Risk determinants in the hotel sector: Risk credit in MSMEs

Búa, Milagros Vivel; Lado-Sestayo, Rubén; González, Luís Otero

doi:10.1016/j.ijhm.2017.11.004

Cited by 18 publications

(17 citation statements)

References 44 publications

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“…The negative coefficient of CR shows that increasing (decreasing) one point of CR causes to decrease (increase) LV by 0.27 points for seasonal business while increasing (decreasing) one point of CR causes to decrease (increase) LV by 0.15 points for non-seasonal business. These findings are consistent with previous empirical literature (Iqbal & Kume, 2015;Srivastava, 2014;Danila et al, 2020;Gilchrist & Mojon, 2018;Imbierowicz & Rauch, 2014;Buchdadi et al, 2020;Tulcanaza et al, 2019;Vivel-Búa et al, 2018;Chodnicka-Jaworska & Jaworski, 2017;Woo et al, 2020) and also support the hypothesis-1. The selected firms hold a larger amount of capital as an incentive to avoid failure.…”

Section: Discussionsupporting

confidence: 93%

“…Tulcanaza et al (2019) believed that the external determinants of the capital structure significantly affected credit risk of large corporations listed on the Korean Stock Exchange. Vivel-Búa et al (2018) found negative influence of leverage on credit risk. Chodnicka-Jaworska and Jaworski (2017) indicated that economic uncertainty and earning potentials positively contributed to the credit risk.…”

Section: Literature Reviewmentioning

confidence: 97%

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Firm’s Risk and Capital Structure: An Empirical Analysis of Seasonal and Non-Seasonal Businesses

Tahir¹,

Moazzam²,

Sultana³

et al. 2020

JAFEB

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The study attempts to analyze the impact of firm's risk on capital structure in the context of seasonal and non-seasonal businesses. We use two independent variables namely credit risk and systematic risk and one dependent variable to explore this connection. Sugar sector is taken as seasonal while the textile sector as non-seasonal businesses. The panel data of twenty-five firms from each sector are taken ranging for the period of 2012 to 2019 which has been retrieved from their annual reports for empirical analysis of the study. The results reveal the negative impact of credit risk on capital structure in both types of businesses. Increasing (decreasing) one point of credit risk causes a decrease (increase) leverage ratio by 0.27 points for seasonal while increasing (decreasing) one point of credit risk causes to decrease (increase) leverage by 0.15 points for non-seasonal businesses. Furthermore, the study shows positive impact of systematic risk on leverage ratio in non-seasonal business and no impact in seasonal business. Any increase (decrease) in the systematic risk causes an incline (decline) leverage ratio by 2.68 units for non-seasonal businesses. The study provides a guideline to managers for risk management in businesses. The research focusses on theoretical as well as managerial and policy implications on risk management in businesses.

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Section: Discussionsupporting

confidence: 93%

Section: Literature Reviewmentioning

confidence: 97%

Firm’s Risk and Capital Structure: An Empirical Analysis of Seasonal and Non-Seasonal Businesses

Tahir¹,

Moazzam²,

Sultana³

et al. 2020

JAFEB

View full text Add to dashboard Cite

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“…The vulnerability of hospitality organisations to a wide range of risks has sparked a growing scholarly interest in corporate-level risk management, with particular focus on systematic and non-systematic risk (Chen, 2013;Kim et al, 2012;Vivel-Búa et al, 2018), risk perception (Waikar et al, 2016) and mitigation strategies (Gjerald and Lyngstad, 2015). This vulnerability has increased as the industry continually witnesses risk-taking activities by major hotel groups, mostly in the area of mergers and acquisitions (Falk, 2016) but also in the adoption of new technology in product and service innovations (Hu et al, 2009;Lee et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Factors that shape a hotel company’s risk appetite

Zhang

Paraskevas

Altınay

2019

International Journal of Hospitality Management

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Despite a growing scholarly interest in risk management within the field of hospitality, risk appetite, which plays a key role in effective risk management, has not yet received wider attention. This paper contributes to our understanding of risk appetite by exploring the factors that influence risk appetite in a hotel company context. Through in-depth interviewing with risk appetite experts and corporate-level hotel executives, we identified two sets of factors ('primary' and 'secondary') that influence a hotel compan's risk appetite. Although, at corporate level, these factors do not differ from other industry contexts, they can be used by managers in the hotel sector as a starting point to understand drivers and inhibitors of their companies' risk appetite while researchers can use them as a basis to develop descriptive or predictive models of a hotel company's risk appetite.

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“…We consider capital as output mainly for the following two reasons: the generation of capital through hotel operation provides more funds and greater ability for hotels to update capital items, such as furniture, televisions, telephones, kitchens and cleaning machines (Ramanathan et al, 2016). On the other hand, the generation of capital is very important for providing stability for hotel operation (Vivel-Bua et al, 2018). The contextual variables used for the second phase analysis can be further divided into two sub-groups, namely hotel-specific variables (hotel size, capitalization and labour productivity), and industry-specific variables the number of arrivals (international tourism), the number of departures (international tourism), international tourism receipts and expenditure.…”

Section: Methodsmentioning

confidence: 99%

Investigation of efficiency in the UK hotel industry: a network data envelopment analysis approach

Tan

Despotis

2021

IJCHM

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Purpose This paper aims to investigate efficiency in the UK hotel industry and further evaluate the impacts of hotel characteristics and industry environment on efficiency. Design/methodology/approach The network data envelopment (DEA) weak link approach is used for the efficiency analysis, while the determinants of efficiency are evaluated by bootstrapped truncated regression. Findings The findings show that the UK hotel industry is very inefficient. The results of overall efficiency deconstruction show that the second-stage production process experiences an even lower level of efficiency than that of the first stage. The second-phase analysis shows that both the hotel-specific characteristics and the industry-specific characteristics are significantly related to UK hotel efficiency. Research limitations/implications The robustness of the results is affected because a single set of input-intermediate product-outputs and a single DEA method were used. Therefore, further studies can use alternate inputs, intermediate measures and outputs in the efficiency analysis. In addition, the robustness of the efficiency score can be checked using alternate parametric or non-parametric methods. Practical implications Hotels in the UK should focus on cost reduction, business diversification, improvement in the capital level and labor productivity, while at industry and macroeconomic level, discounts are recommended to be provided to international tourism and the tourism industry should be further opened. Originality/value The weak-link approach has been applied to estimate the efficiency level, as this provides more robust and accurate results compared to other non-parametric methods in the existing empirical studies and unique hotel-specific and industry-specific determinants of efficiency are considered in the second-stage analysis.

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Risk determinants in the hotel sector: Risk credit in MSMEs

Cited by 18 publications

References 44 publications

Firm’s Risk and Capital Structure: An Empirical Analysis of Seasonal and Non-Seasonal Businesses

Firm’s Risk and Capital Structure: An Empirical Analysis of Seasonal and Non-Seasonal Businesses

Factors that shape a hotel company’s risk appetite

Investigation of efficiency in the UK hotel industry: a network data envelopment analysis approach

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