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AI-powered digital human avatar influencer (DHAI) is a digitally created character with a human-like appearance and noteworthy social media presence. They mimic human behavior through form, behavior and emotional realism. However, there have been varied viewpoints in the literature about the effect of DHAI realism on consumer response. Therefore, this study aims to examine the effect of form, behavioral and emotional realism on consumer engagement and parasocial relationships that further affect attachment toward DHAI and brand, with the moderating effect of content authenticity.

Using a cross-sectional design, 426 respondents in India were asked to visit the Instagram page of a specific DHAI identified through a pretest study. The authors used the Smart PLS 4.0 version to examine the hypotheses.

Accordingly, based on the social presence theory, the findings of the quantitative study indicated that DHAI’s form, behavioral and emotional realism positively influence customers’ engagement with DHAI, but only the behavior and emotional realism of DHAI positively affect the parasocial relationship. Further, perceived DHAI’s content authenticity moderates the effect of DHAI engagement and parasocial relationship on DHAI sentimental attachment.

This study provides novel and practical insights for developing DHAI by considering realism characteristics for enhanced customer engagement, parasocial relationship and attachment toward DHAI and brands.

The resource-based view (RBV) emphasises the importance of resources for firm performance. However, recent research argues that the focus on firm performance should also be based on inside-out (IO) and outside-in (OI) capabilities. Specifically, we study the importance of resources on product development (an IO) and market driving (an OI) entrepreneurial marketing capabilities on entrepreneurial firm performance in an emerging market. The study further investigates the moderating effects of marketing agility on the relationship between resources and capabilities.

The study is based on survey data of a multi-industry sample of 102 entrepreneurial firms in Pakistan.

The results show that marketing agility moderates the relationship between resource-mix flexibility on product development and market driving capabilities, but it only positively moderates the relationship between resource-mix inimitability and product development capability. Marketing driving and product development capabilities play a role as parallel mediators between resources and firm performance.

The study lies at the intersection of marketing and entrepreneurship literature by (1) providing a nuanced understanding of marketing agility as a boundary spanning factor for IO and OI entrepreneurial marketing capabilities; (2) integrating the resource types and product development from IO and market-driving from OI capabilities perspectives; (3) identifying the effects of IO and OI on firm performance providing guidance for entrepreneurs seeking improved firm performance.

This chapter explores the transformative potential of integrating Artificial Intelligence (AI) with virtual reality (VR) in developing adaptive learning and development (L&D) programmes. Traditional L&D methodologies are increasingly inadequate in the face of rapidly changing business environments. AI and VR technologies offer unprecedented opportunities to personalise learning experiences, enhance engagement and improve outcomes. This chapter provides a comprehensive overview of current trends, applications, challenges and future directions of AI and VR in L&D. Key findings emphasise the role of these technologies in fostering continuous learning cultures, addressing individual learner needs and enhancing organisational effectiveness. Practical insights and case studies are included to guide HR professionals in leveraging AI and VR for innovative and effective L&D programmes.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

Purpose: The study aims to explore the profound impact of virtual currencies and decentralised finance (DeFi) protocols on financial dynamics, user engagement, and operational aspects within the Metaverse.

Methodology: This research employs mathematical modelling and quantitative analysis to comprehensively investigate the pivotal roles of these elements within the dynamic virtual environment.

Findings: The mathematical equations applied in our study have illuminated the intricate mechanics of financial expansion, operational efficiencies, and user dynamics in the Metaverse’s virtual currency and DeFi systems. These insights underline the transformative influence of these digital ecosystems on future economies, emphasising the critical role of quantitative analysis in navigating and maximising their potential.

Significance: This research aims to shed light on the pivotal roles of virtual currencies and DeFi protocols through mathematical modelling and quantitative analysis. It contributes to a deeper understanding of their significance in shaping the future of virtual economies and financial interactions within the ever-evolving Metaverse.

To remain competitive in an unpredictable environment where the complexity and frequency of cybercrime are rapidly increasing, a cyber resiliency strategy is vital for business continuity. However, one of the barriers to improving cyber resilience is that security defense and accident recovery do not combine efficaciously, as embodied by emphasizing cyber security defense strategies, leaving firms ill-prepared to respond to attacks. The present study thus develops an expected resilience framework to assess cyber resilience, analyze cyber security defense and recovery investment strategies and balance security investment allocation strategies.

Based on the expected utility theory, this paper presents an expected resilience framework, including an expected investment resilience model and an expected profit resilience model that directly addresses the optimal joint investment decisions between defense and recovery. The effects of linear and nonlinear recovery functions, risk interdependence and cyber insurance on defense and recovery investment are also analyzed.

According to the findings, increasing the defense investment coefficient reduces defense and recovery investment while increasing the expected resilience. The nonlinear recovery function requires a smaller defense investment and overall security investment than the linear one, reflecting the former’s advantages in lowering cybersecurity costs. Moreover, risk interdependence has positive externalities for boosting defense and recovery investment, meaning that the expected profit resilience model can reduce free-riding behavior in security investments. Insurance creates moral hazard for firms by lowering defensive investment, yet after purchasing insurance, expanded coverage and cost-effectiveness incentivize firms to increase defense and recovery spending, respectively.

The paper is innovative in its methodology as it offers an expected cyber resilience framework for integrating defense and recovery investment and their effects on security investment allocation, which is crucial for building cybersecurity resilience but receives little attention in cybersecurity economics. It also provides theoretical advances for cyber resilience assessment and optimum investment allocation in other fields, such as cyber-physical systems, power and water infrastructure – moving from a resilience triangle metric to an expected utility theory-based method.

Data quality assurance (DQA) is essential for enabling the sharing and reuse of research data, especially given the increasing focus on data transparency, reproducibility, credibility and validity in research. Although the literature on research data curation is vast, there remains a lack of theory-guided exploration of DQA modeling in research data repositories (RDRs).

This study addresses this gap by examining 12 distinct cases of DQA-related knowledge organization tools, including four metadata vocabularies, three metadata schemas, one ontology and four standards used to guide DQA work in RDRs.

The study analyzed the cases utilizing a theoretical framework based on activity theory and data quality literature and synthesized a model and a knowledge artifact, a DQA ontology (DQAO, Lee et al., 2024), that encodes a DQA theory for RDRs. The ontology includes 127 classes, 44 object properties, 7 data properties and 18 instances. The article also uses problem scenarios to illustrate how the DQAO can be integrated into the FAIR ecosystem.

The study provides valuable insights into DQA theory and practice in RDRs and offers a DQA ontology for designing, evaluating and integrating DQA workflows within RDRs.

Prior work has shown that the board of directors can alleviate market imperfections that lead to capital investment inefficiency. The authors extend previous work by exploring how board characteristics influence the efficiency of human capital investment, a critical production factor that has remained insufficiently examined. Specifically, this study aims to investigate how board activity, size, the presence of a separate chairman, female directors and board independence affect firm labour investment efficiency in the European context.

The sample contains 4,331 firm-year observations traded on the STOXX® Europe 600 index from 2009 through 2022. This paper applies a lagged ordinary least squares (OLS) regression to test the proposed hypotheses. It also uses a dynamic panel generalised method of moments (GMM) regression to tackle potential endogeneity concerns.

The results show that board gender diversity and the level of independent directors are positively linked to labour investment efficiency, whereas board size and meeting frequency are negatively related to labour investment efficiency. Meanwhile, the presence of a separate chairman on the board does not appear to be significantly associated with labour investment efficiency. In additional subgroup analyses, the authors find that board gender diversity mitigates managers’ inclinations towards both overinvestment and underinvestment in labour. The authors also find that the level of independent directors helps greatly in reducing the underinvestment in labour, while it fails to attenuate the overinvestment in labour. Moreover, the authors find board size to be significantly associated with the tendency to make suboptimal labour decisions, manifesting as both overinvestment and underinvestment in labour. Finally, the results show that board meetings are significantly associated with overinvestment problems, while underinvestment problems seem to be unrelated to meeting frequency.

The empirical results have implications for policymakers and market participants in Europe. Firstly, firms may improve the efficiency of their labour investments by increasing directors’ independence and adding more female voices to corporate boards. Secondly, the evidence shows that some board attributes, such as board activity and size, do not necessarily have a beneficial impact on corporate decisions, particularly labour investment decisions. Finally, market participants are likely to benefit from this paper by understanding the role of board attributes in promoting the efficient allocation of firm resources.

This paper makes two significant contributions. Firstly, it extends the literature on the role of boards of directors in shaping corporate decision-making processes, particularly concerning human capital investment decisions within European firms. By doing so, the authors provide new evidence confirming that certain board attributes, such as board size, director independence and board gender diversity, are important for optimising firms’ resource allocation. Secondly, although numerous studies investigate boards’ role in capital investment decisions, relatively few empirical studies exist on the role of boards in labour investment decisions. This paper, therefore, tries to tackle this void in the literature by investigating firms’ decision-making concerning labour investments.

The purpose of this study is to provide a comprehensive exploration of academic research on halal purchasing decisions and consumer behavior by integrating bibliometric and systematic review methodologies.

This study uses a multi-method approach, combining bibliometric and systematic review methodologies, to comprehensively analyze the domain of halal purchasing decisions and consumer behavior. A data set of 184 articles published between 2007 and 2024 was sourced from the Scopus database. The bibliometric analysis was conducted using Bibliometrix in R, facilitating performance analysis, science mapping and network analysis to explore key authors, affiliations, collaborations and thematic trends. Additionally, the systematic review examined the limitations and future research areas discussed in prior studies, providing the basis for formulating potential research questions to address identified gaps.

The study identifies significant contributions within the domain of halal purchasing decisions and consumer behavior, emphasizing the critical roles of religiosity, trust and halal certification as dominant themes. Bibliometric analysis reveals key authors, influential publications and collaborative networks, highlighting Malaysia as a central hub for research in this field. Additionally, the analysis underscores the intellectual structure and thematic evolution, identifying underexplored areas such as non-Muslim perspectives, emerging halal industries and geographic diversity. The systematic review complements these insights by addressing recurring methodological and theoretical limitations, offering targeted recommendations for future research.

This research uniquely combines bibliometric and systematic review methodologies to provide a comprehensive review of the halal consumer behavior literature, identifying limitations and gaps in prior studies and proposing actionable areas for future research.

This review investigates the effects of temporal aggregation and systematic sampling on time-series analysis, focusing on their influence on data accuracy, interpretability and statistical properties. The purpose of the study is to synthesise existing literature on the topic and offer insights into the trade-offs between these data reduction techniques.

The research methodology is based on an extensive review of theoretical and empirical studies covering univariate and multivariate time series models, focusing on unit roots, ARIMA, GARCH, cointegration properties and Granger Causality.

The key findings reveal that while temporal aggregation simplifies data by emphasising long-term trends, it can obscure short-term fluctuations, potentially leading to biases in analysis. Similarly, systematic sampling enhances computational efficiency but risks information loss, especially in non-stationary data, and may result in biased samples if sampling intervals coincide with data periodicity. The review highlights the complexities and trade-offs involved in applying these methods, particularly in fields like economic forecasting, climate modelling and financial analysis.

The originality and value of this study lie in its comprehensive synthesis of the impacts of these techniques across various time series properties. It underscores the importance of context-specific applications to preserve data integrity, offering recommendations for best practices in the use of temporal aggregation and systematic sampling in time-series analysis.