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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.

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.

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.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

The purpose of this study is to explore the intersection of mindfulness practices and ethical decision-making within organisational leadership. Drawing from ancient Buddhist principles and contemporary neuroscience, this study aims to illuminate how mindfulness can enhance cognitive and emotional regulation, thereby fostering ethical behaviour and improved decision-making among leaders and employees. By examining the theoretical and practical implications of mindfulness in the context of organisational behaviour, this research seeks to contribute to the development of more compassionate, ethical and effective leadership practices, ultimately promoting a more mindful and sustainable business environment.

This concept paper explores the integration of mindfulness meditation practices with decision-making, particularly its influence on ethical choices, through a comparative study of modern techniques and the ancient teachings of the Tripitaka. Using a methodology that spans literature review in organisational behaviour and leadership, alongside in-depth analysis of the Tripitaka and contributions from scholars like Bhikkhu Bodhi, the paper examines the potential of mindfulness in enhancing ethical decision-making. It incorporates a range of sources, including peer-reviewed journals and seminal books across various disciplines, to underscore the transformative potential of mindfulness in addressing contemporary challenges and guiding leadership practices.

This discussion explores how mindfulness, rooted in ancient Buddhist philosophy and aligned with modern neuroscience, can significantly enhance managerial decision-making by fostering a balance between cognitive and emotional factors. It delves into the transformative potential of mindfulness in refining thought processes, promoting ethical decision-making and mitigating cognitive biases. By bridging traditional wisdom with contemporary scientific insights, the analysis underscores mindfulness as an active, dynamic process crucial for personal growth and effective leadership in complex environments.

One limitation of this research is its reliance on theoretical frameworks and literature reviews, which may not capture the full range of practical challenges in implementing mindfulness practices within organisations. Additionally, the diversity in mindfulness methodologies and the subjective nature of mindfulness experiences may affect the generalisability of the findings. Future research should include empirical studies to validate the proposed benefits of mindfulness in organisational settings and explore the most effective strategies for integrating mindfulness practices into leadership and decision-making processes. This would help in understanding how mindfulness can be tailored to suit different organisational cultures and individual preferences.

The practical implications of applying mindfulness in organisational settings include enhanced decision-making abilities, improved leadership effectiveness and increased employee well-being. Mindfulness training can equip leaders and employees with the skills to manage stress, navigate complex ethical decisions and maintain focus amidst distractions, leading to more thoughtful and responsible business practices. Organisations might see a reduction in conflict, enhanced creativity and better teamwork, contributing to a more harmonious and productive workplace. Implementing mindfulness programmes could also support talent retention and attraction by promoting a workplace culture that values mental health and ethical behaviour.

The social implications of integrating mindfulness into organisational decision-making and leadership, as suggested by the document, include promoting ethical behaviour, enhancing emotional regulation and improving team dynamics. Mindfulness practices can lead to more informed and conscious decision-making, reducing cognitive biases and fostering a culture of ethical awareness within organisations. This shift towards mindful leadership could potentially transform organisational cultures, encouraging greater compassion, ethical responsibility and collective well-being, thereby contributing positively to broader societal values and norms.

The originality and value of this research lie in its novel integration of mindfulness concepts derived from ancient Buddhist teachings with contemporary neuroscience and organisational behaviour studies. By exploring the deep-rooted philosophical underpinnings of mindfulness and their applicability to modern ethical decision-making and leadership practices, this work offers a unique perspective that bridges historical wisdom with current scientific understanding. It provides a comprehensive framework for understanding the transformative potential of mindfulness in organisational settings, highlighting its capacity to foster ethical leadership, enhance decision-making processes and contribute to a more mindful, compassionate and sustainable business environment.

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.

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 proliferation of smartwatches in the digital age has radically transformed health and fitness management, offering users a multitude of functionalities that extend beyond mere physical activity tracking. While these modern wearables have empowered users with real-time data and personalized health insights, their environmental implications remain relatively unexplored despite a growing emphasis on sustainability. To bridge this gap, this study extends the UTAUT2 model with smartwatch features (mobility and availability) and perceived security to understand the drivers of smartwatch usage and its consequent impact on environmental sustainability.

The proposed theoretical model is evaluated based on data collected from 303 smartwatch users using a hybrid structural equation modeling–artificial neural network (SEM-ANN) approach.

The PLS-SEM results supported smartwatch features’ effect on performance and effort expectancy. The results also supported the role of performance expectancy, social influence, price value, habit and perceived security in smartwatch usage. The use of smartwatches was found to influence environmental sustainability significantly. However, the results did not support the association between effort expectancy, facilitating conditions and hedonic motivation with smartwatch use. The ANN results further complement these outcomes by showing that habit with a normalized importance of 100% is the most significant factor influencing smartwatch use.

Theoretically, this research broadens the UTAUT2 by introducing smartwatch features as external variables and environmental sustainability as a new outcome of technology use. On a practical level, the study offers insights for various stakeholders interested in smartwatch use and their environmental implications.

Mobile health (mHealth) technologies, in particular, have been sought after and advocated as a means of dealing with the pandemic situation. Despite the obvious advantages of mHealth, which include monitoring and exchanging health information via mobile applications, mHealth adoption has yet to take off exponentially. Expanding on the unified theory of acceptance and use of technology (UTAUT) model, this study aims to better comprehend consumers’ receptivity to mHealth even after the pandemic has subsided.

Through purposive sampling, data were collected from a sample of 345 mobile phone users and analysed using partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) capture both linear and nonlinear relationships.

Effort expectancy, performance expectancy, social influence, pandemic fear and trustworthiness positively influenced mHealth adoption intention, with the model demonstrating high predictive power from both the PLSpredict and ANN assessments.

The importance–performance map analysis (IPMA) results showed that social influence had great importance for mHealth uptake, but demonstrated low performance.

Referrals are an alternative that policymakers and mHealth service providers should think about to increase uptake. Overall, this study provides theoretical and practical insights that contribute to the advancement of digital healthcare, aligning with the pursuit of Sustainable Development Goal 3 (SDG 3) (good health and well-being).

This study has clarified both linear and nonlinear relationships among the factors influencing intentions to adopt mHealth. The findings from both PLS and ANN were juxtaposed, demonstrating consistent findings.