Investor choices in luxurious hotels in the era of COVID-19: an analytical approach to the case of Crete

Abstract

The goal of this research is to identify the investment preferences of top managers and owners of four- and five-star hotels and the impact of the COVID-19 pandemic on their investment planning. The study examines various factors, including the individual characteristics of executives (such as gender, age group, education level, and position held in the hotel unit), and their perceptions of the pandemic’s effects on their investment planning. To the best of our knowledge, this is the first attempt to capture the pandemic’s impact on investment options, planning, and spending for luxury hotels. The study first conducted a structured questionnaire survey to record investment priorities under two distinct scenarios: one prior to the pandemic and another after the pandemic’s spread and containment measures. Statistical tests were then used to compare the results between the two periods, revealing clear differences in investment choices among top executives and owners. Chi-square tests were also employed to examine the impact of individual characteristics on executives’ perceptions of the pandemic’s impact on their investment planning and their willingness to limit investment spending. Ordinal regressions were used to explore association further. The findings show that participants’ age group was associated with their perception of the pandemic’s negative impact on investment planning and their willingness to limit investment spending. Participants’ position in the hotel unit partially explained their willingness to reduce investment resources during the pandemic. Therefore, the research highlights that the age of higher management in luxury hotels impacts the business's ability to adapt to the post-COVID-19 environment. The results also suggest that policy makers' public measures aimed at enhancing executives’ willingness to invest during difficult periods should consider these factors.

1 Introduction

Greece’s economy heavily relies on its tertiary production sector, particularly its tourism industry. In 2018, over 30 million foreign tourists visited Greece, which generated $19 billion for the country’s economy. This figure increases to over $48.4 billion when accounting for domestic tourism and the indirect economic impact on other sectors [42, 85].

The robustness of the statistical data mentioned above explains why Greece has developed a comprehensive infrastructure that caters to various types of tourist activities. Despite this, nearly 70% of international arrivals occur during the summer season, primarily on the islands, with Crete being a prominent destination. Since 2013, hotels in Greece have been evaluated based on the star rating system of the European Hotelstars Union, which rates categories from one to five stars and includes additional flags for superior services. One of the advantages of this system is its reliance on objective criteria such as hotel infrastructure, services, amenities provided, and room size, ensuring consistency across different implementations [56]. The European Hotelstars Union also conducts undercover guest inspections for hotels with three to five stars to maintain service quality and ensure ongoing compliance with their criteria. In 2016, Greece had over 1,850 four- and five-star hotels with more than 340,000 rooms, a figure that has increased significantly in recent years, with many of these hotels situated on Crete. Therefore, this research focuses on this Greek island.

The tourism industry in Greece has seen a significant growth in the number of hotels and beds, which can be attributed to the considerable investments made in the sector. In 2018 alone, the investment surpassed 2.2 billion euros [42], highlighting the importance of mapping out investment planning in both research and practice. This is important not only for the impact it has on the Greek economy, but also for setting global standards for hotels in other destinations. Therefore, identifying the investment preferences of hotel owners and top management is critical in determining the factors that influence the choice of tourist accommodation for leisure and customer satisfaction, as supported by the existing literature.

The literature has identified several factors that are critical for both the selection of accommodation and customer satisfaction during their stay. These factors include room infrastructure [6, 10, 55, 94, 95], staff behavior and attitude [16, 19, 72, 94], customer interactions [39, 65, 66], employee experience [20], food and beverage quality [1, 17], reception quality [9, 33, 34, 35, 48], room quality [48, 75, 76, 88], safety [38, 68, 88, 94], sociability [3, 59, 68, 94], waiting time [8, 36], and digital booking platforms [60, 96]. Additionally, psychological factors such as the feeling of “value for money” and the overall hospitality experience are important to customers [23, 64, 77]. It is thus important for hotel top management to invest in improving these factors. To categorize these factors, this research focuses on eleven dimensions and asks top management executives to note their investment preferences through hypothetical scenarios. Because the hotel star rating system can be a differentiating factor in the choice of accommodation and affect customer satisfaction, the research exclusively focuses on four and five-star hotels where the evaluation system plays a less significant role [74].

To capture the impact of individual characteristics on economic and investment decisions, we utilize structured questionnaires. Recording individual characteristics of participants is a common practice in research to better understand how personal factors may influence decision-making processes. Gender, age group, educational level, and position held in the hotel are examples of such characteristics that could potentially impact economic and investment decisions. For example, research has shown that gender can influence risk-taking behavior [7], age group can affect consumer preferences [86], educational level can affect investment choices [5], and position in the hotel can impact managerial decision-making [29]. By collecting this information, the research can explore whether there are any patterns or correlations between individual characteristics and investment preferences among hotel executives.

Following the recording of the initial investment preferences, we also documented the development of investment preferences after the emergence of the COVID-19 pandemic and the measures taken to mitigate it. The pandemic first appeared in Wuhan, China in December 2019 and quickly spread worldwide, resulting in an unprecedented global crisis [92]. Unlike previous economic shocks faced by the tourism industry, the pandemic's impact and the subsequent measures implemented to address it, such as restrictions on travel and population mobility, can be considered an economic “super-shock” for the industry. Therefore, it is important to examine whether there were any changes in investment planning before and after the pandemic. Our research indicates that there was a significant difference in investment planning between the two periods, suggesting that the pandemic has led to significant structural changes in the tourism industry.

Furthermore, to explore the potential influence of individual characteristics on the hotel management’s perception of the pandemic’s impact on investment planning and spending, we conducted statistical tests and ordinal regressions. Our findings suggest that the age group of top management executives or owners significantly affects their perception of the pandemic’s negative impact on investment planning and spending. Additionally, the position held by each executive in the hotel unit could alter their perception of the required reduction in investment expenditure under the pandemic’s new conditions.

To our knowledge, this is the first study to explore the effects of the pandemic on investment options, planning, and spending in four and five star hotels. Additionally, the study investigates the relationship between the individual characteristics of top management executives in these hotels and their perception of the pandemic’s impact, providing an interpretive basis for the observed changes.

The article is structured as follows: the second part provides a literature review, the third part details the data collection and methodology, the fourth part presents results on investment options before and after the pandemic, the fifth part analyzes the statistical relationship between individual characteristics and pandemic impact perception, and the final part offers conclusions and future research ideas.

2 Theoretical background

2.1 The tertiary sector in Greece: tourism and hotel businesses

In 2019, Greece’s Gross Domestic Product (GDP) was $205.26 billion, ranking it 51st globally, according to the World Bank. The country experienced the largest economic recession in its history from 2009 to 2017, with nominal GDP declining by over 28% during that period, as reported by the European Commission in 2015 [27] and Eurostat in 2020 [26]. However, in subsequent years, such as 2017, 2018, and 2019, the Greek economy showed positive growth, according to the International Monetary Fund. The COVID-19 pandemic dealt another blow to the Greek economy in 2020, causing GDP to fall by 7.9%. Nonetheless, the economy appears to have rebounded quickly, with GDP rising to $214.87 billion in 2021, an increase of 8.4% over the previous year, per the [91] report. The tertiary sector accounted for about 80% of Greece’s GDP in both 2019 and 2021, according to the World Bank, Central Intelligence Agency, and other sources.

The tertiary sector, particularly shipping and tourism, contributed the most to the GDP of Greece. The country has a highly developed tourism industry, and data from Fig. 1 indicates that the industry's contribution to the GDP in 2016 and 2017 was significant. Greece had 30.1 million foreign visitors in 2018, an increase of 10.8% compared to 2017, where it had 27.2 million visitors [85]. The growth rate of tourism in Greece during this period was significantly higher than the global average of 4%. In 2018, Greece ranked eighth among top destinations in Europe and among the top twenty worldwide in terms of tourist arrivals. The revenues from foreign tourist arrivals in 2018 reached approximately 19 billion dollars, ranking Greece ninth in Europe. Including domestic tourist traffic and the indirect impact of tourism on other sectors of the economy, the aggregate impact of the tourism product on Greece's GDP was estimated to have reached €48.4 billion, or 27.3% of GDP for 2017 [42]. Total investments in the hotel industry for 2018 amounted to €2.26 billion, up 46% from the previous year [42].

Fig. 1

Sex distribution of participants

Greece relies heavily on its tourism sector, and as a result, it has developed various types of tourism infrastructure to attract different types of visitors. While the Greek government has attempted to diversify its tourism offerings with specialized campaigns for conference, religious, agro, alternative, and winter tourism, about 70% of international tourist arrivals happen during the summer months between May and September.

The Greek hotel industry is well-developed and offers different types of accommodation. Since 2013, Greece has adopted the European Hotelstars Union star rating system, which evaluates the quality of services provided in hotel accommodations. This system was created by HOTREC, an umbrella organization for 39 clubs from 24 European countries. The rating system includes 21 categories with 270 items, and some are mandatory while others are optional. Criteria include quality management, wellness, and sleep accommodation. Each entry in the criteria list is associated with a given number of points, and each Hotelstars level requires a minimum tally of these points, as well as some mandatory criteria. The Superior flag is awarded at each star level if the minimum tally of points required for the next Hotelstars level is achieved, and if all the mandatory requirements are satisfied. Hotel ratings are based on objective criteria, such as hotel infrastructure, services, amenities, and room size. Anonymous guests/partners regularly check the quality of services in hotels with three to five stars.

The implementation of a star rating system for hotels has several advantages for all stakeholders in the tourism industry, including governments, tour operators, and travel agencies. The system enables customers to make informed comparisons between accommodations, reduces information asymmetry, and helps in creating reasonable expectations about the quality of services provided [56, 62, 67, 77].

Studies have indicated that customer perceptions of hotels vary based on their star ratings. For instance, Martin-Fuentes [56] found that customer reviews’ average score increases with each additional star, even when all other factors remain the same. Similarly, using data from over 10,000 hotels, Bulchand–Gidumal et al. [15] discovered a positive correlation between a hotel's star rating and its customer rating. Rhee and Yang [77] demonstrated that customer expectations of hotels vary significantly based on their star category. Finally, Qu et al. [74] discovered that the satisfaction of guests staying in three-star hotels was lower than those in higher-rated hotels. Moreover, this study revealed no significant difference in customer satisfaction between four- and five-star hotels, which justifies the inclusion of both types of hotels in the present study.

Table 1, which is provided by the Hellenic Chamber of Hotels and presents the number of hotels and beds per category in Greece for the year 2021, shows the classifications awarded for each star category.

Table 1 Number of hotels and beds per star category

Table 2 provides the same data for the island of Crete which is the focus of our research and it is presented below.

Table 2 Number of hotels and beds per star category in Crete

Since 2016, the number of hotels in Greece has increased to 10,036 (3.14%), and the number of beds has increased to 894,789 (13.47%), which may suggest the development of large hotels or significant investment in existing establishments, given that the growth of beds is three times greater than that of hotels. Furthermore, there has been a noteworthy rise in the number of establishments with four and five-star ratings in recent years. Additionally, there are 302 organized camping sites that can be included in these accommodation figures. It is also noteworthy to observe here that almost one fourth (25%) of all the hotel capacity of Greece for luxurious 4* and 5* hotels in the year 2021 can be found in the island of Crete, something that shows the significance of the island in the hotel industry of Greece. Furthermore, considering the fact that the percentage of the number of hotels in the island compared to the whole of Greece is smaller, leads us to the conclusion that hotel units in Crete have more rooms per unit compared to the rest of Greece.

2.2 Customer satisfaction factors and accommodation selection factors in the hotel sector

A few decades ago, the field of management began to develop the idea of customer satisfaction. As defined by Oliver [70], customer satisfaction measures the difference between customer expectations prior to purchasing a product or service and their evaluation of the product or service after consumption. In the service industry, there is an ongoing debate as to whether customer satisfaction should be viewed as a specific concept that depends on individual transactions between customers and service providers, or as a comprehensive concept that encompasses all interactions between the two parties [49]. While Johnson et al. [49] argue in favor of the latter perspective, their review of various studies on the customer satisfaction index reveals that the majority of authors hold the opposing view that it is based on a specific perception of satisfaction derived from each individual transaction.

The concept of service quality is closely linked to customer satisfaction and is founded on the expectation-confirmation theory [30, 71]. However, research has demonstrated that while this theory is suited for analyzing each transaction separately, it is more effective for describing the idea of service quality [41], based on the notion that the perception of service quality precedes customer satisfaction. According to this theory, customers cognitively evaluate the performance characteristics of the different services they are provided with, which contributes to their short-term satisfaction and ultimately shapes their overall service experience [83]. Numerous empirical studies conducted in the hotel industry have established that service quality has a direct and positive impact on customer satisfaction [4, 14, 19, 68, 93].

Various models have been developed by researchers to measure the quality of services offered. One such model is SERVQUAL [72], which proposes reliability, responsiveness, assurance, empathy, and tangibles as the five dimensions of service delivery quality. However, the model has been criticized by some scholars for not sufficiently reflecting service quality dimensions specific to the hospitality sector [1]. As a result, researchers have created domain-specific models such as HOLSERV [58] and LODGSERV [53] that more effectively reflect service quality dimensions in the lodging industry. Based on these existing models, Wu and Ko [94] developed a comprehensive scale to measure service quality in the accommodation sector called the Hotel Service Quality Scale (SSQH). This scale includes the following dimensions of service quality: behavior, expertise, problem solving, atmosphere, room quality, facility, design, location, sociability, effort, and waiting time. Although there are differences in the service quality characteristics included in the above-mentioned models, researchers generally agree that multiple dimensions [13] best capture service quality.

The quality of the accommodation infrastructure is a crucial service dimension for the hotel industry [94]. This dimension includes various aspects such as interior design [55, 95], infrastructure design [6, 10], atmosphere [12], which encompasses lighting, music, noise, temperature, signage, and wall color, as well as cleanliness [55, 63, 80]. All of these factors are essential in determining customer satisfaction and are thus crucial factors that potential customers consider when forming their expectations and deciding on an accommodation and making reservations.

The conduct and demeanor of employees constitute the second critical aspect that impacts both customer satisfaction and their decision to choose a particular accommodation [16, 19, 72, 94]. Attitude refers to employees’ attributes, such as friendliness, kindness, politeness, empathy, honesty, and attentiveness [20]. Several studies suggest that service providers can significantly benefit from comprehending their customers’ expectations and evaluations of their staff's attitudes [16, 18, 89, 97]. Additionally, many studies establish a clear correlation between employee attitude and customer satisfaction [2, 40, 68] and consider staff behavior to be a significant factor in selecting an accommodation [24].

The third dimension refers to the interactions between customers during the service delivery process, which are critical to the hospitality experience. Studies have shown that customer interaction is an essential component of customer service quality evaluation and a determinant of customer satisfaction. Additionally, customers expect no negative interactions between customers during hospitality.

The fourth dimension, the level of employee experience, describes how the skills and knowledge of employees influence customer-employee interactions when they perform specific tasks. The level of employee experience largely determines the quality of employee interaction with customers, and employees’ problem-solving skills contribute to positive evaluations of customer interaction quality with a hospitality service provider. This dimension of service quality appears to influence customer satisfaction and is another factor in the choice of an accommodation by potential customers.

The fifth dimension relates to the quality of food and beverages provided, which affects satisfaction based on the availability of a sufficient variety of food and beverages, their overall quality, food hygiene, and service level. There is a validated relationship between food and beverage quality and customer satisfaction, and the role of food and beverage quality in choosing an accommodation has been identified.

The sixth dimension is reception quality, which includes the adequacy and speed of the check-in process, the baggage transfer process, and the ability of front desk staff to solve problems. Front desk staff performance has the greatest impact on customer overall perception of service quality and satisfaction and is a critical factor in choosing an accommodation.

The seventh dimension is room quality, which includes aspects such as room size, temperature, quietness level, and how comfortable the mattresses and pillows are. Hotel room quality has been identified as the strongest determinant of customer satisfaction and is an important dimension of the quality of services provided.

The eighth dimension is security, which includes the protection of people, hotel premises, customer property, and ensuring the personal safety of both employees and customers. Security features and facilities are a primary concern for travelers around the world and remain a critical accommodation selection factor as well as an important dimension of service quality that determines overall satisfaction with hospitality services.

The ninth dimension is sociability, defined as the positive social experience gained from the sense of satisfaction from being with other people who are all participating in the same activity and sharing the enjoyment. This dimension is an important factor in customer satisfaction and has been identified as a determinant of overall satisfaction with hospitality services.

The final dimension, the tenth one, pertains to waiting time, which refers to the duration that customers must wait to receive a service [8, 36]. Upon entering a hospitality space, customers typically have expectations of a reasonable waiting time for each service they require, which in turn affects their overall satisfaction levels [21, 54, 82]. Waiting is often seen as a frustrating experience by customers in the service industry [57]. In the study of Houston et al. [37], waiting time was included as a significant predictor of customer satisfaction with hospitality. Similarly, Nunkoo et al. [68] found waiting time to be an essential component of service quality, affecting customer satisfaction. Waiting time is closely linked to the sixth dimension that includes the duration of wait time during check-in at an accommodation site and can influence a potential customer’s decision on their choice of accommodation [24].

With the emergence of Web 2.0 and the growing popularity of digital booking platforms in the accommodation industry, online evaluations of various accommodations have significantly impacted the industry beyond the dimensions mentioned earlier [61, 96]. According to research, potential customers increasingly value these publicly available reviews, which have become a key factor influencing their choice of accommodation [60, 96]. In fact, awards such as the TripAdvisor Excellence award, which are not necessarily directly linked to user evaluations or service quality, have been found to play a significant role in the selection process of potential customers [60].

Lastly, the notion of “value for money” plays a crucial role in both the satisfaction of guests with their stay and the selection process of potential customers. This particular concept does not represent a distinct feature but rather encompasses the services provided, the cost, and the marketing strategy of the property. Even after taking into consideration the impact of the various dimensions we previously explored, which are associated with customer satisfaction and the selection of accommodation, studies indicate that the influence of “value for money” remains substantial [23, 64, 77]. Consequently, we chose to investigate its effect independently in this study.

All the factors mentioned above are crucial to attracting and satisfying customers, and therefore are of utmost importance to the senior management of four- and five-star hotel units. As a result, investment options should be segmented based on the factors that top management considers to provide their company with a comparative competitive advantage. Therefore, these factors will form the basis of our research as important customer satisfaction factors and significant determinants of hotel accommodation selection should share the investment decisions of business executives in the tourism industry as suggested by Dolnicar and Otter [24].

Additionally, several studies have found that demographic factors, such as gender and age, can significantly influence a customer’s decision-making process, risk tolerance, and satisfaction. For instance, research has shown that women tend to be more risk-averse than men and are less likely to overestimate their potential, leading them to avoid perceived high-risk stocks in financial investments. Similarly, in terms of consumer satisfaction, women may differ in the factors that create a feeling of satisfaction compared to men, according to studies conducted by Voss and Cova [86] and Anselmsson [5].

2.3 The impact of the COVID-19 pandemic on factors influencing the selection of accommodations and customer satisfaction in the hotel industry

In December 2019, a new strain of coronavirus called SARS-CoV-2 emerged in Wuhan, China. Like SARS, this virus is airborne and easily spreads between people, causing a disease known as COVID-19. In response to the Chinese government’s announcement of the new disease in January 2020, strict measures such as quarantines and business closures were implemented in many major cities. However, unlike SARS, this outbreak quickly turned into a global public health crisis and was declared a pandemic by the World Health Organization in March 2020. The pandemic spread rapidly across all continents and countries, with Europe and the United States becoming the epicenters of the outbreak in March 2020. As of August 1, 2020, COVID-19 had been confirmed in over 190 countries, with more than 17 million confirmed cases and 674,291 deaths worldwide. The actual number of cases is believed to be much higher than the official statistics, as only confirmed cases with significant symptoms are included in the count.

Despite the fact that the tourism industry has faced financial setbacks in the past due to unforeseeable external factors, this current crisis appears to be distinct. The COVID-19 pandemic is not just a typical shock, but rather an economic super-shock. An economic super-shock refers to a significant alteration in essential macroeconomic factors or economic relationships that have a considerable impact on macroeconomic outcomes and on the fundamental aspects of an economy that are interconnected with them, such as inflation, consumption, and unemployment [25].

Historically, economic shocks have been commonplace. For instance, events such as the war in Iraq and the outbreak of severe acute respiratory syndrome (SARS) in 2003 caused global economic growth to decline to 3.2% [44]. Additionally, the global economic crisis of 2009 resulted in a recession of − 1.3% [45], while the emergence of COVID-19 was projected to bring about a recession of nearly − 3% in 2020 [46]. However, what sets the current crisis apart from previous shocks is that no economy has been left unaffected, making it the most severe crisis since the Great Depression [90]¹.

The tourism industry is no stranger to economic shocks caused by unforeseeable factors. Tourist destinations have experienced the impact of events such as cyclones, fires, earthquakes, and terrorist attacks, and have implemented measures over the years to mitigate the risks [78]. However, the super-shock caused by COVID-19 was unique in that it brought the non-essential economy to a halt for three crucial reasons. Firstly, the resulting economic shock and subsequent decrease in travel traffic were global in scale. The tourism industry is particularly vulnerable to outbreaks of infectious diseases, including COVID-19, as it relies heavily on the mobility of potential guests. According to data published by the United Nations World Tourism Organization (2020), international tourist arrivals decreased globally by around 20–30% as of April 2020. Secondly, the economic super-shock caused by the pandemic was more severe than regular shocks, with economic growth reductions twice as large [25]. The pandemic’s economic effects have led many travelers to significantly reduce or postpone any travel expenses, far more than any other category of expenditure. According to a recent survey by the Institute of the Association of Hellenic Tourism Enterprises [43], for the top five tourist markets from which Greece welcomes visitors, the rates of postponing tourist expenses range from 40% for Germany to 50% for Italy, and even higher at 61% for Chinese visitors. Consulting research has shown significant effects in the first half of 2020 compared to the same period in 2019, with regards to room availability, occupancy rates, and financial performance of hotel units in Athens and Thessaloniki, as illustrated in Fig. 2 [28].

Fig. 2

Age group of participants

In addition, the shock caused by the pandemic had the potential to bring about structural changes in certain sectors of the tourism industry [25]. This is due to the fact that many of the measures implemented to combat the spread of the virus, such as heightened levels of cleanliness in hotel premises, the use of personal protective equipment by staff, and the redesigning of service procedures to reduce personal contact, may become permanent fixtures in the hotel industry.

3 Methodology and data

This research employs structured questionnaire as its methodology.² The questionnaire contains closed-ended questions with categorical variables to explore participants' demographic characteristics, such as gender, age, level of education, and job. The questionnaire also presents hypothetical scenarios for the distribution of available investment resources before and after the spread of COVID-19. Categories were created to contain the eleven critical attributes identified in the literature review,³ which are essential for customer attraction and satisfaction. To record the answers, a finite budget is provided, which respondents can allocate among all potential fields of investment according to their preferences. The use of a finite budget is motivated by the fact that scales that do not consider resource scarcity and cost dimensions are not appropriate for option prioritization research. Using a finite budget offers advantages such as accurately reproducing the real problem of ideal resource allocation that hotel units face, without changing the context in which the problem is examined. This is important since changing the context of expression and formulation of the question can influence responses [11, 47, 51, 52, 81, 84]. The use of a Likert scale, for example, removes the monetary value contained in an investment in a hotel unit and reduces the respondent’s mental connection to the concept of cost and potential risk that such an action entails, problems that are overcome by the method of allocating a finite budget.

The survey also includes questions about the overall impact of the COVID-19 pandemic on investment planning and costs in the hotel industry. Participants are asked to rate their level of agreement with statements related to these topics using Likert scales and other similar ordinal variables. These types of responses are suitable for capturing emotional responses and non-numerical dimensions [50]. Unlike other categorical variables, Likert scales enable a wide range of statistical analysis methods to be used to interpret the results, such as the χ² test of association and ordinal regression.

The study’s sample size comprised forty-one adult participants who were randomly selected to complete the questionnaire. The survey was aimed at owners or senior managers of four- and five-star hotel units located in Crete. These hotel units are independent hotels and they don’t belong to any hotel chain with presence outside the island of Crete. With this hotel selection, we avoid any exogenous factor influence in the decision-making process of the participants. The data collection was carried out through face-to-face interviews, and all participants provided complete responses. The sampling process occurred between December 2020 and January 2021, a period in which the participants had the opportunity to experience a full tourist season under the measures implemented to mitigate the spread of the coronavirus pandemic.

We performed a four-step analysis of our results. Firstly, we presented the responses obtained from each sample. Secondly, we compared the results between the two hypothetical scenarios of the questionnaire, which were investment planning before and after COVID-19. We used the Welch t-test to check for statistical significance in any observed differences [87]. This test was chosen because:

• participants could allocate a numerical amount of available resources to each factor,

• it examines the mean value as well as the dispersion about the mean of all observations within each category,

• the Welch t-test allows for unequal variances between the factors being compared [79, 98] which may be the case in our observations since they are comparisons of groups of observations for the same factor before and after the occurrence of a completely unpredictable event (i.e., the pandemic and measures to combat it). These events may have radically changed the individual variances, and

• the control Welch t-test produces reliable results even in cases where the compared groups of observations have equal variances [22].

Thus, the format of the hypothesis test for this second phase will be as follows:

During the third stage of our analysis, we employ cross-tabulations and χ² tests of association to explore the relationship between the ordinal variables in our questionnaires. These variables include demographic data and general questions related to the impact of the pandemic. This enables us to identify significant statistical correlations and interpret them accordingly. The underlying assumptions for this analysis are as follows:

Finally, in the fourth and last stage of our analysis, we utilize regression models to examine the relationship between specific factors, such as demographics, and the various choices made by participants concerning the negative impact of the pandemic and the level of expenditure restraint. As the dependent variable in our study is an ordinal variable, we use ordinal regression models to analyze the data. Ordinal regression is a regression technique used to determine the value of an ordinal variable based on the impact of other independent variables, whether numerical or non-numerical. A logit model is typically used, but probit or complementary log–log distributions can also be used. In our study, we use a probit distribution due to the distribution of the observations and the fit of the model. Ordinal regression can be seen as a generalized form of multiple linear regression and binomial logistic regression [32].

The modelling for probit ordinal regression which will be used in our work takes the following shape. Initially, a latent variable is assumed y*. This latent variable is determined by

$$ {\text{y}}^* = {\text{ w }} \cdot {\text{ x }} + \varepsilon , $$

where ε is normally distributed and has a zero mean and unit variance, conditioned on x. The response variable y cannot be measured with accuracy due to the latent nature of y* but it can only be determined based on the interval that y* falls.

So, y can take value 1 if y* ≤ k₁, y can take value 2 if k₁ ≤ y* ≤ k₂ and goes on the same way until y takes value N if k_N-1 < y*. By setting k₀ = − ∞ and k_N = ∞, the above can be summarized as

$$ {\text{y}} = {\text{N if and only if k}}_{{{\text{N}} - {1}}} < {\text{ y}}* \, \le {\text{ k}}_{{\text{N}}} . $$

From these assumptions, one can derive that the conditional probability of y is

$$ {\text{P }}\left( {{\text{y}} = {\text{ N}}\left| {} \right.{\text{x}}} \right) \, = \Phi \left( {{\text{k}}_{{\text{N}}} - {\text{ w}} \cdot {\text{x}}} \right) \, - \, \Phi \left( {{\text{k}}_{{{\text{N}} - 1}} - {\text{ w}} \cdot {\text{x}}} \right), $$

where Φ is the cumulative distribution function of the standard normal distribution, and takes on the role of the inverse link function σ.

The log-likelihood of the model for any single case of x_i, y_i can now be stated as

$$ \log {\mathcal{L}}\left( {{\text{w}},{\text{k}}\left| {{\text{x}}_{{\text{i}}} ,{\text{y}}_{{\text{i}}} } \right.} \right) = \sum\nolimits_{{\left( {{\text{N}} = 1} \right)}}^{{\text{N}}} {} \left[ {{\text{y}}_{{\text{i}}} = {\text{N}}} \right]\log \left[ {\Phi \left( {{\text{k}}_{{\text{N}}} - {\text{w}} \cdot {\text{x}}} \right) - \Phi \left( {{\text{k}}_{{{\text{N}} - 1}} - {\text{w}} \cdot {\text{x}}} \right)} \right] $$

Four assumptions must hold for the data to apply an ordinal regression model, including the ordinal nature of the dependent variable, the categorical, ordinal, or continuous nature of the independent variables, the absence of significant multicollinearity among the independent variables, and proportional odds among the independent variables. The first three assumptions are met in our data. We test for the existence of proportional odds in each model using the parallel lines test. The computational analysis was performed using Microsoft Excel and the SPSS statistical package.

4 Results and analysis

4.1 Presentation of survey results and comparison of investment options before and after the advent of the coronavirus pandemic

The first part of this section is devoted to the presentation of the survey results, while the second part studies the changes that occurred in the various characteristics that the owners and senior executives of four and five star hotels would invest in and by extension in their investment planning, by comparing how they distributed a given amount of money before and after the advent of the coronavirus pandemic and its consequences.

The first question has to do with the gender of the participants and according to the results, the sample consists of 22 men and 19 women, corresponding to a rate of 53.6% and 46.34%. Due to the fact that in the business world and especially when it comes to senior management, men tend to be slightly more represented than women, the sample can be considered representative in terms of participants’ stratification and gender.

The second question deals with the participants’ age group. Based on the results, 5 of the survey’s questionees, i.e. 12.20%, belong to the age group between 18 and 30 years old, 18, i.e. 43.9%, to the 30 to 45 age group, 14, i.e. 34.15%, to the 45–60 age group and lastly, 4, i.e. 9.76%, to the age group above 60 years. It has to be highlighted that, since the goal was only for hotel owners or senior executives to be included in the sample, it is to be expected that the majority of the participants would have an age between 30 and 60. It is understandable that a very young person is relatively unlikely to own a hotel or to have climbed so high up the career ladder so as to hold a senior management position. Though small, the percentage of younger questionees is not insignificant and a possible reason for this is that there is a great number of family-owned units. Moreover, it is quite uncommon for people over 60 to hold the sole ownership or, even more so, management of a hotel business. Conclusively, in this instance as well, the sample can be considered representative.

Concerning the third question, which requires the participants’ education level to be provided, the results (Fig. 3) demonstrate that 7.82% of the questionees (i.e. 3) are high school graduates, 24.39% (i.e. 10) are university graduates, 53.66% (i.e. 22) are university graduates who also hold a master’s degree (they constitute the majority of the sample), 7.32% (i.e. 3) have completed doctoral studies and 7.32% (i.e. 3) have completed other studies (mostly they have graduated from Institutes of Vocational Training –IEKs– that provide education and practical knowledge in the fields of tourism and finance at the post-secondary education level). It has to be noted that the observed stratification is, to a great extent, accordant with the stratification in Greek society, as OECD research [69] demonstrates that Greece is among the countries with the highest rates of university education, while it is also to be expected that a person who is the owner of a hotel or holds a senior management position will have graduated from a higher education institution.

Fig. 3

Education level of participants

Moving on to the fourth question regarding the respondent’s hotel position (Fig. 4), the results show that 58.54% of the sample (i.e. 24 people) are hotel owners, 19.51% (i.e. 8) are Chief Executive Officers (CEOs), in charge of the daily operations of the business, 14.63% (i.e. 6), are directors of finance, handling the financial management of the hotel and controlling its revenue and expenditure and 7.32% (i.e. 3) are investment directors, dealing with developmental work. It is important to note that most respondents are hotel owners, meaning either major shareholders or sole owners. A possible explanation is that in Greece and especially in the island of Crete, most large hotel units are family owned, which means that they are controlled by a limited number of individuals and the shares are not publicly traded on organized stock exchanges, even in the case of S.A. corporations, as this would result in multi-shareholding and dispersion of ownership beyond the close family circle. It is worth mentioning that the structure of the Greek hotel industry is very similar to that of Greek shipping, where family businesses that are not publicly traded are also prevalent. Additionally, the lower percentages of the respondents holding a position different than that of the owner can be attributed to the less extensive hierarchical stratification found in Greek hotels compared with that of hotel businesses in more developed economies, such as those of Western Europe, leading an owner to take on multiple roles, such as that of the Chief Executive Officer, the financial director or the manager responsible for the development planning. All the above suggest that the sample accurately reflects the actual structure of the hotel industry in Greece and particularly in Crete.

Fig. 4

Hierarchical position of participants

The fifth question provides participants with an opportunity to indicate their preferred investment destination for a hypothetical amount of three million euros if it became available to them in the year 2019, that is before the emergence of COVID-19 as a global pandemic, with the aim to enhance customer attendance and satisfaction levels with the services offered. The characteristics offered in the questionnaire are room infrastructure, employee attitude and behavior, customer interaction, food and beverage quality, front desk quality, room quality, security, hospitality experience, waiting time (not including the waiting time related with the initial registration at the reception desk), the engagement in booking platforms and the attributes that offer a value-for-money feeling. These characteristics are further divided into a number of subcategories. Table 3 displays the average investment amounts across the various subcategories a participant could choose from.

Table 3 Average investment amounts—scenario before the COVID-19 pandemic

By observing Table 3, it can be deduced that the attributes that received the highest average investment amounts in the scenario before the outbreak of the COVID-19 pandemic were room infrastructure, room quality and customer interaction. Food and beverage quality is also considered to be a vital characteristic, as significant amounts were allotted to it. The subcategories that have secured the highest average investment amounts were opportunities for interaction (that is the addition/improvement of numerous events and activities for customers), interaction areas (that is the expansion and enhancement of interaction spaces), noise levels (that is the soundproofing of the room), size of room and internal decoration. On the other hand, the characteristics of security and waiting time (not including check-in time) didn’t receive substantial average investment amounts, which potentially indicates that four and five star hotels in Crete have already achieved a high level, as far as these attributes are concerned, leading the participants to consider that no additional investment is deemed necessary.

As far as the sixth question is concerned, the same scenario described in the previous question is set, with the fact that the aforementioned amount of money becomes available to use in the year 2020, after the advent of the COVID-19 pandemic and the related containment measures, being the key differentiator. This notable difference is emphasized in the phrasing of the question to secure consistency in the responses and disallow other possible factors to affect the result. It has to be highlighted that the survey was conducted during December 2020 and January 2021 and the participants had already experienced a summer tourist season filled with the catastrophic effects caused by the coronavirus pandemic. As a result, any changes in investment plans, in comparison with the previous question, are expected to derive solely from the COVID-19 pandemic and all the measures that accompanied it. Table 4 demonstrates the results for investment averages per subcategory.

Table 4 Average investment amounts—scenario after the COVID-19 pandemic

By studying the results that pertain to the post-COVID period, as they are presented in Table 4, it becomes evident that the highest average investment amounts belong to the categories of employee attitude and behavior, room quality, room infrastructure and, to a lesser degree, to the value for money elements. Upon further examination of the various subcategories, it can be inferred that the survey participants, post-pandemic, have a tendency of investing the highest average investment amounts in room facilities, employee training, employee education when it comes to customer interaction, room cleanliness and special offers. On the contrary, the lowest average investment amounts pertain to the attributes of hospitality experience, security and customer interaction. The results as a whole might suggest that the tourism sector has experienced a significant loss of employees due to the COVID-19 pandemic, resulting in the need to enhance the services provided by new, untrained or disengaged staff. A comparative analysis of the two scenarios displayed above is to follow after the completion of the presentation of the remaining questions and their respective results.

The seventh question calls for respondents to estimate whether the outbreak of the COVID-19 pandemic has affected their investment planning, in a negative way, and to what magnitude. Via the aforesaid question, there takes place an effort to investigate the impact of the coronavirus pandemic on the hotel investment planning, from the participant’s perspective. Thus, apart from the quantitative changes in the investment priorities that were examined through the two previous questions, information can be now obtained concerning to what extent the respondents realize the consequences the COVID-19 pandemic has brought to the tourist industry. Moreover, this question shifts the focus from a hypothetical scenario, as described in the previous question, to the participants’ actual business planning.

To begin with, it is noteworthy that no respondent chose the ‘not at all’ option, which suggests that they all deem the outbreak of the COVID-19 pandemic and its consequences to have had an impact on their investment planning, at least to some extent. In addition, only 4 participants (i.e. 9.76%) believe that their investment planning was altered to a small degree due to the COVID-19 pandemic. It has to be mentioned that this is the second least frequently met answer among the questionees, which highlights that the vast majority of them (i.e. 90.24%) argue that their investment priorities have been affected at least enough by the changes the COVID-19 pandemic has brought to the tourism sector. On the other hand, 11 respondents (i.e. 26.83%) support that their investment planning has been reasonably affected, 17 (i.e. 41.46%) declare that their investment activity has been greatly altered and 9 (i.e. 21.95%) state that investment changes brought about by the COVID-19 pandemic were critical. Through the above mentioned, it is easily deduced that the percentage pertaining to the last two options surpasses 50%, it adds up to 63.41%, to be exact, which underscores the great need for hotel businesses to adapt their investment plans to the current situation caused by the COVID-19 pandemic. The presented results lead to the significant finding that even in the year 2020, when COVID-19 had just appeared, the consequences of the measures adopted to tackle it had already greatly affected hotel investment plans, many of which were designed to take place over the medium- to long-term.

The last question introduces another hypothetical situation where respondents are required to ponder whether they would hold onto a portion of the available for investment capital that was given in the sixth question and delay the investment, due to the potential impact of the coronavirus pandemic on their hotel business. Thus, this question constitutes an attempt to, firstly, determine whether the COVID-19 pandemic has caused a reduction in current investments and, secondly, to estimate the extent of this potential reduction.

According to the results, 4.88% of the sample (i.e. 2 people) would choose to withhold between 0 and 20% of the available for investment capital for future use considering the uncertain situation that has been created by the coronavirus pandemic, 9.76% (i.e. 4 people) would retain 21–40%, 29.27% (i.e. 12 people) would refrain from using 41–60%, 34.15% (i.e. 14 people) would hold onto 61–80% and 21.95% (i.e. 9 people) would not allocate more than 80% of the available capital. By studying the above mentioned, it becomes evident that few participants would decide to withhold less than 40% of the available investment capital. On the contrary, the vast majority of the respondents would take action against spending more than 40% of the available investment capital, thinking that more funds should be accessible to face the uncertain scenario created by the coronavirus pandemic. Additionally, there exists a fear that any investment at the given time would be doubtful to yield any results, as it would be restricted by the processes created to deal with the coronavirus pandemic. All the aforementioned demonstrate the great impact that the coronavirus pandemic and the circumstances it brought about had on the operation of the hotel industry. The significant findings of the last two questions demonstrate that the current situation is causing the postponement or even cancelation of businesses’ investment plans, leading the industry to a standstill or even disinvestment, taking into consideration that a minimum level of investment is necessary for maintaining the existing infrastructure. As a result, future growth is hindered and the long-term dynamics of such an important sector of the Greek economy suffer serious damage.

The following part of the analysis focuses on the changes in the characteristics that owners and senior management of four- and five-star hotels would invest in before and after the outbreak of the coronavirus pandemic by comparing the results of the fifth and sixth questions presented above. Table 5 depicts the aforesaid changes. Via the Welch t-test, the statistical significance of the differences observed is investigated. The subcharacteristics in bold denote statistically significant changes with the significance level set at 5% (Table 5).

Table 5 Changes in the investment amounts between the post-coronavirus pandemic situation and the pre-existing situation

In the following passage, we will discuss the characteristics and subcategories that have undergone statistically significant changes. Although our objective is to identify rather than interpret these differences, we will provide brief explanations for the potential reasons behind them. Overall, we have observed a small decrease of 1.45% (equivalent to 7,317 euro) in the investment amount for the first attribute of room infrastructure, from a total assumed investment capital of three million euro. While there is no significant change in the investment amount for the feature, there has been a considerable shift in the allocation of investment capital across its various subcategories. This is evident from the fact that all changes are statistically significant. Prior to the pandemic, the largest sums would have been allocated to individual features such as interior decoration, room design, and room atmosphere. However, following the emergence of the pandemic and the resulting changes in operating conditions for hotel units, we have seen significant reductions of 137,805 euro (90%) for the interior decoration subcategory, 53,659 euro (51%) for the room design subcategory, and 63,415 euro (91%) for the room atmosphere subcategory. In contrast, subcategories such as room appliances and room facilities have seen a significant increase in the investment amounts that our survey participants would be willing to spend under the new conditions. This increase amounts to 112,195 euro (214% compared to the corresponding funds before the pandemic) for the room appliances subcategory and 135,366 euro (111% compared to the corresponding funds before the emergence of the coronavirus) for the room facilities subcategory. One plausible explanation for this significant increase is that these subcategories contain elements such as air purifiers, sanitizers for personal effects, and air conditioning, which either did not exist or were not considered necessary before the pandemic. The new conditions brought about by the pandemic made them essential to ensure the health of customers and make the hotel proposition attractive to customers. Therefore, the top management executives participating in the research have allocated more funds to these subcategories, mainly by reducing the amounts they would have previously allocated to the other three subcategories—interior decoration, room design, and room atmosphere—before the pandemic.

We have observed a notable increase in investment resources (278,049 euro) for the second characteristic, which pertains to the attitude and behavior of hotel workers. This increase represents a threefold rise in the funds available for investment in light of the coronavirus pandemic and the measures implemented to address it. Furthermore, all individual components of this characteristic demonstrate an increase in investment under the new conditions. In particular, the investment amounts for two out of three categories of this characteristic, namely employee training and process design, exhibit statistically significant changes. After the pandemic-induced changes, the survey participants are willing to invest an additional 123,171 euro (a 113% increase) in staff training and an extra 145,122 euro (a 1983% increase) in process planning.

From these findings, it becomes evident that under the new conditions, adopting specific procedures to enhance staff performance and training employees to the new standards of behavior and service has become crucial in the minds of hotel owners and top executives. This could also be attributed to the need to train new staff to fill in the gaps left by the loss of a significant portion of the workforce due to COVID-19, and subsequently improve the services provided by new or untrained or disengaged staff during the pandemic. It seems that these measures can attract customers in the current environment, which may be why there is a substantial increase in investment in this particular area.

Next, on the third characteristic, which is customer interaction, we can see a significant decrease in investment funds, amounting to 467,073 euro, which corresponds to 52.39% of the pre-pandemic levels. We can also observe a decrease in the willingness of survey participants to invest in individual elements such as creating or expanding/improving interaction spaces, with a decrease of 289,024 euro (75%), and in creating new interaction opportunities or events for customers, with a decrease of 381,707 euro (97%). These reductions are both economically and statistically significant. In contrast, we can observe an increase in the amount of investment for the development of dispute resolution mechanisms by 109,756 euro (1125%), and for staff training in customer interaction by 93,902 euro (95%). These specific increases also appear to be economically and statistically significant. These figures for the individual subcategories seem reasonable since the decrease in willingness to invest in customer interaction areas and in the creation and enrichment of various events is likely due to the new reality characterized by measures taken to deal with the coronavirus pandemic, future waves of this pandemic, and potential future outbreaks of other viruses. These measures aim to minimize human contact, and participants in the sample believe that they will last for some time in the future, as reflected by their modified investment options. This is consistent with our earlier explanation for the increase in investment in room appliances. Lastly, the increase in investment for the development of conflict resolution mechanisms between customers and further training of staff in customer interaction also seems to be a result of the new conditions brought about by the pandemic. The adoption of new measures to deal with the pandemic has created a new large field of disputes that could arise between customers or customers and staff. Dealing with these new disputes in the best way, as shown by the changes in investment choices of the participants, is becoming an important area of competitive advantage that each hotel tries to improve immediately.

Moving on to the fourth characteristic, we can see a reduction in investment funds for this feature, with an average drop of 50,000 euros (or 17.67%) due to the new conditions brought about by the coronavirus pandemic and the corresponding measures taken to combat it. What’s interesting is that although there isn’t a significant decrease in the investment amount for the feature as a whole, there is a wide range of variations in the amounts allocated to the individual elements that make up this characteristic, according to our survey. Specifically, we note a significant decrease in the willingness of hotel owners and senior executives to invest in expanding the variety of food and beverage offerings by 93,902 euros (or 76%) and 75,610 euros (or 84%), respectively, under the new conditions. It’s worth noting that statistical tests confirm the significance of these decreases. Meanwhile, the average investment amount in the level of service doesn’t show a statistically significant change. However, we do observe a significant increase in the investment amount for food hygiene, with participants in our survey willing to spend 123,171 euros (or 1122% increase) more on this element, as confirmed by a statistically significant p-value. This clearly indicates the increased importance of hygiene and cleanliness in the post-coronavirus era, even for four- and five-star hotels with already high standards. Moreover, given the new mandatory hygiene and cleanliness measures imposed by many governments, including Greece, it’s likely that hotel top management will undertake more investments in food hygiene to comply with these new regulations. It’s possible that this increased need for investment in hygiene has caused a decrease in investment funds for the other individual elements in this characteristic.

Regarding the fifth characteristic, we can see a substantial increase of 136,585 euros (a 145.45% rise) in the amount that hotel managers would be willing to invest in improving the quality of the reception desk under the new conditions brought about by the pandemic. This is understandable since the reception desk is one of the few points of contact between hotel staff and customers that cannot be eliminated or reduced significantly. Thus, allocating more resources to enhance this contact is likely to be seen as a way of safeguarding the health of both customers and staff. The study also shows a significant increase in investment in the speed of check-in and reducing wait times by 76,219 euros (a 1250% increase). This increase can be attributed to various measures implemented to deal with the pandemic, such as limiting the number of people in enclosed spaces and avoiding overcrowding. As a result, waiting times have increased, and customers are often left waiting outside hotel premises. Investing more in this area is likely an attempt to address this issue. Moreover, there is a statistically significant rise of €18,902 (a 517% increase) in investment allocation for improving baggage handling processes. This increase may involve purchasing new equipment, hiring additional staff, or introducing new procedures. These changes are likely part of the same effort to minimize human contact and socialization.

When analyzing the sixth characteristic, which pertains to the quality of the rooms, it is evident that the willingness of the sample participants to invest in this characteristic has decreased overall. This results in a reduction of 147,561 euros to be invested. Although the reduction initially appears to be significant, it is not as substantial when considering the characteristic as a whole, as it only constitutes a 23.82% reduction in investment planning compared to the scenario before the pandemic, where this particular characteristic had one of the largest invested funds. However, when examining the investment funds of the individual sub-categories, significant decreases and increases in amounts are observed. All of these changes are statistically significant according to our controls. Specifically, the investment amounts in the sub-categories of room size, heating, and noise decrease by 167,073, 56,098, and 158,537 euros, respectively, under the new conditions. These percentage decreases amount to 87%, 49%, and 73%, respectively, compared to the invested amounts in the respective sub-categories in the pre-coronavirus pandemic investment scenario. In contrast, significant increases in amounts are observed in the other two sub-categories of the characteristic under the new conditions. The sub-category of mattresses and pillows shows an increase of 58,537 euros to be invested, which represents an increase of approximately 65% compared to the funds to be invested in the scenario before the pandemic. Furthermore, the subcategory of room cleanliness increases by 175,610 euros, which is a twenty-fourfold increase compared to the scenario before the pandemic. These changes are likely due to the renewed importance placed on cleanliness and hygiene, even though four- and five-star hotel units already maintained high levels of cleanliness and hygiene before the pandemic. Both the sub-category of cleanliness and that of mattresses and pillows, which come into direct contact with guests’ bodies and faces and are largely periodically consumable, show substantial increases in the allocation of investment amounts under the new conditions. These amounts seem to be largely reallocated from the sub-categories of room size, heating, and noise, which appear to take a back seat under pandemic conditions.

Furthermore, the security characteristic shows a notable increase of 30,488 euro in investment funds (32.89% higher than the pre-pandemic scenario). This increase solely stems from the statistically significant change in the subcategory of room security equipment. Specifically, participants are willing to invest 57,317 euro more in this subcategory, which is more than double the proposed investment in the pre-pandemic scenario (a 261% increase). This substantial increase may be due to the fact that room ventilation systems are included in this category, which may need an upgrade to limit the spread of the virus and ensure the smooth operation of the hotel.

As far as the hosting experience is concerned, we see a significant decrease in the amount of investment capital that participants are willing to allocate to this attribute. All individual elements of this characteristic show a decrease in investment funds in the post-pandemic scenario. However, only the reduction in the public relations expenses subcategory is statistically significant, with a decrease of 19,512 euro (84% less than pre-pandemic investments). In general, this characteristic seems to have decreased in investment funds after the pandemic, possibly because other features related to cleanliness and hygiene took precedence in the minds of hotel management.

Concerning the characteristic of reducing service waiting times within the hotel premises (excluding reception), we observe a substantial increase of 67,073 euro under post-pandemic conditions. This is over 13 times the amount that sample participants were willing to invest in the pre-pandemic plan, representing a significant increase of 1.375% and a statistically significant p-value. The large increase is likely due to two factors. Firstly, pre-pandemic, four- and five-star hotels had relatively satisfactory service times and therefore required minimal investment in this category. However, the pandemic has likely drastically changed this situation due to restrictions on crowding in enclosed spaces, limited movement in public areas, and the implementation of various security protocols, all of which have significantly increased service times. This has made significant investment in reducing service times necessary.

Regarding the category of booking platforms, we can see that there is a slight increase of 609.76 euro in the post-pandemic investment plan. This corresponds to only a 0.32% increase compared to the pre-pandemic investment plan. Essentially, the investment amount in this category remains stable economically in both scenarios. However, we can observe a significant change in the subcategory related to award participation. Specifically, we can see a reduction of €17,073 in this subcategory under the new conditions, which is a significant 58% decrease compared to the investment amount before the COVID-19 pandemic. This reduction could indicate that the importance and value of this subcategory as a means of attracting customers is diminishing in the minds of top management in four- and five-star hotel units, given the new environment that is developing.

Concluding our analysis, we turn to the “value for money” category, where we observe a significant increase in the amounts participants would allocate under the new conditions. Specifically, the amount allocated to this category almost triples, with an increase of 200,091 euro (a percentage increase of 188.59%). The sub-category of special offers sees the largest increase, with 120,732 euro (a percentage increase of 202%). Moreover, the free services subcategory shows a considerable increase, with 60,976 euro invested, equivalent to 25 times the investment before the pandemic. The sub-category of free upgrades also sees an increase of 33,537 euro. These increases reflect the growing importance of offers, free benefits, and upgrades as competitive advantages for attracting customers in a time of decreasing tourism and declining customer spending. Notably, all of these changes are statistically significant.

4.2 Analysis on the correlation between individual characteristics and perceptions of the pandemic’s impact on investment planning and spending

In this section of the analysis, we will investigate whether there exists a correlation between personal and demographic characteristics of top management members in four- and five-star hotel units and their perception of the pandemic’s impact on their investment planning and spending. Specifically, we will explore the relationship between responses to questions related to personal information, such as gender, age group, education level, and position in the business, and responses to questions 7 (Has the coronavirus pandemic negatively changed your investment planning?) and 8 (Has the coronavirus pandemic negatively changed your investment planning?). These questions inquire about the negative effects of the pandemic on investment planning and the inclination to limit investment costs due to the pandemic’s impact and measures taken to address it.

Initially, we will conduct our analysis by examining each personal characteristic paired with either question 7 or 8. The purpose is to determine whether there is a correlation between a respondent’s demographic information and their investment planning and willingness to spend on investments in light of the pandemic. To perform this initial analysis, we will take two steps as described in our methodology section. First, we will generate pairwise cross-tabulation matrices (Table 6) to identify any relationships between a respondent's answers to one question and their answers to the other. As both questions use Likert scales or categorical data, it is not possible to use the Pearson correlation coefficient [73] due to the non-quantitative nature of the data [31]. Second, we will conduct a χ² statistical correlation test to determine whether the correlations observed in the cross-tabulations are statistically significant, indicating a significant relationship between the two questions. The resulting Cramér’s V or Cramér’s phi value will also be presented to show the strength of the correlation between the two variables under consideration.

Table 6 Age and willingness to limit investment spending due to COVID-19

Upon conducting the two aforementioned steps, we only identified a correlation in one pair that studied the relationship between age group and the inclination to restrict investment expenditure during the pandemic. The correlation is presented in the following cross-tabulation that displays the two categorical variables: the participant’s age and their response to question 8 concerning their readiness to limit investment spending due to pandemic-related uncertainties and their repercussions.

To keep things brief, we will not provide an extensive description or analysis of the table. Instead, we will move on to conducting a statistical analysis of the relationship between the two variables using the χ² statistical correlation test and Cramér’s phi value. To do so, we need to state our null and alternative hypotheses:

• H0: There is no correlation between the age of a top management executive in a four- or five-star hotel and their decision to limit investment spending due to the pandemic’s impact on business activity.

• H1: There is a statistically significant correlation between the age of a top management executive in a four- or five-star hotel and their decision to limit investment spending due to the pandemic’s impact on business activity.

Based on the results of the χ² correlation test, it is evident that there exists a statistically significant relationship between the two variables since the p-value of the test is 0.025, which is much lower than the predetermined level of statistical significance α = 5%. Therefore, we can reject the null hypothesis, which suggests no correlation between the two variables, and accept the alternative hypothesis that a statistically significant correlation exists between them. Additionally, the value of Cramer’s bi (0.436) is greater than that of previous tests where we failed to detect statistically significant correlations, indicating a strong relationship between the two variables under consideration.

To summarize, we have found a significant correlation between the age of top management executives of four- and five-star hotels and their decision to retain a portion of available investment funds due to the pandemic. Moving forward, we will investigate whether this correlation remains strong when considering all demographics together and utilize ordinal regression analysis to interpret any identified correlations.

Based on our methodology, we will create two ordinal regression models to examine the relationship between demographic and personal information of the participants (gender, age group, level of education, and administrative position) and their investment planning during the pandemic. In the first model, the dependent variable (y₁) will be the negative change in investment planning due to the pandemic as described in question 7 of the questionnaire (Has the coronavirus pandemic negatively changed your investment planning?). In the second model, the dependent variable (y₂) will be the level of investment spending restraint due to the pandemic, as identified through question 8 of the questionnaire (If you had the investment capital referred to in the question 6 what percentage would you choose to hold by postponing investment due to the uncertainty caused by the coronavirus pandemic and its potential impact on your business?). By using these models, we will be able to identify which independent variables have a statistically significant interpretive value for the changes observed in the dependent variable.

In the following section, we will present the findings and observations from the two ordinal regression analyses. The first model includes independent variables (x_i) such as gender, age group, level of education, and job position, while the dependent variable is the negative impact of the pandemic on investment planning (info on its model fit on Tables 7, 8).

Table 7 Model fitting statistical test—ordinal regression: investment planning priorities

Table 8 Goodness-of-fit statistical test—ordinal regression: investment planning priorities

To assess the explanatory power of our model, it is important to note that ordinal regression does not have a measure of explanatory power such as the coefficient of determination or R², unlike OLS regression. However, some researchers have developed alternative coefficients similar to R². In our model, these coefficients range from 0.769 to 0.932 (Table 9), indicating that a significant portion of the dependent variable (level of negative investment planning change) can be explained by the independent variables (individual characteristics). This finding supports the idea that a participant's individual characteristics play a role in determining their perception of the negative impact of the pandemic on their investment planning.

Table 9 Pseudo R-square values of ordinal regression—ordinal regression: investment planning priorities

In the next stage of our analysis, we will evaluate the interpretive value of each independent variable by examining the statistical significance of its coefficient.⁴ To accomplish this, we will review the regression results presented in Table 8⁵ which report the coefficients of the independent variables. Based on the results presented in this table, it is evident that all age group categories are statistically significant at a significance level of α = 5%. Therefore, it is reasonable to suggest that age influences the perception of the negative impact the pandemic has had on investment planning, with older participants demonstrating a more negative reaction to the changes in investment planning. However, the gender, level of education, and job position of the executives in the four- and five-star hotel units do not appear to have any impact on their perception of the negative change in investment planning caused by the pandemic.

To conclude the analysis of the regression results, it is necessary to mention the parallel lines test. The test indicates whether the assumption of proportional probabilities is met or not. In the specific model we used, the test results show that the assumption is fulfilled as the p-value (1) is higher than the level of statistical significance α = 5% (Table 10).⁶

Table 10 Test of parallel lines results—ordinal regression: investment planning priorities

To sum up, it appears that the age of executives in the top management of hotel units, similar to those we are studying, influences their perception of the extent of negative changes in investment planning caused by the pandemic.

The second model considers individual characteristics of the participants as independent variables (x_i), including gender, age group, educational level, and job position. The dependent variable is the level of investment cost limitation resulting from uncertainty and the impacts of the coronavirus pandemic (info on its model fit on Tables 11, 12).

Table 11 Model fitting statistical test—ordinal regression: investment spending

Table 12 Goodness-of-fit statistical test—ordinal regression: investment spending

Based on the data presented in Table 12 and corresponding to the previous regression, it can be observed that at least one of the model’s estimators differs significantly from zero. This indicates a strong level of goodness-of-fit for the model.

Furthermore, we can observe that the alternative coefficients to the coefficient of determination in our model range from 0.768 to 0.943 (Table 13). This range suggests that a substantial portion of the dependent variable (i.e., the level of investment spending restraint) can be explained by the independent variables in our model (i.e., individual characteristics). This finding supports the notion that a participant's individual characteristics influence their inclination to withhold investment resources due to uncertainty and the impact of the coronavirus pandemic.

Table 13 Pseudo R-square values of ordinal regression—ordinal regression: investment spending

To further analyze the independent variables in our model, we can examine Table 9, which displays the regression results for the coefficients of these variables. We can see that all age group categories are statistically significant at a level of α = 5%, indicating that an executive's age may influence their level of investment spending restraint, with older executives showing a higher inclination towards investment spending restraint.

Moreover, all job positions except for CEO are significantly related to the rate of limitation of investment expenses. This finding suggests that an executive's position can also play a role in their opinion on the level of investment spending restraint due to the pandemic.

However, gender and educational level do not seem to have a significant effect on the level of investment spending restraint among executives of four- and five-star hotel units.

Finally, the results of the test of parallel lines for this model indicate that the assumption of proportional probabilities holds as the p-value of the test (1) is greater than the level of statistical significance α = 5% (Table 14).⁷

Table 14 Test of parallel lines results—ordinal regression: investment spending

To summarize, our analysis suggests that an executive's age is a significant factor in their level of investment spending restraint due to the pandemic. Moreover, an executive's position can also play a role in their opinion on the level of investment spending restraint. Specifically, non-CEO positions are significantly related to the rate of limitation of investment expenses. Thus, both age and job position appear to influence an executive’s decision regarding investment spending restraint in the face of pandemic-related uncertainty.

5 Conclusion

This article presents findings that demonstrate how the investment priorities of top executives in four- and five-star hotels have significantly changed due to the emergence of COVID-19. Prior to the pandemic, the main investment priorities for these hotel units were focused on infrastructure and room quality, as well as initiatives that promoted customer interaction. However, since the pandemic, investment priorities have shifted towards employee behavior and attitudes, as their role in ensuring customer satisfaction has become increasingly important under the new conditions. The sense of “value for money” that hotels offer customers is now achieved through offers, free services, and upgrades, as investments in customer interaction have become less of a priority. Hygiene, cleanliness, and reduced service times to avoid crowding are now considered absolute investment priorities for top management and provide a significant competitive advantage in the current environment. Notably, the most significant changes in investment allocation are observed in areas such as room cleanliness, food hygiene, service times, check-in procedures, staff training, and conflict resolution mechanisms. Conversely, investments in spaces that promote customer interaction, as well as infrastructure investments focused on room size, interior decoration, and atmosphere, have decreased in significance in the new environment. The statistical significance of these changes was verified using the Welch t-test.

In the subsequent stage of our analysis, we aimed to investigate whether certain individual characteristics of participants, such as their gender, age, education level, and hierarchical position within the company, could systematically explain their perception of the changes in their investment planning caused by the pandemic and their willingness to curtail investment costs in the new environment. Previous studies [5, 7, 29, 86] have suggested that such factors can play a crucial role in shaping financial decisions, and our research supported this finding. Through the use of χ² tests and Cramer’s V value, we identified a significant association between the age of hotel executives and the extent to which they aimed to reduce investment spending after the pandemic. By employing ordinal regression models that included all individual characteristics as independent variables, we discovered that an executive’s age influenced their perception of the negative impact of the pandemic on their investment planning. Furthermore, our analysis indicated that both age and hierarchical position within the company could be factors that affect the level of investment cuts deemed necessary by executives in response to the post-pandemic environment.

Our research could have significant managerial implications for the tourism industry in Mediterranean countries and other countries which focus on summer tourism. Firstly, to the best of our knowledge, it is one of the first studies (if not the first) that presents a clear view of the change that occurred in the investment priorities of luxurious hotels’ executives due to the pandemic. In this way, it illustrates the new shaping of the tourism industry in the post-pandemic era. For this reason, it can be an invaluable guide for new professionals entering the luxurious hotel industry as executives or personnel in departments related to the investment strategies of the hotels. In addition, it can help new businesses which want to expand their operation in the luxurious hotel industry showing them the consensus of the industry about the investment priorities which can lead to a competitive advantage in the new environment formed after the COVID-19 pandemic. Our study also can help the relevant government authorities to align any government development aid program towards the luxurious hotel industry by informing them about the new chartography of investment priorities that the post-pandemic era brought. Furthermore, our research could also contribute to the effort of business owners in the luxurious hotel industry in their quest for finding appropriate personnel for their investment management. Our work shows that factors such as age and hierarchical position in the business structure play a role in the response of the personnel facing a emergency crisis like the COVID-19 pandemic in the area of company investments, informing the hiring policies of the luxurious hotel industry in such a setting. Finally, our study could be informative to other business professionals as well as the customers of the luxurious hotel industry about the luxurious hotel industry investment focus helping them set their expectations.

5.1 Limitations and avenues for further research

Our study presents some limitations which are based on its setting. Firstly, it focuses on luxurious hotels in a Mediterranean island. This fact means that our results are not representative of hotels that do not belong to the luxurious hotel segment (4* and 5* hotels). Moreover, luxurious hotels, which are not focused on summer vacation tourism but are mainly focused in other forms of tourism (business tourism, winter tourism), also fall outside the scope of the present research. In addition, hotel chains who operate in many different geographical areas or hotel segments and take their investment decisions on a central base for the totality of their hotel units are also not represented in our survey.

These limitations of our research also point to potential avenues for future research. Future researchers could look on how the pandemic affected the investment choices in other hotel segments (3*, 2*, 1* hotels). Additionally, a future survey of how hotel establishments which focus on other forms of tourism (business tourism, winter tourism, agrotourism etc.) were affected by the pandemic would be exceptionally important and informative. Finally, a future investigation of the shaping of the investment strategy in the new post-COVID-19 era of big hotel chains with multiple geographical and hotel segment presence would be of interest.