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This study explores luxury wine hospitality by considering (1) physical activities and (2) activities created by integrating the physical domain with digital technology. In doing so, it aims to identify the different types of wine tourism-related luxury experiences and build a framework for interpreting hybrid luxury experiences in wine hospitality in the digital era.

An explorative mixed-methods approach was adopted to investigate types of luxury wine hospitality using cluster analysis and in-depth interviews with producers of wines with controlled and guaranteed designation of origin in Italy’s Sangiovese area.

This study presents a framework for understanding hybrid digital and physical experiences in wine hospitality by examining the core components of luxury experiences. We identify six types of luxury experiences in wine hospitality that combine a physical experiential component with varying degrees of integration with digital technologies.

Our findings (1) provide wine businesses operating in hospitality within the luxury segment with a useful tool for optimising the integration of digital technology into physical experiences to add value to visitors’ activities and (2) highlight the importance of digital skills for wineries that organise luxury experiences.

This study systematises the integration of digital technologies into physical activities related to wine hospitality. It presents a hybrid physical–digital analytical framework that adopts an experiential outline of the strategic design of wine hospitality businesses.

The present study aims to compare the effect of international paid remittances on financial development in three Gulf Cooperation Council (GCC) countries from 1985 to 2020.

The study applied the bound cointegration technique and the autoregressive distributed lag (ARDL) method for long- and short-run estimations as well as diagnostic tests to increase robustness.

The ARDL long-run results showed that international paid remittances had a significant negative effect on financial development in Oman and Saudi Arabia but an insignificant negative effect in Bahrain. The error correction model for the short run of the ARDL slowdown model showed that international paid remittances had a significant positive effect on financial development in Oman, Bahrain, and Saudi Arabia.

Few studies have examined remittance outflows from GCC countries, which are enriched by oil wealth and located in one of the most stable geographical areas in the world. The findings from this study can help policymakers understand how to enable remittances and investments in order to establish regulations that will preserve remittance inflows and meet target services.

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 study aims to examine the effect of implementing and marketing Islamic bank corporate social responsibility (CSR) on the performance of Islamic banks moderated by the community’s religious commitment.

The research was conducted at branch offices of Islamic banks in Indonesia with a sample of 251 branches. The questionnaire was distributed for six months. All variables are measured using previous studies’ instruments and Smart PLS 3.0 to analyze the data.

This study found that CSR implementation had a direct effect, while CSR evaluation and CSR marketing had no significant effect on performance. The results also show that community religious commitment negatively moderates the effect of CSR evaluation on performance and positively moderates the effect of CSR marketing on performance.

This research contributes to the literature on Islamic marketing and financial services management by reporting that CSR marketing in the context of a religious society has a significant effect on improving branch bank performance. Hence, this study expands the understanding of stakeholder and environmental contingency theories by assessing society’s role and perceptions in determining Islamic banks’ performance.

Practically, this research helps bank managers to encourage people to become Islamic bank customers using CSR information.

This study reveals the role of community religious commitment as a moderator for the effectiveness of CSR marketing in improving performance. Previous research has never examined the role of this variable.

The present study investigates the customers' behavioural intention to use voice-based artificial intelligence (AI) to find the appropriate hotels and resorts in an emerging nation. This study determines the influences of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to obtain the appropriate information and find the location of the hotels and resorts.

This study used a purposive sampling method for collecting data from the respondents, who are customers of the hotels and resorts in Bangladesh. A self-administered survey questionnaire was used to obtain responses from 378 respondents. After collecting the data, the reliability and validity of the constructs and hypotheses were tested via partial least squares structural equation modelling (PLS-SEM).

The findings of the study indicate that information quality, system quality, privacy and novelty value have a positive and significant impact on attitude and behavioural intention to use voice-based AI assistant services in an emerging nation. However, system quality does not significantly influence behavioural intention to use voice-based AI assistant but it has an indirect significant influence on behavioural intention through the mediation effect of attitude.

The study’s findings provide essential guidelines for practitioners to understand the impacts of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to find the appropriate hotels and resorts to meet customers' needs and expectations.

This study contributes to the existing literature on technology adoption by highlighting the interconnectedness of various factors influencing users' behavioural intentions. The study’s focus on an emerging nation provides a valuable theoretical contribution. It highlights that user perceptions and attitudes towards technology adoption may differ from those in developed nations due to unique contextual factors.

Nigeria’s quest to industrialize has received major attention in recent times. Special economic zones (SEZs) are thus recognized as capable of providing opportunities for firms to improve innovation capability and export performance. Limited empirical evidence exists on their economic impacts, particularly in terms of promoting innovation and exporting. This makes it difficult to provide accurate impact estimates or identify the channels through which the SEZ affects firm behavior. This study aims to specifically establish the Nigerian pattern of firm behavior in SEZs to improve the understanding of African SEZs and the appropriate incentives that can foster industrialization.

The study used primary data from a survey of firms in Nigeria’s active SEZs and in-depth interviews of major stakeholders. The primary data was collected through a semi-structured questionnaire administered to firms within the selected SEZs and an in-depth interview guide. The sampled SEZs were in Lagos, Ogun, Kano and Cross River States. In-depth interviews were conducted with the zone management, Nigerian Export Processing Zones Authority officials and the Manufacturers Association of Nigeria. Data were analyzed using descriptive statistics, content analyses of policy documents, principal component analysis, logit regression and two-stage least squares regression.

The results suggest that the number of incentives offered to SEZ firms does not matter for improved firm performance in terms of innovation and exports. Firms’ export performance is poor because firms do not comply with the basic tenet of SEZ as an area dedicated to free trade and consequent mandatory export of manufactures and services from the free trade enclave. Although firms in the Nigerian SEZs are considered to be fairly innovative, it is important to note that there is no evidence of innovation that is new to the world among these firms.

The research team could not visit the zones in the Northeast region of the country due to insecurity. Some of the zones had also become inactive at the time of the survey, and the response rate from firms was very low. This limited the number of sampled firms.

The findings imply that the NEPZA Act needs to be revised to reflect the contextual realities of SEZs in Nigeria. The policies and details of each incentive offered by the government should be made clear to investors. For example, the permission given for SEZ firms to sell 100% of their manufactured, assembled or imported goods into the domestic market should be revisited as it obviously affects innovation and exports negatively. This would tremendously help SEZ firms fulfill their mandate of promoting exports, improving FDI attraction, enhancing industrialization and promoting economic competitiveness.

Previous studies have either focused on how SEZs affect innovation or influence export performance without accounting for the role of incentives in shaping innovation and export performance. This study fills this knowledge gap by providing evidence on the type of incentives for firms in Nigeria’s SEZs, investigating their effects (or otherwise) on innovation activities at the firm level and ascertaining how improvement in innovation capacity arising from the incentives affects firms’ export performance.

Entrepreneurial practice often relies on the minimum viable product (MVP) to test business model hypotheses, yet the conceptualization of its makeup remains inadequately defined, particularly in uncertain startup contexts. This paper aims to clarify how entrepreneurs should think about MVPs in terms of their uses, goals and essential components to enhance their effectiveness as a tool for navigating uncertainty.

Existing literature on MVPs is broad and often overlapping, leading to confusion regarding essential components and best practices for MVP development. Through a systematic analysis of existing published definitions, this paper offers clarity by proposing a framework that breaks the MVP concept up into process, goals and the fundamental elements necessary to launch an MVP. By doing so, it provides actionable guidance for entrepreneurs seeking to utilize MVPs in their business model testing endeavors.

This conceptual paper critically examines the lean startup approach, seeking to disentangle the complexities surrounding MVP development. Drawing on existing literature and practical insights, the study identifies and articulates a framework that clarifies the MVP concept along with the core elements required for creating a viable MVP, including (a) artistic elements, (b) a robust distribution channel and (c) an effective user feedback mechanism.

This paper contributes to theory development and pedagogical practices by providing a structured framework for understanding and implementing MVPs in entrepreneurial contexts. By identifying the minimal category elements of an MVP, it offers practical insights into entrepreneurs and educators alike, facilitating effective business model hypothesis testing in varied and uncertain environments.

This research aims to contribute to the ongoing debate on the prospective advantages of implementing accrual accounting in countries where such a transition is underway, with a focus on Italian municipalities. The research seeks to ascertain the requisite conditions, in public sector accounting mangers’ perception, for a useful transition from modified cash accounting to full accrual accounting within the Italian context.

The methodology adopted is Qualitative Comparative Analysis, which involves conducting a survey through semi-structured interviews with accounting managers in municipal accounting departments. The sample is drawn from municipalities in the Emilia-Romagna region with populations exceeding 15,000 inhabitants.

The study shows that some stakeholders have a tangible demand for financial statement information. They believe that accrual accounting statements provide accurate insights into municipal financial health and that these statements are comparable across municipalities. All these factors together constitute sufficient conditions for considering useful the implementation of accrual accounting in local governments, in the opinion of public sector accounting managers.

This paper contextualises the broader international debate on transitioning to full accrual accounting in the New Institutional Sociology framework. The Qualitative Comparative Analysis is an underutilised methodology within the field of public sector accounting, and the public sector accounting managers’ point of view is scarcely investigated in literature.

CSR authenticity reflects the perceived trustworthiness of CSR activities. Despite its importance, research on CSR authenticity in virtual contexts remains limited. This study investigates how specific dimensions of CSR authenticity influence consumer engagement in virtual CSR initiatives by focusing on the mediating role of cognitive cause proximity (i.e. cause salience and cause relevance).

Data were collected through a structured online survey on Chinese social media, which yielded 389 valid responses. Structural equation modelling using AMOS 24 was employed to test the proposed hypotheses.

The results show that perceived benevolence and the broad impact of CSR authenticity positively affect consumer engagement in virtual CSR. Significantly, cause relevance mediates the relationship between the broad impact of CSR and CSR engagement. However, cause salience unexpectedly reduces consumer engagement in virtual CSR initiatives.

This study extends construal level theory (CLT) and social exchange theory (SET) to virtual corporate social responsibility, offering fresh insights into how consumers respond to authentic CSR efforts. Practitioners can enhance CSR authenticity by highlighting its broad impact and benevolence to drive engagement while being cautious about overemphasising cause salience, which may risk alienating consumers.

The aim of the paper is to process clients’ online reviews of 20 spa locations in the Slovak Republic in the form of a case study. The purpose is to identify and specify the shortcomings of Slovak spa resorts as well as the needs and preferences of their clientele.

To obtain data, Google text reviews were scraped using the Instant Data Scraper. The text mining approach was used for the analysis of 5,670 reviews (using the RapidMiner Studio and Atlas.ti tools).

The performed sentiment analysis of the reviews has determined that 4,807 of the reviews were positive, 476 were neutral and 387 were of negative sentiment. The subsequently processed word list states the most frequently mentioned nouns and adjectives. Detailed opinion mining identifies the areas/phrases with the most frequent occurrence of the negative sentiment in their reviews.

The stated results are important from the perspective of service quality management in order to improve the satisfaction of spa customers. Sentiment analysis of Google Maps reviews for the target clientele remains unexplored and represents a novel area of study.