Hospitality Business Research Articles

Digital transformation in hospitality: the role of AI in enhancing business through gastronomic offerings

The impact of AI on the hospitality industry is increasingly significant. This paper explores the influence of digital transformation on the hospitality sector, with a particular focus on the role of artificial intelligence (AI) in enhancing business operations through gastronomic offerings. Digital technology is transforming the way hospitality establishments operate, offering new opportunities for optimizing operations, personalizing customer experiences, and efficiently managing resources. By analyzing the application of AI technologies, such as predictive analytics, personalized marketing, and automated ordering systems, the paper addresses how hospitality businesses can improve their competitive position and guest satisfaction. The study employs both qualitative and quantitative research methods to present successful examples of AI integration into business models, highlighting the benefits, challenges, and prospects for future development. Managers from 64 restaurants across the Republic of Serbia participated in the research, and the findings demonstrate the clear potential of artificial intelligence to transform hospitality, creating sustainable and innovative business practices.

Children as a “Left Behind” group by outdoor hospitality businesses (Campsites): A human resources perspective

Through better recruitment of Children’s representatives working in mini-clubs, outdoor hospitality businesses have a further opportunity to both enhance the well-being of children while also addressing a major societal issue such as sustainability. The findings of the research suggest that the content of job advert for children’s representatives should be recalibrating with a focus on philosophy of the organisation (responsible management education), which will then inform the type of knowledge wished to be conveyed (sustainability), how (case method), and the profile (qualifications, experience, personality, etc) of the staff needed. The findings of the research also highlight the fact that job crafting, which used to be solely a coping strategy used by staff, is also a coping strategy used by managers in their recruitment strategy. There is an urge for the development of qualifications/courses in Youth, Society and Sustainable Futures to prevent staff and managers developing their own coping strategies, which might negatively impact the performance of the organization.

The Role of Social Media Marketing in the Tourism and Hospitality Industry: A study on Bangladesh

At present, the hospitality industry has started to follow a social media marketing strategy as part of its marketing promotion to improve its brand value. This paper aims to find the various social media sites available in the hospitality industry. This paper seeks to identify the appropriate social media as well as suggest its benefit in the context of Bangladesh. It also aims to identify the importance of tourism marketing to reach actual and potential consumers. A mixed (qualitative and quantitative) method was used in the collection of primary data, whereas the random sampling method was used to obtain such data. This study also included elicited qualitative questions so as to accommodate understanding of the customer psychographics factors. The study will also determine the role social media marketing plays and how it operates in the tourism and hospitality arena, using a wide range of literature from academic articles, journals, and books. The research sample size is 270. The results of the study show that Facebook is one of the most favorable media for promoting the hospitality business. The study also tried to find out various aspects of respondent’s basic information as well as their personal choices and views.

Moral intelligence among Muslim employees in hospitality settings: scale development and validation

PurposeAs upscale hotels have guests from diverse backgrounds and cultures, their Muslim employees should respect each. Consequently, this paper seeks to develop a multi-dimensional scale that measures Muslim employees’ moral intelligence within the hospitality domain.Design/methodology/approachThe following dimensions: fear of Allah, humility, generosity and tolerance were included in this new scale. To develop this scale, this paper incorporates a multistage approach.FindingsEmploying ADANCO-PLS v.2.4, the nomological validity of this new scale was examined. These dimensions will help Muslim employees in hospitality contexts enhance their moral intelligence, which can result in their skill development, increased job delight and retention since they will feel appreciated and encouraged toward their ethical growth.Practical implicationsUpscale hotels can benefit by creating workplace cultures of giving for guest experiences. When Muslim employees are ready to take it up a notch, they contribute exceptional guest experiences and deeper relationships. This leads to higher levels of customer loyalty rates with revisit intentions.Originality/valueGiven moral judgments are essential in hospitality businesses; this paper highlighted using moral intelligence attitudes among Muslim employees.

Artificial Intelligence Driven and Sustainability of Hospitality Businesses in Owerri, Imo State, Nigeria

The study explored the relationship between Artificial Intelligence and the sustainability of hospitality businesses in Owerri, Imo State, Nigeria. The study specifically sought to examine the relationship between AI and the environmental sustainability of hospitality businesses in Owerri, Imo State, Nigeria, and to ascertain the relationship between AI and the social sustainability of hospitality businesses in Owerri, Imo State, Nigeria. The study adopted descriptive survey research design and data were collected from 203 respondents with the aid of a structured questionnaire and hypotheses were tested using Pearson Product Moment Correlation Coefficient with the aid of Statistical Package for Social Sciences (SPSS, version 27). Findings revealed that there is a significant positive relationship between AI and the environmental sustainability of hospitality businesses in Owerri, Imo State, Nigeria, with r = 0.882 n = 203 and a p-value of 0.001 (p<0.05). Also, there is a positive significant relationship between AI and the social sustainability of hospitality businesses in Owerri, Imo State, Nigeria, with r = 0.801, n = 203, and a p-value of 0.000 (p<0.05). The study concluded that there is a statistically significant positive relationship between Artificial Intelligence and the sustainability of hospitality businesses in Owerri, Imo State, Nigeria. The study recommended that hospitality businesses in Owerri should adopt AI technologies to enhance environmental sustainability. Also, hospitality businesses in Owerri should leverage AI-driven customer service chatbots and employee engagement platforms to enhance social sustainability.

The Impact of Pairing Local Food and Wine on the Sustainability of Hospitality Businesses in the Wine Region of Srem (Vojvodina, R. Serbia)

This research examined how food and wine pairing affects the sustainability in local hospitality settings. This study focused on hospitality employees’ attitudes in Srem, where it explored the importance of integrating local produce, the economic and environmental benefits of reduced food miles, and customer satisfaction through authentic experiences. Key research questions investigated the perceived value of food–wine pairing, factors that influenced sustainable practices, and employee motivation. Data were gathered via a structured survey that highlighted challenges like limited ingredient availability and high costs. The results show that the employees recognized the importance of sustainability but displayed limited motivation for further initiatives. The findings suggest that enhancing local sourcing can bolster economic resilience and cultural preservation, promoting a sustainable tourism model for the region. This study recommends additional research on ecological impacts and local community benefits to further embed sustainability in hospitality practices.

What Is the Impact of Digital Tools on Hospitality Business Performance? The Case of SME Accommodation Sector in Portugal

ABSTRACTDespite numerous studies on digital tools in tourism and hospitality, few quantitative investigations use an integrated set of these tools to enhance business success. This study examines the impact of digital tools on hospitality business performance, using data from 234 SMEs in Portugal's accommodation sector. Findings show that integrating tourist accommodation into business groups positively influences digital tool adoption. Higher investment in business digitalisation also positively affects tool adoption and overall performance. The most valued digital tools by Portuguese SMEs include updated websites, social networks, online store/reservation systems, and digital marketing. This study provides valuable insights for tourism marketers and business managers to enhance SMEs' competitiveness in the hospitality sector.

A Classification Pipeline Methodology for Predicting Personality Traits

With multiple advancements in machine learning and deep learning a multitude of tasks that were either too complex to hardcode or even regarded as impossible can now be performed. This technology can be used to our advantage to determine someone's personality based solely on their perception and opinions. Nowadays, a lot of tasks involving the victim's personality are necessary for criminal activity surveillance, personality has started to play a significant role in mental health therapy as well as hospitality businesses are trying to monitor the personality of customers to provide better services. We can assist in the completion of such a work by combining deep learning with a classification-based machine learning model connected by a data pipeline. The use case can also be extended to hospitality businesses to understand the mindset of the patient and is also useful during selection of candidates for a particular job profile based on their personality traits. It is also used in many successful marketing campaigns to target specific audiences. We propose a combined classification pipeline that employs both unsupervised and supervised deep learning approaches such as K-Means along with LSTM, GRU, RNN and Autoencoders (Deep Embedded Clustering) to draw a comparative analysis between these approaches to analyze personalities successfully.

From Traditional to Digital: The Evolution of Business Models in Hospitality Through Platforms

The hospitality business model has undergone profound changes influenced by successive waves of technological advances and platforms. This paper explores how different waves of technologies and platforms are shaping the evolution of the hospitality business model. Employing a systematic literature review, this research analyzes how platform innovation has driven significant shifts in the industry. The analysis yielded four key findings. Firstly, the incorporation of digital technologies resulted in a radical transformation of conventional business models, prompting substantial alterations in marketing strategies, distribution processes, and consumer behavior patterns. Secondly, the industry has undergone five significant technological revolutions, evolving from steam-powered transportation to the integration of artificial intelligence (AI) and the Internet of Things (IoT), thereby driving a shift towards innovation-centric business models. Thirdly, the advent of digital platforms, most notably online travel agencies (OTAs), posed a challenge to traditional hospitality models by introducing personalized experiences and disrupting established market norms. While digital platforms offer advantages such as enhanced convenience and customization, they also present challenges, including intensified competition and declining profit margins. Nonetheless, technological advancements such as AI, virtual reality (VR), and augmented reality (AR) present a potential avenue for redefining value propositions and enhancing customer experiences. Fourth, the future of the hospitality business model is poised for further disruption, influenced by the emergence of new virtual platforms and services. This could potentially result in a reshaping of the industry’s value chain and value proposition.

Toward Sustainable Performance in the Hotel Food Supply Chain: Influences of Quality Management Practices and Digital Integration

This study addresses a gap in the literature on how quality management practices within the food supply chain can be effectively integrated with digital technologies to enhance Food Supply Chain Sustainable Performance (FSCSP) in the hospitality and tourism sector in Egypt. Thus, a quantitative approach was employed, with data being collected through both online and in-person surveys from employees with sufficient knowledge of their hotel’s food products, food supply chain, and quality management practices in five-star hotels in Egypt. SPSS and WarpPLS-SEM techniques were used to analyze the research data. The findings showed that Food Supply Chain Quality Management (FSCQM) practices positively influence both FSCSP and Supply Chain Digital Integration (SCDI), with SCDI serving as a mediator in the relationship between FSCQM and FSCSP. This study is helpful for hospitality businesses in developing economies because it shows how important it is to combine digital innovation with strong quality management to improve operating efficiency and gain a competitive edge in terms of sustainability. This involves fostering a technology-driven culture that encourages creativity, innovation, transparency, and information-sharing among employees to ensure the successful integration of FSCQM practices with hotels’ digital competences.

Predicting hotel booking cancellations using tree-based neural network.

In the hospitality business, cancellations negatively affect the precise estimation of revenue management. With today's powerful computational advances, it is feasible to develop a model to predict cancellations to reduce the risks for business owners. Although these models have not yet been tested in real-world conditions, several prototypes were developed and deployed in two hotels. The their main goal was to study how these models could be incorporated into a decision support system and to assess their influence on demand-management decisions. In our study, we introduce a tree-based neural network (TNN) that combines a tree-based learning algorithm with a feed-forward neural network as a computational method for predicting hotel booking cancellation. Experimental results indicated that the TNN model significantly improved the predictive power on two benchmark datasets compared to tree-based models and baseline artificial neural networks alone. Also, the preliminary success of our study confirmed that tree-based neural networks are promising in dealing with tabular data.

Organization learning and business resilience in developing hospitality industry

The study aims to investigate the effect of organizational learning on business resilience in developing the hospitality industry, specifically focusing on four-star hotels in the southeast region of Nigeria. One of the major reasons why this research was conducted is because of the vacuum that exists between organizational learning and business resilience in the hospitality industry. A cross-sectional survey design was employed because it supports using questionnaires to collect data from participants. A validated and reliable instrument was used to collect data from two hundred thirty one participants drawn from the selected hospitality industry in the southern part of Nigeria. The results demonstrate that organizational learning significantly positively affects business resilience. Organizational learning also affects crisis anticipation and recoverability which are crucial to be resilient in the industry. The results of this research supported prior empirical findings and supported both social learning theory and resilience theory. Regarding originality, the geographical scope and the industry where this research was conducted distinguish it from other prior empirical investigations. It was recommended that since the hospitality business is highly competitive, managers should utilize organizational learning as a strategy to overcome their competitors and remain sustainable in the industry. The findings of the research will contribute to the literature and hospitality industry in developing country contexts.

Implementation of Environmental Accounting Policies in Hospitality Business in an Effort to Improve Tourism Sustainability in the Lake Toba Region

The Lake Toba area is an iconic tourist destination in Indonesia that faces major challenges in preserving its ecosystem. Stakeholders, especially hotel companies, need to be able to develop more effective strategies to achieve sustainable tourism in the Lake Toba area. The aim of this research is to find out the relationship between hotel companies' environmental accounting policies and driving factors, including hotel companies' corporate social responsibility (CSR) programmes, management support and community support. The research method used in this research is mixed method research (qualitative and quantitative). Analysis of qualitative data processing using Nvivo. Then, quantitative analysis using data analysis methods, namely by testing questionnaires, validity, reliability, data normality, multicollinearity, heteroscedasticity, multiple regression analysis, and testing the coefficient of determination of the best fit model. The results of the qualitative research with Nvivo based on the concept of tourism sustainability are CSR, environmental issues, public image and environmental accounting policies. The results of the quantitative research based on the partial test showed that CSR and management support had a significant effect on the accounting policy of the hotel business environment, while the community support variable had no effect on the accounting policy of the hotel business environment.

Role of Technological Advancements on Customer Satisfaction in Japan’s Hospitality

Purpose: The aim of the study was to analyze the role of technological advancements on customer satisfaction in Japan’s hospitality. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Technological advancements have significantly enhanced customer satisfaction in Japan’s hospitality industry by improving service efficiency, personalization, and overall guest experience. Innovations such as AI-driven customer service robots, automated check-in systems, and smart hotel rooms equipped with IoT devices offer convenience and seamless interactions for guests. These technologies reduce waiting times, streamline operations, and allow for personalized services, such as customized room settings and tailored recommendations, increasing guest satisfaction. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), service quality model (SERVQUAL) & expectancy-confirmation theory (ECT) may be used to anchor future studies on the role of technological advancements on customer satisfaction in Japan’s hospitality. Hospitality businesses should adopt integrated management systems that combine customer relationship management (CRM), property management systems (PMS), and online booking platforms. Industry regulators should promote standards for technology integration in the hospitality sector.

Future workforce skills for hospitality and tourism business in Thailand

Currently, the hospitality and tourism industry in Thailand is at a state of great flux. This, therefore, calls for a highly competent and versatile workforce to deal with this situation. For this paper, we shall attempt at providing an overview of the current skill set from the current workforce involved in the hospitality and tourism industry in Thailand. We also attempt to identify the prevailing opinions of the managerial personnel on different work aspects, which include the perception of the skills that Thai workers possess. Moreover, the paper looks at what the managers would want and hope for in the future workforce, and the strategies applied to develop such skills in workers soon. This qualitative research approach is employed to dig deep into the minds of key decision makers in Thailand by way of in-depth, one-on-one, or small group interviews to document and consolidate the insights and viewpoints of distinguished executives and top-level decision makers belonging to prestigious hotel and tourism conglomerates in Thailand. The findings of our research unmistakably indicate that the hospitality industry in the country is doubtless in a growth and prosperous period, where it successfully meets various and constantly changing preferences among tourists coming for different purposes. With the tourism industry in Thailand mushrooming, it is vital to establish what has contributed to such success. With more and more people globe-trotting and with tourists pouring in droves into Thailand, the need for a skilled workforce within the hospitality industries is at an all-time high. It therefore becomes relevant during these times of change to identify the skillset of the personnel at work within this vibrant industry. Deep inside the minds of managerial professionals, we can gain a clear insight into how they perceive the current workforce. By conducting comprehensive interviews on an individual and small group basis, we hope to capture the capability and strengths of the Thai workforce with a great degree of subtlety. Second, we also attempt to find out what aspirations and expectations the managers have from the future workforce. The resulting skills and competencies will have to be identified as the industry continues to evolve, but by exploring strategies utilized to develop such skilled labor, we expose the preparations being made for the workforce of tomorrow. Our qualitative research methodology enables us to interact directly with key decision-makers in the industry. We have thus gathered much information and opinion by involving those senior executives and senior-level decision makers of reputed hotel and tourism conglomerates. Their valuable insights helped us to closely look into the current scenario and aspects which still needed to be improved. Our findings clearly revealed that the hospitality industry in Thailand is one of the fastest growing and most successful sectors. It has satisfactorily adapted to the increasing and changing tastes and preferences of the tourists coming from different parts of the world. On the other hand, innovation and high-quality service have allowed this industry to satisfactorily address the needs and desires of its guests. Conclusion Our study puts light on the change which the hospitality and tourism industry of Thailand is experiencing at present. The following sections provide a broad outline of the skillset of the workforce, based on in-depth interviews with managerial professionals. We have taken a closer look at what industry leaders expect and/or hope for in the future workforce, and how they are developing the skills necessary to meet those expectations. This research supports the notion that the hospitality industry is increasing its growth to meet the demands of a multifaceted global customer base.

Credit Cost Management and Financial Performance of Hotel Businesses in Kenya

This study explores the impact of credit cost management on the financial performance of businesses in Kenya's hospitality sector. Specifically, it examines how interest rates, loan collateral, and loan repayment terms influence the profitability and financial stability of small and medium-sized enterprises (SMEs) in the industry. The study is supported by Loanable funds theory, Credit scorecards theory and Tradeoff theory. Through a desk review of existing literature, the study identifies that high interest rates increase the cost of borrowing, creating financial pressure on businesses, while lower rates support profitability. Loan collateral is found to be a major determinant of access to credit, with businesses that provide sufficient collateral securing loans more easily. Flexible loan repayment terms contribute to better cash flow management and liquidity, improving overall financial performance. The study concludes that effective credit cost management is critical for the success of hospitality businesses and recommends that financial institutions and policymakers collaborate to provide more favorable credit terms, including lower interest rates, reduced collateral requirements, and more flexible repayment options. Increased transparency in loan agreements and government interventions to support SMEs are also essential for fostering financial growth in the sector.

The Role of Service Quality in Improving Customer Satisfaction: A Study in the Hospitality Business of University

This research aims to analyze the dimensions of Service Quality, analyze service attributes, and analyze the influence of Service Quality on Customer Satisfaction at the Hospitality Business. Universitas Terbuka (UT), as a State University with Legal Entity (SU-LE) in Indonesia, operates various business units, including hospitality services such as guesthouses and meeting facilities. This research focuses on analyzing the service quality and customer satisfaction within these hospitality services of UT. UT aims to improve non-tuition revenue streams through its business operations, making the quality of its hospitality services a crucial aspect of its overall performance. Respondents who completed this study were as many as 379 customers of guesthouses and meeting facilities. The data used in this study is primary data sourced from questionnaires distributed to customers. The research methods used are gap analysis, IPA analysis, and the Structural Equation Model. The variables that influence customer satisfaction are tangible and responsiveness variables, while the variables reliable, assurance and empathy do not influence guest house UT customer satisfaction. This research contributes to the attributes of service quality and customer satisfaction in a state university hospitality business. This study is an exploration of each customer's preferences regarding perceived satisfaction and the level of importance expected for the services provided by hospitality businesses.

A Demand-Driven Analysis of the Determinants of P2P Accommodation hosts’ Business Performance: Scale Development and Validation

ABSTRACT Hosts of peer-to-peer (P2P) accommodations often struggle to accurately assess guests’ perceptions of their performance due to the lack of valid measurement scales. This study aims to address this gap by developing and validating a 20-item scale to measure P2P hosts’ business performance as perceived by their guests. The scale encompasses five domains: living environment, authenticity, platform incentives, guests’ civility, and amenity quality. This study also examines the predictive validity of these dimensions by assessing their effects on customer satisfaction, which varies by traveling party. By enhancing the understanding of P2P accommodation performance measurement and extending cue utilization theory, this study provides valuable insights for future research on hospitality business performance and effective performance evaluation in the P2P accommodation sector.

A Novel Data Analytics Methodology for Discovering Behavioral Risk Profiles: The Case of Diners During a Pandemic

Understanding tourist profiles and behaviors during health pandemics is key to better preparedness for unforeseen future outbreaks, particularly for tourism and hospitality businesses. This study develops and applies a novel data analytics methodology to gain insights into the health risk reduction behavior of restaurant diners/patrons during their dining out experiences in a pandemic. The methodology builds on data relating to four constructs (question categories) and measurements (questions and attributes), with the constructs being worry, health risk prevention behavior, health risk reduction behavior, and demographic characteristics. As a unique contribution, the methodology generates a behavioral typology by identifying risk profiles, which are expressed as one- and two-level decision rules. For example, the results highlighted the significance of restaurants’ adherence to cautionary measures and diners’ perception of seclusion. These and other factors enable a multifaceted analysis, typology, and understanding of diners’ risk profiles, offering valuable guidance for developing managerial strategies and skill development programs to promote safer dining experiences during pandemics. Besides yielding novel types of insights through rules, another practical contribution of the research is the development of a public web-based analytics dashboard for interactive insight discovery and decision support.

Forecasting Occupancy Rate using Neural Network and Decision Tree at Hotel X

Occupancy rate is a critical factor in a hotel because it is used to measure the operational success of a hotel. The higher the hotel occupancy rate, the more successful the hotel business is in generating revenue. In the hospitality business, occupancy rates are very important to monitor and analyse as marketing strategies and pricing policies. This study compares the forecasting of occupancy rates at Hotel X using neural network method and decision tree method. The dataset used in this study is room available, room sold, and available occupancy percentage data at Hotel X from April 2018 to June 2023. The simulation was carried out by dividing the data into training data and testing data with a percentage ratio of 70:30, 75:25, 80:20, 85:15, and 90:10. The forecasting results that have been carried out using a neural network with one hidden layer have an optimal RSME result of 0.010 for split data of 70%:30% and 80%:20% while using a neural network with two hidden layers the optimal result of RSME is 0.013 for split data of 75%:25%. Forecasting results using decision tree RSME optimal results of 0.022 for split data 85%:15% and 90%:10%. From this forecasting, the most optimal results use a neural network with one hidden layer for data splits of 70%:30% and 80%:20% with RSME results of 0.010. The results of the research can be used by Hotel X as a policy determination in the next hotel management.