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This paper aims to determine the gender composition of journal editorial boards in the field of library and information science and to identify trends in the gender composition of different editorial roles and the country of affiliation and occupation of people fulfilling these roles.

In an analysis of 13 selected Library Information Science journals published by the Emerald Publishing group, data relating to 549 editors and editorial board members were obtained from the Open Editors online database. Data were assessed by role, gender, country and continent of their affiliation, and occupation.

Women were found to be under-represented as editors and editorial board members in 10 of the 13 journals. This was most evident in the highest-ranking role of editor or editor-in-chief. The majority of editors and editorial board members were from English-speaking countries located in Europe and the Americas, followed by Asia. The vast majority of editorial personnel belonged to the teaching and learning profession, with relatively few support staff, or researchers taking on these roles.

The findings of this study highlight the gender inequality in prestigious and career-advancing academic roles across multiple research areas. To the best of the authors’ knowledge, no such research has yet been conducted in the field of library and information science.

We examine how superstition shapes corporate tax avoidance and do so by taking a risk perspective and focusing on the zodiac-year belief prevalent in China.

We adopt a difference-in-differences research design to compare the degree of corporate tax avoidance in the CEOs’ zodiac year with that in the adjacent years. We do propensity-score matching to form a sample of Chinese listed firms for the regression analysis.

We find causal evidence that firms exhibit a greater magnitude of tax avoidance in the CEOs’ zodiac years, a result attributable to relatively weak tax enforcement in the Chinese context. We also find that the zodiac-year effect on corporate tax avoidance is more pronounced for firms with tight financial constraints, firms with high business risk, firms headquartered in regions with a high degree of superstition and non-state-owned firms.

This study is the first to show that superstition is a determinant factor of tax avoidance and contributes to the tax literature by shedding light on the behavioral risk factors that shape corporate tax avoidance. We take the perspective of CEOs’ risk appetite to analyze how tax avoidance is influenced by the CEOs’ trade-off between the costs and benefits of avoiding taxes. Our results suggest that, when CEOs are more risk-averse, they attach more importance to financial risk than the risk of reputational losses and litigation associated with corporate tax avoidance. The findings imply that tax avoidance can be curbed by increasing (or decreasing) the tax (financial) risk confronting the CEOs.

The primary purpose is to empower financial institutions in AI integration decisions. By combining QSFS and the Golden Cut technique, the study establishes a robust foundation for assessing AI progress effects, aligning implementation with performance goals, and promoting technical innovation. Dimensions explored include AI-related workforce competency, technological adaption, and ethical AI practices, crucial components within the BSC framework for technological innovation.

This study employs a distinctive approach, integrating the Balanced Scorecard (BSC) framework with Quantum Spherical Fuzzy Sets (QSFS) and the Golden Cut approach to explore the dynamic landscape of AI deployment. The integration addresses uncertainties, enhancing impact assessment accuracy amid ambiguity associated with AI outcomes. QSFS and the Golden Cut technique together facilitate precise identification of thresholds and crucial values.

The research delves into the intricate relationship between enduring financial stability and AI progress, recognizing technology's crucial influence on financial decision-making. Findings underscore technology's significant impact on financial institutions' AI integration decisions. This novel approach provides a strong quantitative basis, offering insights into workforce competency, technological adaption, and ethical AI practices.

Despite valuable contributions, the study acknowledges limitations, such as potential biases and generalizability concerns, emphasizing the need for cautious interpretation and suggesting future research directions. Recognizing the research's boundaries and complexities in studying AI deployment in financial institutions underscores the need for ongoing exploration.

The research's originality lies in presenting an innovative methodology, integrating BSC, QSFS, and the Golden Cut, providing a unique perspective for decision-making. Contributions extend beyond academia, offering practical insights to enhance AI strategic implementation in the financial industry. This novel approach enriches the technology and finance discourse, fostering theoretical and practical advancements.

This study aims to investigate the dynamic interaction between interest rates and commercial property financing offered by Islamic banks in Malaysia.

The authors use the autoregressive distributed lag (ARDL) cointegration methodology to analyse the short- and long-run effect of the interest rates and rental rates on commercial property financing of Islamic banks in Malaysia between 2010: Q1 and 2018: Q2.

The findings reveal that changes in interest rates affect Islamic commercial property financing. This indicates that Islamic banks still rely on interest rates as a benchmark without fully implementing Islamic rental rates. This corroborates the subsequent finding, where overnight policy rates influence commercial property financing.

Despite the authors’ attempt to provide insights into Islamic commercial property financing, the study is limited to secondary data; further research can use survey information to obtain other details that are not included in this study. Similarly, this study does not cover the operation and financial lease debate in Musharakah Mutanaqisah. Future studies can examine the challenges faced by the financial institution towards implementing rental rates in other emerging and developing countries using a different methodology.

This study is the first to investigate the dynamic changes in overnight policy rates, average lending rates and rental rates on Islamic commercial property financing in Malaysia using ARDL techniques. The authors uncover the research and institutional implications of Islamic commercial property financing rates and provide policy and future research directions coupled with the proposed modified rental rate to be developed.

This research extends self-congruity theory and assesses the predictive validity of the triad personality congruence among brand, brand-related sustainability initiatives (BSI) and self-concept (BSSC: brand-sustainability-self-congruence) on consumers’ brand evaluation.

Three studies assessed BSSC using the brand personality scale (BPS) and the direct congruence measure (DCM). Through moderated mediation analyses, BSSC effects were examined on consumers’ behavioral intention, behavior and subjective well-being (SWB). The mediating role of brand trust and the moderating role of brand/BSI types and consumer characteristics were also assessed.

BSSC increased consumers’ brand trust, behavioral intention, behavior and SWB. Data based on BPS revealed impactful attributes that increase/decrease BSSC levels across brand-BSI combinations and the moderating role of sustainability involvement and income to enhance BSSC effects. BSSC was particularly effective, according to DCM.

For BSI planning, strategic consideration of BSSC based on both BPS and DCM is recommended. Thus, managers may predict the psychological impact of BSI and align its attributes to increase consumers’ brand evaluation.

In the sustainable marketing context, this research discusses BSSC – triad personality congruence – based on BPS and DCM and its predictive effects on consumers’ short-term brand evaluation, their actual behavior and SWB, a long-term life evaluation. The results imply a possible variation in consumers’ information processing according to the congruence measurement approach. Thus, it is relevant to the research on self-congruity, sustainability, marketing, consumer psychology/behavior and well-being.

The study aims to synthesize existing knowledge and proposes a research framework for building a resilient supply chain (SC) through artificial intelligence (AI) technology. It also identifies existing literature gaps and paves the way for a future research agenda.

A systematic literature review has been carried out to identify the peer-reviewed articles from Scopus and Web of Science databases. Then, the selected articles published between 2012 and 2023 are analyzed using descriptive and thematic analysis methods to unearth research gaps and offer new research directions.

Descriptive and thematic analysis reveals the overall development of literature on the role of AI for supply chain resilience (SCR). Based on the findings of the thematic analysis, the motivation, application, capability and outcome (MACO) framework has been developed and propositions have been proposed. Several future research directions have also been suggested in terms of theory, context and methodology (TCM).

The study provides a fresh perspective on the integration of AI technology within the realm of SCR. The developed MACO framework serves as a practical tool for supply chain management (SCM) professionals, offering a nuanced understanding of AI's applications across various functional areas to streamline operations, minimize waste and optimize resource utilization, thereby helping them in strategic planning.

This study contributes to the literature on the role of AI for building SCR by uncovering gaps, offering research directions and developing propositions for future research directions.