This study examines the influence of various firm-specific factors on overall ESG performance disclosure and its three dimensions, namely, Environment, Social, and Governance, on listed companies from environmentally sensitive sectors in India. This study considered the sustainability (ESG) scores of the selected companies for a period of 10 years, from 2013 to 2022. This study employs a panel data regression analysis. The results of the study revealed that firm characteristics such as age and size have a significant positive impact, whereas leverage has a significant negative influence on the overall ESG performance disclosure of selected companies. Similar results are obtained for the environmental dimension, and profitability is also found to have a negative impact on it. Firm age, size, and liquidity are found to have a significant positive influence on social performance disclosure, whereas leverage has a significant negative effect. Surprisingly, Government ownership is found to have a significant negative influence on governance disclosure.

Purpose: The study investigates the factors that impact the adoption of learning management systems (LMSs) among educators for effective implementation of open and distance learning (ODL) environment in advanced technological institutes (ATIs).Design/methodology/approach: This study uses the extended technology acceptance model (TAM) and analyses data using the partial least square–based structural equation modelling approach to validate the construct and test proposed hypotheses. Data were collected through an online questionnaire from the respondents.Findings: This study reveals that perceived self-efficacy and job relevance significantly impact perceived usefulness (PU) and perceived ease of use (PEU). PU, PEU and service quality significantly impact attitudes of educators, which impact their behavioural intention and actual use of LMS as a chain reaction.Practical implications: The management should organise hands-on training sessions to improve educators' computer self-efficacy and explain the importance of the LMS and its features to offer an effective ODL environment for delivering high-quality education.Originality/value: The previous studies focused on LMS use from the students' point of view rather than educators. This study investigates educators' LMS adoption in ATIs using the extended TAM. The findings may be helpful for management to implement an effective ODL environment that offers fully integrated distance learning and e-learning during the prevailing COVID-19 pandemic.

Purpose: There is a significant decrease in students' attendance in Zoom classes compared to traditional classes. This paper investigates the factors that affect students' attitudes, behavioral intentions and actual use of Zoom for online classes at higher educational institutions (HEIs) in Sri Lanka.Design/methodology/approach: This research uses the technology acceptance model (TAM) as a theoretical model. The data are collected from HEI students via an online survey form. The hypotheses between constructs in the model are tested using partial least squared–structural equation model.Findings: The analysis shows that computer self-efficacy (CSE) affects perceived usefulness (PU) and perceived ease of use (PEU), which affects attitude (ATT) and behavioral intention (BI) and actual use (AU) of Zoom in a chain reaction. Further, PEU affects PU, which, in turn, affects BI. Furthermore, the effect size of PU to BI is larger than ATT to BI.Practical implications: Students' attendance for Zoom classes mainly depends on CSE, PU and PEU. Therefore, HEIs should promote Zoom with interactive training before teaching online. Further, officials should revise the curriculum in schools to upsurge the CSE of students.Originality/value: During coronavirus-19, no research was published on students' use of Zoom for online classes in the Sri Lankan context. Moreover, the TAM model has been modified by including CSE as an external variable.

The objective of this study is to analyze metaverse topics and education through bibliometric methods to identify research trends, leading researchers, institutions, and prevalent themes from the past decade (2010–2020) and developing themes for the next five years (2021–2024). This study followed the PRISMA framework to extract data from the Scopus and Web of Science (WoS) databases, resulting in 475 publications. Biblioshiny was used for descriptive bibliometric analysis and thematic mapping, whereas VOS Viewer co-occurrence and trending analysis were used to identify clusters and trending topics in this domain. Thematic mapping and cluster analysis revealed that Virtual Reality (VR) and Augmented Reality (AR) technologies are the dominant forces shaping the future evolution of education in a metaverse context. The co-occurrence analysis revealed that future studies could focus on customized education, dynamic teaching methods, and a sustainability-oriented educational framework. This study suggests the potential of an interactive, dynamic, and scalable learning environment using metaverse learning. Policymakers, educators, and developers should establish collaborations, enhance infrastructure, and remove barriers to realize this potential. By combining the results from both the Scopus and WoS databases, this study offers a thorough literature overview of the applications of the metaverse in learning and development while proposing future research areas for academics to address existing gaps and explore the potential of metaverse technology in this field.

Purpose: Artificial intelligence (AI) has transformed marketing operations, creating new benchmarks for operational productivity, customer interaction and sales growth. This study investigates factors that affect the adoption of AI among marketing professionals, focusing on developing benchmarking archetypes and assessing the moderating impact of technology resistance (TR).Design/methodology/approach: Data from 353 marketing professionals across diverse sectors in Sri Lanka was analyzed using a dual-method approach. The UTAUT2 model guided hypotheses tested with PLS-SEM to establish generalizable benchmarks, while fuzzy-set qualitative comparative analysis (fsQCA) was employed to identify distinct adoption archetypes serving as configurational benchmarks.Findings: All the UTAUT2 factors significantly influence AI adoption, with TR as a substantial barrier. The fsQCA revealed seven distinct benchmarking archetypes, with behavioral intention, effort expectancy, facilitating conditions, hedonic motivation and price value emerging as core conditions for high adoption, while performance expectancy, social influence and habit functioning as peripheral factors.Practical implications: The research provides diagnostic benchmarking tools that organizations can use to assess their AI readiness, identify implementation pathways aligned with their contextual characteristics, reduce technology resistance and enhance marketing efficiencies.Originality/value: This study advances benchmarking literature by identifying both generalizable adoption drivers and distinct configurational archetypes for AI implementation in marketing while establishing technology resistance as a critical moderating variable.

Purpose: This study aims to comprehensively examine the relationship between initial public offering (IPO) activities and macroeconomic factors in Sri Lanka. Design/methodology/approach: This study uses principal component analysis (PCA) and autoregressive distributed lag (ARDL) techniques to examine the relationship between IPO activities and macroeconomic factors. Ten macroeconomic variables are transformed into principal components (factors) using PCA. Then, ARDL is applied to investigate the long- and short-term relationships between IPO activities and the transformed macroeconomic factors. Findings: The empirical investigation identifies three principal factors from the ten macroeconomic variables, of which two factors have a significant long-run association with IPO activities: “return on investment (RTOI)” and “economic and market development (ECMD).” In the short run, “trade openness and banking sector development (TOBD)” and RTOI are significantly associated with IPO activities. Research limitations/implications: The study was based on 30 years of observations, which passed all diagnostic tests but may be insufficient for generalizing the findings. Future studies could use high-frequency data (monthly or quarterly) to increase the number of observations and repeat the method and analysis. Also, while the symmetrical ARDL method was used in this study, an asymmetrical ARDL method may provide more insightful results and interpretations. Practical implications: The study highlights the importance of considering both long- and short-term associations when analyzing the impact of macroeconomic variables on IPO activities. Originality/value: This study is the first to comprehensively examine the relationship between IPO activities and macroeconomic variables using PCA and the ARDL technique. The study provides insight into the macroeconomic factors that influence IPO activities in Sri Lanka and highlights the importance of considering long- and short-term associations.