BUS8x15 Strategic Hospitality Management Assignment Sample

• Subject Code : BUS8x15
• Subject Name : Hospitality Management

Analysis of The Role of Big Data in Hospitality Management

Table of Contents

Motivation..

Aim..

Objectives.

Justification..

Background.

Literature review..

Research question..

Research design..

Methodology.

Sampling.

Data collection..

Data processing and analysis.

Interview questions.

Scope.

Potential contributions.

Ethical considerations.

Potential harm..

Intellectual Property.

Informed consent

Nature of participation..

Independence and impartiality.

Project timeline.

Resources needed.

References

Motivation

a. Aim

The aim of the current study is to analyze the role of big data in mitigating the operational management issues in the context of hospitality organizations New Zealand.

b. Objectives

• To identify the issues faced by the operational management departments of hospitality organizations
• To determine the impacts of big data on hospitality management
• To suggest some strategies through which big data can be used for mitigating the challenges faced by the hospitality organizations

c. Justification

The use of big data has increased operational efficiency of many organizations. There is a number of researches that shows relationship between the use of big data is management section of different organizations. The current study is required to be conducted to add to the body of knowledge. The study will consider the role of big data in especially in management of the hospitality organizations.

d. Background

The current study is going to develop a knowledge base regarding the roles played by big data in the context of hospitality management. In order to conduct the same, different theoretical concepts are being presented (Zaharopoulos & Kwok, 2017). The study will include information regarding the sources of big data, challenges faced by the management of hospitality organizations, and some suggestive measures that can be taken under consideration to mitigate the challenges faced (Huda et al., 2017). Apart from that, the study will highlight the benefits that big data can provide to the organizations in managing their operations.

e. Literature Review

The literature review section includes theoretical concepts and knowledge gathered from different sources such as scholarly journals, books, authentic websites and other platforms.

Millions of people are attached with the hospitality organizations. Hospitality organizations are required to meet the expectations of these people in order to gain the return ensuring repetitive service. The management of every organization needs to gather a plenty of data through which the business processes and outcomes can be improved. As per the views of Ting et al., (2017), data such as customer feedback, stakeholder’s feedback, market situation and others are required to maintain continuity of the operations and other organizational functions. The huge size of these kind of data including video, audio and web data, generated which are challenging to be handled by the organization. However, as argued by (Pitas et al., 2018), big data has made it easier to control and analyze these data through converting them into compact and précised manner which may transform the organizational functions as well. Using these data sets and analytics organizations can best serve their customers through effective supply chain management.

1. Concept of big data

The enormous quantity of both the structured and unstructured data that creates difficulties in processing and analyzing is considered as big data used particularly in business aspects and are analyzed using different software techniques. As mentioned by Mariani (2019), big data is generally used for generating insights upon the customer intentions through observing, analyzing and predicting behavior from the data collected through various platforms. These platforms may include, GPS enabled devices, data catered through social media search, Close Circuit footage, in-store behavior and others (Liu & Mattila, 2017). These data acts as the insurance of the company and ensure customer satisfaction and retention for a long period.

2. Applications of big data

As the huge amount of data is generated in the business context on a daily basis, those are required to be taken under consideration for managing different areas of operation. The areas where the use of big data is exceptionally beneficial are listed below:

Revenue management

The use of offline and online data the hospitality organizations can conduct a predictive analysis. This may help the organizations to predict the future demand of the hotel rooms. Data that assist in this predictive analysis include current booking rates, past occupancy rates and other key performance metrics of the organization (Cheng & Jin, 2019).

Target marketing

The guests of hotels are diverse in characteristics and they variously differ from each other based on their choices and requirements. For instance, the preferences of business travelers are hugely different from the families (Shirdastian, Laroche & Richard, 2019). Big data is used by the hospitality organizations in order to predict the requirements of the target markets and develop marketing content successfully based on that information as well.

Managing customer experiences

Organizations also use big data in identifying significant customer trends and opinions provided through their behaviors or through physical data. Their strengths and weaknesses are judged and implemented accordingly in the process. Data such as service usage, social media sharing, posted reviews and feedbacks given by the customers that are analyzed in order to identify the trends and their significance in the process (Shayaa et al., 2018).

Gaining competitive advantages

Hospitality industry such as travel and tourism is competitive in nature and there are several types of organizations striving top grab the market share through the analysis of big data (Xiang et al., 2017). Different websites and other data sources are searched in order to identify the customer opinions and feedback about the other organization to recognize their strengths and weaknesses which helps in designing and developing services accordingly.

3. Benefits if using big data

The use of big data cn provides several benefits to the hospitality organizations that include, setting the beat price for their customers. As mentioned by Martin-Rios, Pougnet & Nogareda (2017), it is dependent upon the data gathered regarding the pricing strategies of the competitors in market. As argued by Mariani et al., (2018), big data also helps in optimizing the service extension considering the information regarding customer preferences. It has also been identified that behavior of the customers, their booking trends and others also provide significant benefit in developing promotional responses.

4. Challenges of big data

The technological advancements has brought significant changes in the operational management of the hospitality sectors such as travel and tourism though the knowledge gap between the smart usage of the technologies and customer’s personal experiences could not be aligned.

The website developed for generating the responses for the customers creates difficulties when they use the same for the first time due ti the complexity of the platform.

Organizations are required to appoint qualified and skilled professionals to analyze the data gathered from the different sources and in interpreting the same (Saggi & Jain, 2018). Hospitality organization employees may lack the required capacity to develop significant knowledge base from the available data.

Modern technological tools are used in order ti extract the required data regarding the mentioned areas. It becomes costly for most of the organizations that are suffering from a financial turmoil Haynes & Egan (2017). Maintaining the data security is another challenging concern for the hospitality organizations. Huge information provided by the customers may also contain their personal information. Data theft or data breach are one of the major negative outcome of ineffective data management.

Conceptual Framework

Figure 1: Conceptual Framework

Literature Gap

There are several information enlighten about the use of big data in the context of hospitality management though there is a gap in knowledge regarding the strategic and practical use of these data. The current research will increase understanding on the same.

Research question

What is the role of big data in the field of hospitality operations management?

Research design

a. Methodology

The proposed study will follow the positivism philosophy of research as it allows the researcher to predict the relationship between the variables based on the research findings gathered. Deductive approach will be taken under consideration in order to measure the concepts in a quantitative manner (Buhalis & Leung, 2018). Primary and secondary research will be conducted. The researcher will collect data regarding the various aspects related to big data and its role played in the hospitality organizations by obtaining responses from the management employees of various hospitality organizations of New Zealand (Ko, 2018). It will allow the research to establish an objective outcome that can be used comprehensively in practical field. Secondary data will be beneficial to increase the sphere of knowledge regarding the research topic and it is both time and cost effective.

b. Sampling

The researcher will use the non-probability sampling methods especially, the judgment sampling method. Using this method of sampling, the current researcher will conduct judgment on appropriateness of the participants in the current research situation (Albusaidi, Udupi & Dattana, 2016). For instance, employees that are working in management sections of various hospitality organizations and having sound knowledge regarding big data use in management will be considered for gathering primary data. The sample will consist of 20 participants having sound knowledge in this area.

c. Data collection

The current researcher will develop a questionnaire consisting 9 questions relating to the role of big data in the hospitality management. The questionnaires will be sent through mail and collected accordingly (Serna et al. 2016). In order to conduct secondary research, various existing knowledge sources will be considered evaluating their relevance to the study and authenticity.

d. Data processing and analysis

The raw data collected from the participants will be sorted and simple use of percentage will be considered for analyzing the data. Secondary raw data will be aligned with the current research topic and will be presented through thematic analysis where the data sets will be analyzed under several themes précising the knowledge.

Interview Questions

1. What is your age range?

Options

• 21-30 years
• 31-40 years
• 41 and above

2. What is your gender?

Options

• Male
• Female
• Others

3. How long have you been working within the organization?

Options

• More than 3 years
• More than 5 years
• More than 10 years

4. Does your organization use Big Data?

Options

• Yes
• No

5. How far do you agree that skills for working with Big Data are important?

Options

• Strongly agree
• Agree
• Undecided
• Disagree
• Strongly disagree

6. What are the challenges you have faced while dealing with big data?

Options

• Dealing with the growth of data
• Irregular insight generation
• Data validation
• Resistance organization

7. What are the benefits of using big data in your organization?

Options

• Identification of root causes
• Improving customer loyalty
• Reevaluation of risk portfolio
• Revenue management

8. Which is the highest priority you need to consider while dealing with big data?

Options

• Hadoop
• Java
• Python
• Tableau

9. What are the strategies your organization can choose to use big data effectively?

Options

• Defining business objectives clearly
• Developing roadmap for big data
• Current and previous assessment alignment
• Managing change depending on big data

Scope

The current study will obtain knowledge regarding the practical uses of big data in management of hospitality industry such as travel and tourism, challenges faced by the managers while using the big data and strategies that may improve the operational management.

Potential Contributions

This study will help the contemporary organizations to place their emphasis on mitigating trending challenges and to generate high revenue based on that.

The research team will be gain the opportunity to develop and expand their concept regarding the use of big data in hospitality management and it will help them to use the knowledge gained from current study in conducting other related researches (Antonio, de Almeida & Nunes, 2019).

The body of knowledge will be enhanced through addition of the information regarding the practical use of the data.

Ethical Considerations

a. Potential harm

The ethical issues that may arise in the context of the current research include breach of privacy of personal information shared by the participants and potential reputation loss of the organizations under which the participants are working currently. In order to avoid these issues the researcher will obtain an informed consent from each of the participants in written form. Participants will be allowed to leave the research whenever they want to.

b. Intellectual Property

Intellectual properties of a hospitality organization may include trade secrets, trademarks, patents and copyrights. The current researcher will not disclose any of the intellectual property while conducting the research and after finishing the same that can provide competitive advantage to rival organizations and lead the participant organizations to financial or reputational damage.

c. Informed consent

Research consent form

The research consent form is required to be provided while approaching the potential participants of the research study. This is a written disclaimer from the participants for their agreement to be involved in the study.

Research information sheet

The research information sheet will provide every detail of the research to be conducted that will help to analyze the potential impact of the research on the participants, content and procedures adopted for conducting the research. It is a written disclaimer about the processes that will be conducted and their potential impacts.

d. Nature of participation

The participation of the respondents of the study will be voluntary in nature. Participants can withdraw their participation at any point of time and would not be held responsible for that.

e. Independence and impartiality

The researcher bias will be attempted to reduce to the minimum through using authentic data sources and objective explanation of the findings gathered. All the data sources used for the current study will be recognized by the research contributors. Information gathered from the secondary resources will be cited authentically.

Project Timeline

Figure 2: Project timeline

Resources Needed

The researcher will require financial resources for conducting the research through transporting, buying equipments and others. Human resource will be required to collect the data manually. Technological equipments such as data analysis software is also required to conduct the current study.

References

Albusaidi, H. S., Udupi, P. K., & Dattana, V. (2016). Integrated data analytic tourism dashboard (IDATD). Paper presented at the 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 497-500. Retrieved 5 May 2020, from https://doi.org/10.1109/ICRITO.2016.7785006

Antonio, N., de Almeida, A., & Nunes, L. (2019). Big data in hotel revenue management: exploring cancellation drivers to gain insights into booking cancellation behavior. Cornell Hospitality Quarterly, 60(4), 298-319. Retrieved 5 May 2020, from https://dx.doi.org/10.1177/1938965519851466

Ban, H. J., & Kim, H. S. (2019). Semantic network analysis of hotel package through the big data. Culinary Science & Hospitality Research, 25(2), 110-119. Retrieved 5 May 2020, from https://doi.org/10.20878/cshr.2019.25.2.014

Buhalis, D., & Leung, R. (2018). Smart hospitality—Interconnectivity and interoperability towards an ecosystem. International Journal of Hospitality Management, 71, 41-50. Retrieved 5 May 2020, from https://doi.org/10.1016/j.ijhm.2017.11.011

Cheng, M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. International Journal of Hospitality Management, 76, 58-70. Retrieved on: 2 May 2020, from: https://e-tarjome.com/storage/panel/fileuploads/2019-04-15/1555313770_E10906-e-tarjome.pdf

Haynes, N., & Egan, D. (2017). Revisiting the relevance of economic theory to hotel revenue management education and practice in the era of Big Data. Research in Hospitality Management, 7(1), 65-73. Retrieved 5 May 2020, from https://doi.org/10.1108/IJCHM­07­ 2017­0461

Huda, M., Maseleno, A., Shahrill, M., Jasmi, K. A., Mustari, I., & Basiron, B. (2017). Exploring adaptive teaching competencies in big data era. International Journal of Emerging Technologies in Learning (iJET), 12(03), 68-83. Retrieved on: 2 May 2020, from: https://onlinejour.journals.publicknowledgeproject.org/index.php/i-jet/article/viewFile/6434/4308

Ko, C. H. (2018). Exploring Big Data Applied in the Hotel Guest Experience. Open Access Library Journal, 5(10), 1-17. Retrieved 5 May 2020, from https://doi.org/10.4236/oalib.1104877

Liu, S. Q., & Mattila, A. S. (2017). Airbnb: Online targeted advertising, sense of power, and consumer decisions. International Journal of Hospitality Management, 60, 33-41. Retrieved on: 2 May 2020, from: https://pdfs.semanticscholar.org/e70a/01a0ea5d753e61ebbeff22288b0de30626ae.pdf

Mariani, M. (2019). Big Data and analytics in tourism and hospitality: a perspective article. Tourism Review. Retrieved 5 May 2020, from https://doi.org/10.1108/TR-06-2019-0259

Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management. Retrieved 5 May 2020, from https://doi.org/10.1108/IJCHM­07­ 2017­0461

Martin-Rios, C., Pougnet, S., & Nogareda, A. M. (2017). Teaching HRM in contemporary hospitality management: A case study drawing on HR analytics and big data analysis. Journal of teaching in travel & tourism, 17(1), 34-54. Retrieved 5 May 2020, from http://dx.doi.org/10.1080/15313220.2016.1276874

Pitas, N., Hickerson, B., Murray, A., & Newton, J. (2018). Repositioning undergraduate education in recreation and leisure studies. SCHOLE: A Journal of Leisure Studies and Recreation Education, 33(1), 1-11.

Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758-790. 

Serna, A., Casellas, A., Saff, G., & Gerrikagoitia, J. K. (2018). Big Data and Service Quality. In Quality Services and Experiences in Hospitality and Tourism. Emerald Publishing Limited. Retrieved 5 May 2020, from https://doi.org/10.1108/S2042-144320180000009015

Shayaa, S., Jaafar, N. I., Bahri, S., Sulaiman, A., Wai, P. S., Chung, Y. W., ... & Al-Garadi, M. A. (2018). Sentiment analysis of big data: Methods, applications, and open challenges. IEEE Access, 6, 37807-37827. Retrieved on: 2 May 2020, from: https://ieeexplore.ieee.org/iel7/6287639/6514899/08399738.pdf

Shirdastian, H., Laroche, M., & Richard, M. O. (2019). Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter. International Journal of Information Management, 48, 291-307. 

Ting, P. J. L., Chen, S. L., Chen, H., & Fang, W. C. (2017). Using big data and text analytics to understand how customer experiences posted on yelp. com impact the hospitality industry. Contemporary Management Research, 13(2). Retrieved 5 May 2020, from https://doi.org/10.7903/cmr.17730

Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65. 

Zaharopoulos, D., & Kwok, L. (2017). Law firms’ organizational impression management strategies on Twitter. Journal of Creative Communications, 12(1), 48-61. Retrieved on: 1 May 2020, from: https://journals.sagepub.com/doi/pdf/10.1177/0973258616688969

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