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The intricate and unpredictable nature arising in higher education institutions (HEIs) implementing technology-driven change for e-learning environments demands a much closer examination of individuals’ interpretations and interactions as they undergo these changes. Through a micro-foundational lens, this study aims to examine the micro-level inhibitors and supporting factors of individual’s readiness for change by investigating technology-driven change in HEIs.

A two-phased research design using grounded theory methodology was used to collect and analyse data incorporated within a single-case study in an HEI. Data was collected using semi-structured interviews with 22 participants, followed by a focus group with eight participants centered on factors affecting their readiness for change during technology-driven change in e-learning environments. The data analysis followed an iterative constant comparative approach and its three-phased coding process: open, axial and selective coding.

This study revealed that staff with little awareness of the organisations expectations towards a technology-driven change or who are poorly communicated with can exhibit higher resistance and lower individual readiness for change. While macro-level factors of organisational structure can contribute to the success or failure of technology-driven change, the fundamental features related to individuals’ readiness for change are integral to understanding the micro-level causal behaviours underlying these macro-level phenomena.

The dominant model of change is often focused on drivers that are centered on examining macro-level constructs rather than individuals who are facing the change. This study presents theories on readiness for change and offers a micro-foundational view to bring new perspectives into the current literature on how individual-level micro-foundations enhance understanding of technological change in HEIs.

This paper aims to examine the effect of external Sharia audit on the performance of Islamic banks in Bahrain and Oman, which are countries that implement it. This study aims to explore the role of external Sharia audit in preventing prohibited profits and mitigating the risks of noncompliance with Sharia principles.

This paper opted a quantitative approach and collected data from the employees of the Sharia Supervision Board & Sharia Audit in the Islamic banks. This paper studied how external Sharia audit affects the financial profitability and compliance with Islamic Sharia principles of Islamic banks, using partial least squares structural equation modeling technique.

The results indicated that external Sharia audit had a significant positive effect on both financial profitability and compliance with Islamic Sharia principles in the Islamic banks under study. This means that external Sharia audit enhances the financial performance and the adherence to Islamic Sharia principles of the Islamic banks.

This study has some limitations that suggest directions for future research, such as expanding the sample to other countries and measuring more performance indicators for Islamic banks.

This study suggests that external Sharia audit enhances the performance and compliance of Islamic banks and urges the regulators to adopt it and standardize it.

This study contributes to the literature on Islamic finance and external Sharia audit by providing empirical evidence on the impact of external Sharia audit on the performance of Islamic banks.

Stakeholders' uncertainty about firms' value drives their urge to get information, as well as managerial disclosure choices. In this study, the authors examine whether and how an important source of uncertainty – the recent COVID-19 pandemic's effect on corporate social responsibility (CSR) disclosure – is beyond managerial and stakeholders' control.

The authors develop a novel construct for daily CSR disclosure by employing computer-aided text analysis (CATA) on the press releases issued by 125 New Zealand Stock Exchange (NZX) listed from 28 February 2020 to 31 December 2020. To capture COVID-19 intensity, the authors use the growth rate of the population-adjusted cumulative sum of confirmed cases in New Zealand on a specific day. To examine the association between the COVID-19 outbreak and companies' CSR disclosure, the authors employed ordinary least squares (OLS) regression by clustering standard error at the firm level.

The authors find a one standard deviation increase in the COVID-19 outbreak leads to a 28% increase in such disclosures. These results remained robust to a series of sensitivity tests and continue to hold after accounting for potential endogeneity concerns. In the channel analysis, the study demonstrates that the positive relationship between COVID-19 and CSR disclosure is more pronounced in the presence of a well-structured board (i.e. a large, more independent board and with a higher proportion of women on it). In further analysis, the authors find the documented relationship varies over the pandemic's life cycle and is moderated by government stringency response, peer CSR pressure and media coverage.

This paper is the first study that contributes to the scant literature examining the impact of the COVID-19 outbreak on CSR disclosure. Prior research either investigates the relationship of the CSR-stock return during the COVID-19 market crisis or examines the relationship between corporate characteristics including the quality of financial information and the reactions of stock returns during COVID-19. The authors extend such studies by providing empirical evidence that managers respond to COVID-19 by increasing CSR disclosure.

It is still unclear how Asian universities incorporate the theory or practice of sustainable development (SD) in their research and education programmes. To address this gap, the purpose of this paper is to report on a study that has examined how universities in Asian countries handle and address matters related to SD.

The study used a bibliometric analysis and an online survey-method. The online survey data were analysed through descriptive analysis and one-sample student’s t-test.

The study indicates that there is considerable variation among the Asian countries regarding sustainability practices in higher education institutions (HEIs). The HEIs in far eastern countries, such as Indonesia, Malaysia and Thailand are perceived to demonstrate more sustainability practices.

Even though a substantial number of participants participated in the survey, it did not cover all Asian countries. The online survey was carried out over a limited period of time, and not all HEIs in the field may have received information about the study.

Asia is the largest continent facing a number of sustainability challenges. In this context, the contribution of HEIs is very important. The findings of the current study may serve as a baseline for Asian HEIs to take more initiatives towards SD goals, as HEIs are responsible for the education and training of hundreds of thousands of students who will be occupying key positions in industry, government or education in the coming years.

The study contributes to the existing literature in two distinct ways. First, it was possible to develop a comprehensive instrument to measure sustainability practices in HEIs. Second, this study has filled the gap of the scarcity of studies regarding sustainability practices in HEIs in Asia.

This study aims to identify, review and synthesize existing literature on key theories, drivers and barriers affecting consumer adoption or resistance to artificial intelligence (AI) in the hospitality sector.

This study aims to conduct a complete literature review of the accrued knowledge generated so far on AI in the hospitality sector. To attain the overall objectives of this study, we used the systematic literature review (SLR) method. This method systematically handles the diversity of knowledge in a specific topic to answer precise research questions. It also generates new visions through a synthesis of the literature, to identify the knowledge gaps, set the new directions for the future researcher and provide sufficient guidance to inform the policy and practice.

The findings of this study are presented in three sections, as follows: descriptive analysis, content analysis and synthesized framework. The findings highlighted the state-of-the-art mapping of the existing research in terms of publication frequency over time and across publication outlets, key theories, methods and geographies. In addition, literature on consumer adoption (or resistance) of AI in hospitality is content analyzed to highlight key drivers and barriers. Moreover, this review critically evaluates extant literature and sets future agendas by postulating specific research questions for further knowledge development in this field of study.

The SLR focused on consumer adoption or resistance to use AI in hospitality literature. The future researcher may include additional streams to get better results.

The study findings will help multiple stakeholders to understand the underlying causes of customer resistance or barriers to the intention to use/adopt AI services in the hotel sector. Furthermore, study results will allow them to better analyze the relationship between customer barriers, intents or consumer decision behaviors.

First, this study provides a comprehensive synthesis of the literature on the consumer adoption or resistance of AI in hospitality. This study categorizes the existing diversified literature in two main themes – drivers and barriers – to present a simplistic picture of the existing literature. Second, the review highlights the gaps and limitations in existing research and provides guidance for future scholars. Third, the key contribution of this review is the development of a unified framework on the consumer adoption or resistance of AI in the hospitality sector. That is, this study puts forward the behavioral reasoning theory framework and suggests that future research using this lens will immensely contribute to existing literature. Finally, this study facilitates the practitioners to understand the key motivating and hindering factors affecting the adoption and resistance behavior.

Tourism development has been perceived as a promoter of city restoration and can also affect the post-war city reconstruction. Questions on how to reconstruct ruined buildings and urban areas through a post-war tourism-oriented approach based on the expectations of residents and tourists profound answers. The purpose of this paper is to adopt the sequential mixed method (qualitative and quantitative) with purposive sampling which is a non-probability method to investigate tourism-oriented approaches in the reconstruction of buildings and landmarks as the core components of urban tourism.

The study adopted the sequential mixed method (qualitative and quantitative) to investigate tourism-oriented approaches in the reconstruction of buildings and landmarks as the core components of urban tourism.

The findings of the study point that the preferred strategy for the reconstruction of damaged symbolic building is the preservation of the war effects in regard maintaining the buildings’ history to be considered by urban policy makers, urban designers, and authorities.

The constraint was associated with the time-consuming nature of this type of research. Original documents of the research context and all the interview data were in the Persian language, making the translating process a time-consuming matter. Furthermore, data collection in the area located near the Iran-Iraq border (500 meters) presented a number of security caveats as limitations.

The research found a majority of tourists and the residents preferred tourism zone where the combination of post-war and natural attraction across riverside area. In other word, most considerable post-war attractions are those that combined with the appeal of the other tourism potentials like eco-leisure tourism. The preferred strategy for the reconstruction of damaged building reconstruction as post-war tourism attractions is the preservation of the war effects in regard maintaining the buildings history rather than reconstruction as the most likely to pre-war conditions with less attention paid to the war effects.

Through focusing on the Peruvian context, this paper aims to identify the main determinants of the continuity of entrepreneurial activity among women artisans.

The empirical sample comprises semi-structured interviews with 28 women artisans in Peru during their participation in a fair organized by the Peruvian Ministry of Culture in Lima (Peru). Thematic analysis was subsequently used to determine the main ideas in the transcripts from the interviews conducted.

The findings empirically identified the following job-related (number of work hours, perceived income, future of artisanal jobs), functional (availability and relevance of workstations, the necessity to travel) and socio-cultural determinants (government support, perceived recognition, level of affiliation with Peruvian traditions) as the main drivers of the continuity of entrepreneurial activities among women artisans.

This paper contributes by filling a gap in the literature on women entrepreneurship and artisan entrepreneurship in which empirical studies of Latin American women artisans continuing with their entrepreneurial activities have been limited so far.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。

This paper aims to evaluate the extent of materiality assessment disclosures in sustainability reports and their determinants. The study examines the disclosure practices of listed companies based in the member states of the Cooperation Council for the Arab States of the Gulf, colloquially referred to as the Gulf Cooperation Council (GCC).

First, the materiality assessment disclosures were scored through a content analysis of sustainability reports published by listed GCC companies during a five-year period from 2013 to 2017. Second, a fixed effect ordered logic regression was used to examine the determinants of materiality assessment disclosures.

While sustainability reporting rates improved across the sample period, a significant majority of listed GCC companies do not engage in sustainability reporting. The use of internationally recognised standards has also declined. While reporters provide more information on their materiality assessment, the number of sustainability reports that offer information on how the reporter identifies material issues has declined. These trends potentially indicate the existence of managerial capture. Materiality assessment disclosure scores are positively influenced by higher financial performance (Return on Assets), lower leverage and better corporate governance. However, company size and market-to-book ratio do not influence materiality assessment disclosures.

The findings may prove useful to managers responsible for preparing sustainability reports who can benefit from the examples of materiality assessment disclosures. An evaluation of the materiality assessment should be included in the scope of assurance engagements and practitioners can use the examples of best practice when evaluating sustainability reports. Stock exchanges may consider developing improved corporate governance guidelines as these will lead to materiality assessment disclosures.

The findings may assist in improving sustainability reporting quality, through better materiality assessment disclosures. This will allow corporate stakeholders to evaluate the reporting entities underlying processes, which leads to transparency and corporate accountability. Improved corporate sustainability reporting supports the GCC commitment to implement the United Nations Sustainable Development Goals and transition to sustainable development.

This study addresses the call for greater research examining materiality within a sustainability reporting context. This is the first paper to examine sustainability reporting quality in the GCC region, focussing particularly on materiality assessment disclosures.