variance in trust and adoption intention of BT in libraries, where ANN results indicate that perceived possession is the primary predictor, with a technical capability factor that has a normalized significance of 84%. The study successfully identified the relationship of each variable of our conceptual model.

Unlike the SOR theory framework that uses a linear model and theoretically assumes that all relationships are significant, to the best of the authors’ knowledge, it is the first study to validate ANN and SEM in a library context successfully. The results of the two-step PLS–SEM and ANN technique demonstrate that the usage of ANN validates the PLS–SEM analysis. ANN can represent complicated linear and nonlinear connections with higher prediction accuracy than SEM approaches. Also, an importance-performance Map analysis of the PLS–SEM data offers a more detailed insight into each factor's significance and performance.

Despite the growing interest in closed-loop manufacturing, there is a lack of comprehensive frameworks that integrate product development, production processes, people and policies (4Ps) to optimize sustainable manufacturing performance. This study investigates the influence of the four Ps of closed-loop manufacturing systems (product development, production processes, people and policies) on sustainable manufacturing performance (SMP).

To investigate the influence of the four Ps on SMP, a hybrid analytical model was employed, combining structural equation modeling (SEM) with artificial neural networks (ANN). Data were collected through a structured survey administered to 353 manufacturing firms in Malaysia. SEM was used to assess the relationships between the variables, while ANN was employed to capture nonlinear relationships and improve prediction accuracy.

The research findings demonstrate that product development practices, including eco-design, life cycle assessment and resource planning, exert the most significant influence on SMP. Furthermore, implementing green and lean manufacturing techniques, energy modeling and material utilization/toxicity planning significantly enhances sustainability outcomes. While the social setting (employee motivation, turnover and work–life quality) does not directly impact SMP, it plays a pivotal role in facilitating the implementation of internal environmental policies. Moreover, environmental management practices, both mandatory and voluntary, serve as intermediaries between the four Ps and SMP within closed-loop manufacturing systems.

The findings offer valuable insights for policymakers, industry leaders and manufacturing organizations. By prioritizing product development, implementing green and lean manufacturing practices and fostering a positive social setting, organizations can significantly enhance their sustainable performance. Additionally, the study highlights the importance of effective environmental management practices in mediating the relationship between other factors and SMP.

This study contributes to the literature by providing a comprehensive framework for understanding the factors that drive sustainable manufacturing performance. The hybrid SEM-ANN model offers a robust and innovative approach to analyzing the complex relationships between the four Ps and SMP.

Soft total quality management (STQM) practices are essential for promoting value-added organizational citizenship behaviour (OCB) among employees in quality-focussed manufacturing firms. This study intends to investigate how STQM practices (empowerment, training, teamwork and involvement) affect OCB under the moderating influence of collectivism among employees for excellence in business performance using social exchange and social cognitive theories (SET-SCT).

A total of 245 useable surveys were gathered from manufacturers. Given the importance of the two-staged structural equation modelling–partial least squares–artificial neural networks (SEM-PLS-ANN) technique, this study used a two-staged SEM-PLS-ANN analysis to capture both linear and compensatory PLS models and nonlinear and noncompensatory ANN models.

The findings confirmed that empowerment, involvement and training had a significant impact on OCB. However, teamwork had no impact on OCB. Interestingly, collectivism was found to have a significant and positive moderating effect on training and OCB.

The study contributes significantly to the literature on TQM and human resource management. First, the study broadens researchers’ understanding of how to apply SET by including a collective value from SCT as positive reciprocity to foster positive workplace behaviour. Second, the authors offer a solid management strategy for organizations to assist them in understanding an STQM model that promotes OCB while including collectivism for superior business performance.

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.