Profitability in the hotel sector: a PLS approach

Abstract

Purpose The purpose of this paper is to analyse the determinants of hotel profitability through the application of a least squares path modelling (PLS) and to deepen the study of their heterogeneity through clustering techniques. Design/methodology/approach An eclectic model that incorporates the dimensions identified as determinants of profitability in the previous literature is estimated by means of PLS. Subsequently, in a second stage the hotels are classified using clustering techniques to study which combination of hotel characteristics, location, competitive environment and tourist destination achieve higher profitability. Findings The results show that hotel characteristics, hotel location, competitive environments and tourist destination factors impact on the hotel performance. Also, the results highlight that the eclectic model proposed have to be further developed in future studies, incorporating more theoretical constructs and variables. Research limitations/implications The results contribute to identify and quantify the determinants of profitability in the hotel sector and which combination of them achieve higher profitability. Moreover, this paper provides new evidence to support an eclectic theory of profitability determinants in the hotel sector. Consequently, this paper claims the need of a deeper integration of different areas of expertise to analyse hotel profitability. Originality/value This research is the first empirical evidence that proposes an eclectic model and uses clustering techniques in the study of profitability differences considering simultaneously hotel characteristics and its location. The variables used allow to measure different aspects of the attributes of the hotel and its location.

Internal factors affecting the financial performance of an organisation's business processes

“…Still, in some other studies, ROA is calculated by earnings before interest and taxes (EBIT) divided by total assets or earnings before taxes (EBT) divided by total assets; or earnings after taxes (EAT) divided by total assets (Nguyen et al ., 2021; Pervan et al ., 2019; Tran and Le, 2020). Generally, to measure a company's profitability, most studies combine financial ratios such as ROE and ROA (Alsharari and Alhmoud, 2019; Aslam et al ., 2021; Lado-Sestayo and Vivel-Búa, 2018; Le et al , 2020a; Naz et al ., 2022; Samo and Murad, 2019; Tung and Binh, 2021; Wassie, 2020; Yadav et al , 2022a); combined ROE, ROA and ROS (T. N. L. Nguyen and Nguyen, 2020; Yoo and Kim, 2015); ROE and EPS (Alarussi and Alhaderi, 2018; Al-Homaidi et al , 2021). Some studies also use a combination of other financial indicators such as return on invested capital (ROIC) with ROA (Clifford Defee and Stank, 2005); ROA, ROE with EPS (Lim and Rokhim, 2021); return on capital employed (ROCE), earning before interest, taxes, depreciation and amortisation (EBITDA) with ROA (Nanda and Panda, 2019); ROA, ROE, EPS and market capitalization (Ali et al , 2022); ROCE, return on net worth (RONW) with ROA (Cyril and Singla, 2021); ROIC with ROA (Cyril and Singla, 2020); net profit margin (NPM) divided by sales revenue with ROA (Nanda and Panda, 2018); or occupancy rate (OCCR), gross operating profit per available room (GOPPAR) with ROA and ROE (Menicucci, 2018).…”

“…Furthermore, many studies have addressed factors affecting profitability in a particular industry or business sector such as tourism and hospitality (Lado-Sestayo and Vivel-Búa, 2018, 2020; Menicucci, 2018), pharmaceutical industry (Lim and Rokhim, 2021), food processing industry (Le Thi Kim et al ., 2021; Szymańska, 2017), banking industry (Almaskati, 2022; Batten and Vo, 2019; Coccorese and Girardone, 2021; Ding et al , 2017; Goddard et al , 2004; Horobet et al , 2021; Katırcıoglu et al , 2020; Kumar and Bird, 2022; Nguyen, 2019; Sufian and Habibullah, 2009; Yuen et al , 2022); manufacturing companies (Nanda and Panda, 2018, 2019; Pervan et al ., 2019); construction companies (Cyril and Singla, 2020, 2021; Le et al ., 2020); textile industry (Samo and Murad, 2019); real estate, industrial construction and infrastructure companies (Cyril and Singla, 2020); agri-food industry and agricultural cooperatives (Grau and Reig, 2018; Le Thi Kim et al ., 2021; Singh et al ., 2019); healthcare and hospital industries (Bai and Anderson, 2016; Tiwari, 2022); the sports organisations (Malagila et al , 2021).…”

Untitled

“…These studies can be categorized into two primary approaches: the first primarily integrates financial data, while the second approach incorporates various intrinsic and other extrinsic variables. As an instance, these latter studies (Sami & Mohamed, 2014;Bresciani et al, 2015;Lado-Sestayo et al, 2017;Lado-Sestayo & Vivel-Búa, 2018Menicucci, 2018;Lima Santos et al, 2020;Karhunen & Ledyaeva, 2021) include size, location, ownership structure, hotel affiliation, internationalization, first activity of the hotel, education (of the general manager and/or financial manager), population density, proximity to the airport, seasonality, number of stars, chain branding, occupancy rate etc. In their research Sami & Mohamed (2014) conclude that hotels affiliated within international chains or franchises, and this is in line with the study carried by (Karhunen & Ledyaeva, 2021).…”

The Impact of Marketing Mix on the Consumer Purchase Decision in the Surabaya - Indonesia Hotel Residence

“…The quality of customer service begins with a need from the customer and gets its satisfaction so that the customer can give a positive value to customer service rather than the company itself. Location factors also influence consumers in making decisions about buying goods and services [9]. If the location of the company is easily accessible to consumers and close to the downtown crowd, then it is the right thing for the company to determine a location where the company will operate [10].…”