Identification of risk factors in the hospitality industry: Evidence from risk factor disclosure

Penela, Daniela; Serrasqueiro, Rogério

doi:10.1016/j.tmp.2019.100578

Cited by 6 publications

(11 citation statements)

References 31 publications

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“…Research studies adopting Leximancer for text mining normally report the proximity of themes and distribution of concepts within themes (e.g. Penela & Serrasqueiro, 2019). However, in this paper important connections were also explored to provide further insights.…”

Section: Discussionmentioning

confidence: 99%

What are the key themes associated with the positive learning experience in MOOCs? An empirical investigation of learners’ ratings and reviews

Deng

Benckendorff

2021

Int J Educ Technol High Educ

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Past MOOC research has tended to focus on learning outcomes valued in traditional higher education settings, such as achievement and retention. This study recognises that student ratings are an important alternative outcome measure in MOOCs. This paper adopted a semiautomatic text mining approach to collect and analyse 8475 ratings and reviews submitted for 1794 MOOCs. The analysis revealed six important themes that contributed to positive ratings: ‘learning’, ‘understanding’, ‘interesting’, ‘videos’, ‘recommend’, and ‘questions’. The paper then investigated the characteristics of each identified theme based on the proximity of themes, distribution of concepts within themes, and important connections. Based on research findings, the paper presents the following propositions to assist educators and providers to enhance the learning experience in MOOCs: (1) provide realistic learning contexts and instructional conditions in MOOCs to facilitate the acquisition of knowledge that transfers more readily to real-world practices; (2) carefully design the instructional conditions so that some mental challenge and stimulation is required for learners to achieve a full understanding of the content, rather than making MOOCs too simple or effortless to complete; (3) design the course content, materials, and communications to generate interest; (4) allocate sufficient resources to create high-quality video lectures; (5) employ video lectures to elicit positive emotions from MOOC learners and simplify complex, difficult concepts; and (6) incorporate discussion boards in MOOCs and invest in human and digital resources to address learners’ queries.

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Section: Discussionmentioning

confidence: 99%

What are the key themes associated with the positive learning experience in MOOCs? An empirical investigation of learners’ ratings and reviews

Deng

Benckendorff

2021

Int J Educ Technol High Educ

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“…Focusing on US lodging companies, Penela and Serrasqueiro (2019) find that while the focus of risk disclosure was on economic and financial conditions in 2008, the themes in 2016 focused on risks that could affect company results. Other industry level studies have identified 66 types of risk factors in the energy industry (Wei, Li, Zhu, Sun, and Li 2019) and 21 risk factors in the banking industry, with four of the risk factors unique to that industry ).…”

Section: Rfd Contentmentioning

confidence: 99%

“…LDA has also been used in other articles on RFDs not covered in this study (Israelsen 2014;Ross 2019). Leximancer, a tool similar to NVivo that is used by Penela and Serrasqueiro (2019) to identify the themes in RFDs, can also be explored by future studies as another method of extracting risk methods. In addition to its usefulness in uncovering important factors when research is not based on a theoretical deduction, Leximancer can offer a greater list of potentially useful keywords not identified by NVivo and provide more visually attractive diagrams (Sotiriadou et al 2014).…”

Section: Suggestions For Future Researchmentioning

confidence: 99%

“…While the method of extraction differs in most of the studies, the topics extracted are quite similar. In an untabulated analysis, this study conducts a mapping of the RFDs' contents in Bao and Datta (2014), Huang and Li (2011), Campbell et al (2014), Penela and Serrasqueiro (2019), and Wei, Li, Zhu, Sun, and Li (2019) with the sole objective of identifying common themes. 8 The mapping reveals that the topics discussed in RFDs can be classified in the broad areas of financial risk, idiosyncratic risks, tax risks, systematic risks, macroeconomic risks, and regulatory/legal risks.…”

Section: Suggestions For Future Researchmentioning

confidence: 99%

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Risk Factor Disclosures: A Review and Directions for Future Research*

Isiaka

2021

Accounting Perspectives

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This paper assesses the current state of scholarly work on risk factor disclosures (RFDs) with the goal of synthesizing existing literature and stimulating further research in this area. To a large extent, prior research studies have examined different aspects of the disclosure of corporate risk information in annual reports. Beginning in 2005, the US SEC proposed changes to the disclosure of risk information in annual 10-K reports. The changes mandated large firms in the United States to disclose risk factors in Item 1A of their 10-Ks. While research studies on the impact of the changes are still ongoing, there are concerns among stakeholders that RFDs are vague, repetitive, and boilerplate. As a result, the SEC called on firms to ensure that they clearly disclose all the risks they faced. The SEC's call resulted in the release of an amendment that provides directions to further improve firms' RFDs. Using a systematic literature review method, this paper classifies the RFD literature into five research themes: (i) contents, (ii) informativeness, (iii) determinants, (iv) quality, and (v) effects on firm performance. The paper also reviews theories that have been dominantly applied in RFD studies and provides suggestions for future research.

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“…Studies in the area have mainly analyzed the reviews of social media and hotel booking platforms. As pointed out, the hospitality industry, mainly the hotel sector is subject to numerous risks and fluctuations, such as variation in the demand and supply of hotel rooms, seasonal dependency, energy costs, workers' healthcare, and insurance (Penela & Serrasqueiro, 2019). Moreover, the industry stakeholders, despite less accurate results, still extensively employ the traditional approach of data analysis.…”

Section: Existing Research Issues In Hotel Industrymentioning

confidence: 99%

Application of Machine Learning in the Hotel Industry: A Critical Review

Alotaibi

2020

Journal of Association of Arab Universities for Tourism and Hos

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Study purpose-The hotel industry like any other industry is witnessing a change due to information and communication technology. However, this change is quite slow. Many researchers in recent time have garnered interest in exploring and implementing the new technologies of artificial intelligence and machine learning in the hotel industry. Therefore, the purpose of this study is to give insights on the role of ML and its integrated technologies in the hotel industry. Design/Methodology/Approach-The study has critically reviewed articles published from 2010 to 2020. To achieve the research objective, the study seeks to answer three main research questions related to the existing literature; RQ1: Where does the hotel industry implement machine learning? RQ2: What are the machine learning techniques used in the hotel industry? RQ3: Which countries are using machine learning in the hotel industry? Findings-The study found that machine learning is helpful in demand forecasting, price forecasting, booking cancellation prediction, financial efficiency, and work efficiency. The machine learning algorithms outperform in the forecast accuracy against the statistical models. The countries at the forefront in machine learning technologies are China and USA. The other countries should take the cue from them and implement machine learning in their hotels Originality of the research-This research conducts exploratory analysis to identify the extent of scientific community knowledge and awareness on machine learning in the hotel industry. To the best of the authors' knowledge, no prior researcher has conducted a similar study specifically in the hotel industry.

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Identification of risk factors in the hospitality industry: Evidence from risk factor disclosure

Cited by 6 publications

References 31 publications

What are the key themes associated with the positive learning experience in MOOCs? An empirical investigation of learners’ ratings and reviews

What are the key themes associated with the positive learning experience in MOOCs? An empirical investigation of learners’ ratings and reviews

Risk Factor Disclosures: A Review and Directions for Future Research*

Application of Machine Learning in the Hotel Industry: A Critical Review

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