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In today’s business landscape, drawing upon the critical role of environmental sustainability, this study investigates the intricate relationship between green human resource management practices (GHRMP), big data analytics capability (BDAC), green competitive advantage (GCA) and environmental performance (EP), further moderated by managerial environmental concern (MEC).

This study employs a quantitative approach using the latest version of SmartPLS 4 version 4.0.9.6 on a data sample of 467 participants representing a diverse range of manufacturing SMEs. Data were collected from managers and directors using a structured questionnaire and analyzed using structural equation modeling (SEM). This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a comprehensive understanding of how these practices enhance SME`s sustainability.

The findings provide valuable insights into the manufacturing sector, aiming to enhance SMEs' green competitive advantage. Implementing GHRMP fosters environmental awareness within the workforce, and building BDAC allows for effectively translating that GHRMP into actionable insights, maximizing the potential for achieving GCA. Furthermore, recognizing MEC’s moderating role strengthens positive environmental outcomes associated with GCA. The findings confirm that GHRMP and BDAC are valuable resources and key drivers contributing to competitive advantage in sustainability of enterprises.

For SMEs, our findings suggest that strategically integrating GHRMP with BDAC not only boosts environmental stewardship but also improves operational efficiency and market positioning. This research outlines actionable steps for SMEs aiming to achieve sustainability targets while enhancing profitability. This research provides actionable insights for SMEs in strategic decision-making and policy formulation, aiding SMEs in navigating the complexities of sustainable development effectively.

This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a robust theoretical explanation of how HRM practices and BDAC help SMEs gain green competitiveness. The implication of this study reveals that SMEs implementing and integrating green HRM practices with advanced data analytics are more likely to gain competitive advantage. This study draws theoretical support from the resource-based view (RBV) theory, positing that a firm’s sustainable competitive advantage stems from its unique and valuable resources and capabilities that are difficult for competitors to imitate or substitute.

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.

In this digital age, the rapid technological innovation and adoption, with the increasing use of big data analytics, has raised concerns about the ability of small and medium enterprises (SMEs) to sustain the competition and innovation performance (IP). To narrow the research gap, this paper investigates the role of big data analytics capability (BDAC) in moderating the relationship between digital innovation (DI) and SME innovation performance.

This research has been carried forward through a detailed theory and literature analysis. Data were analyzed through confirmatory factor analysis and structural equation models using a two-stage approach in smartPLS-4.

Results highlight that digital service capability (DSC) significantly mediates the relationship between DI and IP. Additionally, value co-creation (VCC) directly affects digital transformation (DT), while DI has a stronger effect on DSC than IP. Furthermore, BDAC significantly moderates the relation between DSC → IP and DT → IP, whereas it has a detrimental effect on the relation between DI and IP. In addition to that, VCC, DSC, DT, DI and BDAC have a direct, significant and positive effect on IP.

This research was motivated by the practical relevance of supporting SMEs in adopting DT and the resource-based view (RBV) and technology acceptance model (TAM). This study shows that all direct and indirect measures significantly affect innovation performance, including BDAC as moderator. These findings refresh the perspective on what DT, DI, VCC, DSC and BDAC can bring to a firm's innovation performance.

This paper has contributed to DT by empirically validating a theoretical argument that suggests the acceptance and adoption of new technology. This paper aims to fill theoretical gaps in understanding BDAC and DT by incorporating the RBV and TAM theories on BDAC and DT.

The purpose of this study is to assess how managerial capability affects artificial intelligence (AI) adoption and employee well-being now in a dynamic context of organizational change. This study investigated the role that managerial capability and organizational support play in facilitating successful AI technology implementation within organizations. The study seeks to provide an integrated perspective on how organizations can help mitigate the effects of AI anxiety and improve the well-being of employees.

A survey questionnaire was administered to collect data from 324 employees and managers working in small- and medium-sized enterprises (SMEs) located in Pakistan. Partial least squares-structural equation modeling (PLS-SEM) was employed using Smart PLS version 4.1.0.3 to analyze the relationships between the study variables.

The findings of the study show that AI anxiety can significantly impact employee well-being. However, the relationship was moderated by organizational support. When organizational support was high, the effects of AI anxiety decline on employee well-being.

This study offers three important implications; it adds to our understanding regarding AI adoption and its effect on employee well-being by addressing how managerial interventions may facilitate the smooth integration of AI technology and examining the moderating effect that organizational support might have over the association between anxiety and employee well-being. Additionally, we have offered a nuanced view of the potential impact of AI adoption on employees and offered practical recommendations for organizations to undertake to address AI anxiety and promote employee well-being during AI implementation.

In the era of digitalization, digital technology has transformed businesses and created enormous opportunities for organizations worldwide. Unsurprisingly, research on digital transformation has garnered significant interest among academics in recent decades. However, this study aims to recognize the key and holistic antecedents influencing digital transformation in manufacturing firms. This study also investigates the indirect relationships of antecedents with firm performance.

The hypothesis was investigated using the partial least squares structural equation modeling (PLS-SEM) approach. The data was collected from 279 employees through a self-administered survey of manufacturing firms.

The results described a significant and positive impact of competitive pressure, leadership role, organization culture, organization mindfulness, government regulation, and IT readiness on digital transformation and firm performance. Furthermore, digital transformation partially mediates the relationship between antecedents and firm performance.

The study finds a holistic perspective of the critical antecedents of digital transformation using the mediation role of digital transformation and moderating effects of firm agility. Additionally, all antecedents have a significant association with Firm Performance.

This study aims to critically analyze existing research on blockchain technology adoption, examining the dominant models and methodologies used, the primary domains where blockchain is applied and the emerging opportunities across various sectors.

Using a methodical systematic review approach, the authors meticulously examined a pool of 1,322 collected articles, subjecting 38 studies to rigorous assessment. Through this comprehensive analysis, the authors unveiled the key models and influential factors that intricately shape the trajectory of blockchain adoption.

The primary models identified for investigating blockchain adoption were the technology acceptance model and technology–organization–environment. Apart from the core variables within these models, the pivotal determinants influencing various blockchain applications include perceived trust, perceived cost and social influence. In addition, this study highlights supply chain management as a prominent domain for blockchain application adoption.

Understanding these influential factors and models can guide practical decisions and aid stakeholders in formulating effective strategies for blockchain adoption in diverse sectors.

This study contributes to advancing the understanding of blockchain adoption dynamics by unveiling the prevalent models and determinants shaping adoption. This study offers valuable insights into the factors influencing the use and adoption of blockchain technologies across diverse sectors.

The rapid evolution of technological infrastructure and analytical capabilities has facilitated the integration of big data analytics (BDA) across various sectors. This study aims to investigate the suitability of implementing BDA within academic libraries, addressing the demanding need for effective data utilization in contemporary educational environments.

The research is grounded in five critical components: data-driven culture, organizational infrastructure, employee responsibilities, management capabilities and the successful deployment of technology for BDA. An extensive literature review led to the development of a Likert scale-based questionnaire distributed on social media to collect data from university librarians in Pakistan. The authors were able to collect the data from 211 librarians. Descriptive statistics were employed to analyze the variables, while confirmatory factor analysis (CFA) was conducted using the AMOS to validate the research model.

The findings from the measurement model reveal significant positive correlations among all five components, underscoring their collective importance in facilitating the implementation of BDA. This formation is essential for addressing the evolving needs and academic requirements of users in the context of big data in a digital environment.

The study acknowledges limitations about its focus on a single country’s perspective, which may affect the generalizability of the findings regarding the implementation process of BDA in academic libraries.

This research contributes to the existing body of knowledge by highlighting the practices and capabilities of librarians in the era of big data as well as the requisite organizational infrastructure for the effective implementation of analytics in academic libraries.