Manager perceptions of big data reliability in hotel revenue management decision making

Egan, Dave; Haynes, Natalie

doi:10.1108/ijqrm-02-2018-0056

Cited by 27 publications

(23 citation statements)

References 38 publications

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“…Another limitation is that we could find only one article (Egan and Haynes, 2019) that analyzes how managers interact with these systems. That empirical study uses a three-stage iterative thematic technique (Nowell et al, 2017) to facilitate rigorous qualitative analysis.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Hotel revenue management for the transient segment: taxonomy-based research

Baker

Eziz

Harrington

2020

IJCHM

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Purpose This paper aims to (1) organize the open literature on hotel revenue management systems, (2) compare practitioner systems in terms of functionality and (3) integrate (1)-(2) into research stream recommendations for the open literature with an empirical focus. Design/methodology/approach The authors use Nickerson’s taxonomy development method from the field of information systems to build the taxonomy. Findings New forecasting areas include developing a metric for the degree of strategic fit of a hotel’s pricing strategy and using it in conjunction with quantifications of online reviews for predictions. New price optimization avenues include determining whether a lack of congruence between customer perceptions of fairness and trust and pricing history has a detrimental effect on overall hotel performance and determining which combinations of flexible products, decision-maker risk aversion, nonparametric forecasting and reference effect optimization features work best in which situations. Originality/value This is the first study to combine vendor activities outside the technical realms of forecasting and price optimization with an emphasis on the choice modeling technical framework. This study points to several promising studies using qualitative methods, action research and design science.

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Section: Limitations and Future Researchmentioning

confidence: 99%

Hotel revenue management for the transient segment: taxonomy-based research

Baker

Eziz

Harrington

2020

IJCHM

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“…The adequacy and relevance of information play a significant role in decision making in businesses. That is why the concept of big data (high-volume and high-variety of data) proves to be significant in multiple sectors of modern-day businesses (Nisar et al , 2021; Brinch et al , 2018; Sumbal et al , 2019; Zhang and Zhang, 2021; Hamdam et al ,2022; Egan and Haynes, 2019; Smuts and Smith, 2021; El Samad, 2022; Liu, 2017). If a business organization wants to determine its target audience to predict/find out about the clients’ needs, big data can play a major part in such a decision-making.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and big data: ontological and communicative perspectives in multi-sectoral scenarios of modern businesses

Arora

Sharma

2022

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Purpose The purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals. Design/methodology/approach This paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses. Findings AI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic. Research limitations/implications There is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well. Social implications This paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant. Originality/value This paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.

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“…To use them effectively, a deep knowledge of RM techniques is required. This is confirmed by managers' perception of the still fundamental role of the people who deal with them [58].…”

Section: Rm and Ict Supportmentioning

confidence: 74%

Role and Activities of Revenue Manager between Individual and Team Competencies: Empirical Evidence in a Tourist Destination

Varra¹,

Rossi²

2019

JSSM

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Among the new figures that have appeared in the panorama of tourism in recent decades, the revenue manager is one that most intrigues operators and scholars. It is a figure whose outlines are still poorly defined. Even their activities are still not properly chalked out. These activities, other than being often confused with mere dynamic pricing with which actually they do not coincide, take place in structures according to an extreme variety of contents and methods. Of late, the literature has examined some aspects of revenue management. These include the strategic relevance of activity, the impact in terms of performance, the operating modes and technological support in the management of variables, and so on. However, the organizational dimension of revenue management activities does not appear to be very thorough in terms of who does what and the skills associated with this role. This work is part of the research in progress at different Italian destinations to understand which hotel activities fall within "revenue management". Linked with these activities, the research aims to find out about the knowledge, skills, and abilities required. The focus of the present paper is on Bellaria-Igea Marina in the province of Rimini. The results can be useful scholars interested in investigating these aspects. Hotels in organizing revenue management activities can also make use of these results. Further, they would come handy during the selection and evaluation of revenue managers.

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Manager perceptions of big data reliability in hotel revenue management decision making

Cited by 27 publications

References 38 publications

Hotel revenue management for the transient segment: taxonomy-based research

Hotel revenue management for the transient segment: taxonomy-based research

Artificial intelligence and big data: ontological and communicative perspectives in multi-sectoral scenarios of modern businesses

Role and Activities of Revenue Manager between Individual and Team Competencies: Empirical Evidence in a Tourist Destination

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