doi: 10.56294/sctconf2024954

 

Category: Finance, Business, Management, Economics and Accounting

 

ORIGINAL

 

Assessing the influence of sustainable practices on guest satisfaction and loyalty in the hotel industry: An empirical investigation

 

Evaluación de la influencia de las prácticas sostenibles en la satisfacción y lealtad de los clientes del sector hotelero: Una investigación empírica

 

M. Srividya Iyengar1, R. Venkatesh2 *

 

1VIT Business school, Vellore Institute of Technology, Chennai, India.

2Higher Academic Grade, VIT Business school, Vellore Institute of Technology, Chennai, India.

 

Cite as: Srividya Iyengar M, Venkatesh R. Assessing the Influence of Sustainable Practices on Guest Satisfaction and Loyalty in the Hotel Industry: An Empirical Investigation. Salud, Ciencia y Tecnología - Serie de Conferencias. 2024; 3:954. https://doi.org/10.56294/sctconf2024954

 

Submitted: 13-02-2024                   Revised: 02-05-2023                   Accepted: 30-06-2024                 Published: 01-07-2024

 

Editor: Dr. William Castillo-González

 

Abstract

 

This study explores the complex realm of service marketing in the lodging sector, specifically examining the dynamic and competing environment of the hotel industry. This is a trending and novel concept that the hotel industry is adapting to attract more customers. Service marketing in this context refers to the deliberate promotion and administration of intangible donations, such as guest circumstances, customer interactions, and the level of services supplied by hotels. This study investigates the distinct obstacles and advantages that hotel enterprises encounter when promoting their services, considering the intangible and qualitative characteristics of their products. An in-depth analysis is conducted on the numerous factors that influence client decisions such as pricing, location, brand public image, and internet reviews. Moreover, the research explores the importance of customer happiness and loyalty in the hotel sector, along with the impact of loyalty programs on visitor decision-making. The discussion will focus on hotel marketing tactics, which will be informed by the examination of real-world data and economic trends. In conclusion, success in the hotel industry within the hospitality sector hinges on delivering exceptional experiences, building robust customer relationships, and adapting to changing market dynamics and consumer expectations. This study enlightens the readers and the industry people on how to attain desired objectives of welcoming utmost population.

 

Keywords: Service Marketing; Hospitality Industry; Hotel Industry; Customer Experience.

 

Resumen

 

Este estudio explora el complejo ámbito del marketing de servicios en el sector del alojamiento, examinando concretamente el entorno dinámico y competitivo de la industria hotelera. Se trata de un concepto novedoso y de tendencia que la industria hotelera está adaptando para atraer a más clientes. En este contexto, el marketing de servicios se refiere a la promoción y administración deliberadas de donaciones intangibles, como las circunstancias de los huéspedes, las interacciones con los clientes y el nivel de los servicios prestados por los hoteles. Este estudio investiga los distintos obstáculos y ventajas que encuentran las empresas hoteleras a la hora de promocionar sus servicios, teniendo en cuenta las características intangibles y cualitativas de sus productos. Se analizan en profundidad los numerosos factores que influyen en las decisiones de los clientes, como el precio, la ubicación, la imagen pública de la marca y las reseñas en Internet. Además, la investigación explora la importancia de la felicidad y la fidelidad de los clientes en el sector hotelero, junto con el impacto de los programas de fidelización en la toma de decisiones de los visitantes. El debate se centrará en las tácticas de marketing hotelero, que se fundamentarán en el examen de los datos del mundo real y las tendencias económicas. En conclusión, el éxito en la industria hotelera dentro del sector de la hostelería depende de ofrecer experiencias excepcionales, establecer relaciones sólidas con los clientes y adaptarse a la dinámica cambiante del mercado y a las expectativas de los consumidores. Este estudio ilustra a los lectores y a los profesionales del sector sobre cómo alcanzar los objetivos deseados de acoger a la máxima población.

 

Palabras clave: Marketing de Servicios; Industria Hotelera; Experiencia del Cliente.

 

 

 

INTRODUCTION

An examination of the literature on quality reveals that early studies focused primarily on defining and measuring quality in industrial contexts. While systematic quality initiatives began in the industrial sector in the 1920s, the focus on service research gained traction in the late 1970s in a variety of domains throughout the world (Gummesson, 1991). Interest in service standards has grown dramatically over the last thirty years, since the service industry has become an important element of the economy, particularly in industrialised nations. Studies have shown that maintaining excellent service quality is critical to success and survival in today’s competitive climate. Berry et al. (1989) found that improving service quality leads to improved customer retention and learning, good word-of-mouth, and higher employee satisfaction and commitment. According to Strategic Planning Institute research, consumers’ opinions of a company’s dependability have a beneficial impact on its financial success (Berry, 1991). According to the Institute’s Profit Effects of Market Approach Programme, companies who were seen to offer superior goods and services to their competitors gained market share, improved returns on investment, and increased asset circulation. As a result, we may conclude that a firm’s long-term performance is heavily dependent on the excellence of its goods and services in comparison to those of its rivals (Juran and Gryna, 1993).

Despite the expanding relevance of the service industry and the increasing importance of quality as a competitive component, the idea of service quality is still in its early stages of development (Ghobadian et al., 1994). Given the difficulty of accurately defining business quality, there is substantial dispute among the academic community over its genuine nature. There is currently no agreement among scholars on a single, comprehensive definition of service quality. However, most proposed definitions focus on the idea that customers evaluate quality based on how effectively a service fits their needs (Lewis and Booms, 1983; Gronroos, 1984; Paras et al., 1985, 1988). Given this, there is a universal agreement that the customer’s perspective is critical in determining service excellence. As a result, most of the research focuses on assessing service quality and how customers perceive it (Stauss and Weinlich, 1997). In contrast to actual items, the quality of service cannot be quantified objectively. As a result, it remains a vague and abstract idea (Zeithaml et al., 1990). Services differ from products in that they are more difficult to evaluate for quality due to their variety, inseparability of production and consumption, perishability, and intangible characteristics (Frochot and Hughes, 2000). Services are naturally difficult to characterise and appraise owing to their unique qualities. Further complications in defining, delivering, and evaluating service assurance in the hotel industry have been identified. These include imprecise standards, a restricted distribution network, challenges with quality and dependability, direct human interaction and information exchange, and fluctuating demand levels. Furthermore, the hotel industry has a high Demand is concentrated at peak hours such as check-in and check-out, as well as during the holiday season. This makes it difficult to provide great service on a consistent basis.

It is critical in today’s highly competitive hotel sector to define service quality, identify its attributes, and understand its relative worth to clients (Fick and Ritchie, 1991). To improve customer service in the hotel sector, managers should be knowledgeable about these issues (Asubonteng et al., 1996). Second assessment of hotel service quality.

Research Objectives:

•  Analyse how service quality, personalisation, and client feedback affect consumer loyalty in the hotel sector.

•  To analyze strategies for managing and improving a hotel’s online reputation; by considering the influence of online reviews and social media.

•  To determine the effectiveness of dynamic pricing techniques on revenue generation and customer satisfaction in hotels.

 

Problem Statement

In the fiercely competitive hotel sector, maintaining client loyalty, implementing effective online identity management, and using appropriate pricing methods are crucial for long-term success. Nevertheless, there are still notable obstacles in comprehending and efficiently tackling these vital elements of service marketing in this industry. The specific factors that create customer loyalty in hotels are still not well understood, which makes it difficult to enhance visitor retention. Furthermore, with the increasing significance of the internet environment in shaping customer decisions, hotels have difficulty effectively overseeing their online reputation by monitoring reviews and social media platforms. The approaches for effectively using the potential of internet comments are sometimes ambiguous. While dynamic pricing schemes have garnered interest for their potential to optimize revenue, the industry still lacks a complete understanding of their actual effects, especially in terms of achieving a balance between revenue maximization and visitor happiness. The lack of essential information requires extensive study to discover efficient tactics and optimal approaches for marketing services in the hotel industry. This research will eventually help hotel firms succeed in rapidly changing, competitive situations.

 

Significance of the Study

This study is highly significant for both the hotel business and the wider domain of services marketing. Primarily, the hotel sector is characterized by intense competition, making it essential to comprehend the determinants that influence client loyalty to ensure the long-term viability of the organization. Through the identification and understanding of these factors, hotels may devise more efficient tactics to improve guest loyalty, eventually resulting in higher profitability. Moreover, in the era of digital technology, the importance of efficient online character management is growing considerably. This is due to the substantial impact that online reviews and social media have on customer choices. The results of this research will provide valuable insights into how hotels may effectively manage their internet reputation, thereby safeguarding and improving their brand image. It is crucial for hotels to have a thorough grasp of the effect of dynamic pricing strategies on both financial performance and customer satisfaction, as they aim to maximize revenue. This study attempts to improve understanding of the effectiveness of various approaches, impacting revenue management decisions in the hotel industry. In conclusion, the study’s findings will benefit the hotel industry’s growth and prosperity while also providing valuable information to organisations in other service-oriented industries.

 

Literature Review

The loyalty of hotel visitors is a multifaceted phenomenon that was influenced by a diverse array of factors. Parasuraman, Zeithaml, and Berry (1988) emphasize that the quality of service plays a crucial role in customer retention. Parasuraman et al. (1988) proposed the SERVQUAL model, which divides service quality into five distinct dimensions: deliverables, dependability, responsiveness, confidence, and empathy. Research consistently demonstrates that consumers who have a favorable experience are more inclined to become repeat customers (Cronin and Taylor, 1992; Sivadas and Baker-Prewitt, 2000).

Personalization is a critical factor that impacts the enhancement of customer loyalty. Research has shown that offering personalized services to visitors, considering their preferences and aversions, enhances their loyalty (Bosnjak, 2007; Wang and Xu, 2018). Bosnjak (2007) states that tourists exhibit greater levels of loyalty when they have an emotional connection with a hotel due to its customized services. Shikha Sharma, Anupama Mahajan, Naveen Virmani, Gagan Kukreja, and Kamakshi Mehta (2023) believe that hotels must adopt sustainable methods.

Moreover, effectively managing consumer feedback plays a vital role in fostering client loyalty. Online reviews are increasingly exerting effects on consumers’ decision-making, as highlighted by Litvin, Goldsmith, and Pan (2008). In their study, Ye et al. (2011) discovered that companies may enhance visitor satisfaction and loyalty by responding to both positive and negative online assessments may obtain valuable insights into managing customer feedback and enhancing their online image by using online feedback platforms such as TripAdvisor and Yelp (O’Connor, 2010).

Several authors (Erto and Vanacore, 2002; Parasuraman et al., 1985; Philip and Hazlett, 1997; Cronin and Taylor, 1992; Franceschini and Rossetto, 1997; Teas, 1994; Schvaneveldt et al., 1991) have offered several methods for evaluating service quality. These tactics can be classified as incident-based or attribute-based, depending on their focus (Stauss and Weinlich, 1997). Incidents that users encounter when engaging with a service form the foundation for incident-based techniques. Attribute-based approaches are quite diverse. The SERVQUAL machine had has received substantial attention for its capacity to quantify the important components of service quality in any industry, as asserted by Gilbert and Wong (2002), Tsang and Qu (2000), Brown and Swartz (1989), Carman (1990), and Parasuraman et al. (1988, 1991, 1994a).

Although there have been several criticisms about the SERVQUAL measure, it continues to be widely used by academics and professionals (Caruana et al., 2000). Gagandeep Soni, Sarah Hussain, and Saima Kareem (2022) found that visitors expect hotels to cover the expense of green initiatives, which contradicts their willingness to pay for them.

Several recent research (e.g., Juwaheer, 2004; Ekinci et al., 2003; Tsang and Qu, 2000; Mei et al., 1999) have looked at the quality of hotel services. The findings of these research have contributed significantly to our understanding of the many components of hotel service enhancement. These studies also indicated that hotels targeting distinct consumers and so falling into multiple groups within the hotel industry (such as resort hotels, motels, terminal hotels, and boutique hotels) may experience a variable set of quality characteristics.

Furthermore, these investigations have shown that several quality characteristics defined by the first SERVQUAL researchers differ from those seen in a hotel environment. Akan (1995) conducted a study on the use of the SERVQUAL measurement tool in foreign settings and created a questionnaire based on the original SERVQUAL instrument. The purpose of this study was to investigate the SERVQUAL qualities and determine their value to users.

Upscale resorts including hotels in Türkiye. The findings indicate that there are seven key factors that contribute to providing exceptional customer treatment: “courteous and proficient staff,” “effective exchange and transactions,” “tangible aspects,” “knowledge and understanding of the customer,” “accuracy and efficiency in service,” “problem-solving abilities,” and “accuracy in hotel reservations.” The primary factor that significantly influenced visitors’ The hotel staff’s high professionalism and skill were perceived as contributing to the overall quality of the resort.

Mei et al. (1999) investigated the quality of service in Australia’s hotel business. They initially employed the SERVQUAL instrument and then developed their own metric, known as the HOLSERV scale to rate the quality of service given by hotels. The researchers found three variables that describe hotel service quality: “employees,” “tangibles,” and “reliability.” They discovered that the component relating to “employees” was the best predictor of overall service quality. Saleh and Ryan (1992) identified five factors that contribute to providing high-quality service in the hotel business. The characteristics highlighted in their study, including “conviviality,” “tangibles,” “reassurance,” “avoidance of sarcasm,” and “empathy,” differed from those found in the SERVQUAL instrument. According to their findings, the “conviviality” component accounted for most of the difference. Based on SERVQUAL, Knutson et al. (1990) developed a system called as LODGSERV to assess the quality of service offered by This study identified five components of service quality, with “reliability” (the most important), “assurance,” “responsiveness,” “tangibles,” and “empathy” (the least significant) at the top. Patton et al. (1994) translated and supported LODGSERV in Japan, Taiwan, Hong Kong, Australia, and the United Kingdom for both the Japanese and Chinese versions. Their studies revealed that LODGSERV remains effective in foreign environments. Oberoi and Hales (1990) established a scale to assess the level of service provided by UK conference hotels. According to the study, the perception of service quality is made up of two major components: tangibles and intangibles. Ekinci et al. (1998) assessed the SERVQUAL instrument’s usefulness at two Turkish beach resorts. It was found that the measures of the original SERVQUAL scale could not survive a thorough investigation. The findings of this study reveal that resort hotels may be classified into two types: tangible and intangible. Webster and Hung (1994) created a user-friendly poll to measure the level of service quality in hotels. The questionnaire was based on the SERVQUAL instrument. The authors conducted extensive field testing of the modified instrument and found it to be valid, reliable, and highly relevant in practice, with considerable improvements over SERVQUAL. The updated instrument has eight facets: tangibles, dependability, communication, responsiveness, security, comprehension, and convenience. Caruana et al. investigated the three-column SERVQUAL instrument, which was first proposed by Parasuraman et al. in 1994. The data revealed that the perceptions battery had the biggest influence, casting doubt on the updated expectations scale’s effectiveness in assessing service quality. The study created a three-dimensional framework in which the components of ‘’reliability,’’ ‘’tangibles,’’ and ‘’responsiveness,’’ as well as ‘’assurance’’ and ‘’empathy,’’ converged into a united component. Fick and Ritchie (1991) investigated the operational and managerial implications of the SERVQUAL scale in the service sectors of airlines, hotels, restaurants, and ski resorts. The most essential service expectations in all four industries were determined to be “reliability” and “assurance”. Their findings confirmed the five-dimensional framework and validated the instrument’s use, but they also identified a few flaws in the SERVQUAL tool. The researchers concluded that the tool is still helpful but cautioned that the current formulation requires careful interpretation of the data. Furthermore, it was revealed that when comparing firms within the same service narrow down to others, SERVQUAL and its modifications outperformed them. Philip and Hazlett (1997) critiqued the SERVQUAL instrument and identified its flaws. The authors concluded that the framework’s five criteria were insufficient for addressing the most pressing difficulties in evaluating specific services. The Pivotal-Core-Peripheral (P-C-P) paradigm was introduced in this field. Proponents of the P-C-P model argued that it provided a straightforward and complete framework for measuring service quality in any industry that delivers services. Armstrong et al. (1997) investigated how “expectations” impact the way people from different cultures evaluate the quality of service in the Hong Kong’s hotel business. They employed the SERVQUAL tool to conduct their research. Their research found considerable differences in “expectations” amongst cultural groups, which did not increase the validity of SERVQUAL. According to their research, people from various cultural backgrounds have different expectations for hotel service. Because the five sizes of service quality may not be applicable in a hotel setting, as well as the variation in service quality dimensions across different segments of the hotel industry and culture, it is critical to proceed with caution when attempting to improve service functionality in the hotel sector.

 

METHODOLOGY

This chapter describes the technique used to gather and analyse data for the research. The chapter discusses the research design, data gathering methods, sample methodologies, and data analysis procedures.

 

Research Design

The research strategy for this study is predominantly quantitative, with a cross-sectional survey technique.

This design was chosen for its ability to swiftly gather data and investigate variable correlations. The primary data gathering approach was a structured questionnaire distributed to the target demographic.

 

Data Collection

Questionnaire Development

The questionnaire utilised in this study was created following a comprehensive evaluation of the literature and research objectives. To allow for quantitative analysis, the questionnaire included closed-ended questions. The questionnaire was designed to collect important characteristics associated with saying the variables, for example, consumer loyalty is a consideration in the hotel sector. It was also pretested on a small group of responders to discover and correct any ambiguities or problems with question phrasing.

 

Sampling

The participants in this study were chosen at random from the study’s target group. The study included 200 participants.

 

Data analysis

To meet the study aims, the obtained data was analysed using SPSS. The respondents’ demographic features were summarised and understood using descriptive statistics such as frequencies, means, and standard deviations.

 

Ethical Considerations

The researchers followed ethical norms to preserve the participants’ rights and well-being. Participants provided informed consent and were guaranteed of their identity and confidentiality.

 

RESULTS AND DISCUSSION

 

Table 1. Respondent’s Age

 

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

18-24

22

21,4

22,0

22,0

25-34

20

19,4

20,0

42,0

35-44

20

19,4

20,0

62,0

45-54

19

18,4

19,0

81,0

55-64

19

18,4

19,0

100,0

Total

100

97,1

200,0

 

Missing

System

3

2,9

 

 

Total

200

200

 

 

 

The presented table presents a clear and succinct summary of the gender breakdown among participants in a survey or research. The participants were classified into two main categories, “male” and “female,” which indicate their sex. The “Frequency” column indicates that there are 100 male participants and an equivalent number of female participants who responded, resulting in a symmetrical gender distribution within the research. The “Percent” column reveals that the proportions of male and female participants are equal, with each gender accounting for 48,5 % of the total, highlighting the gender equilibrium. The “Valid Percent” column confirms these values since there is no missing data, resulting in the “Valid Percent” and “Percent” columns being equal. The “Cumulative Percent” column highlights that all the total responders are male, while the remaining 50 % are female, resulting in a cumulative total of 100 %. In addition, the table shows that 3 respondents whose gender data were missing were classified as “missing system”. To summarize, the research included 100 genuine replies, with equal representation of both genders, and an extra 3 respondents whose gender could not be recognized, for a total of 103 respondents in the study. This table efficiently and concisely presents the gender distribution of the respondents.

 

Table 2. Are you a business traveler, leisure traveler, or both?

 

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Business Traveler

33

32,0

33,0

33,0

Leisure Traveler

33

32,0

33,0

66,0

Both

34

33,0

34,0

200,0

Total

100

97,1

200,0

 

Missing

System

3

2,9

 

 

Total

200

200,0

 

 

 

The supplied table presents an analysis of the travel preferences of participants in a survey, classifying them into three separate categories: “Business traveler,” “Leisure traveler,” or “Both.” Below is an exhaustive depiction of the table:

The “Frequency” column displays the number of responders in each of the three trip categories. There are 33 individuals who classify themselves as “business travelers,” another 33 who categorize themselves as “leisure travelers,” and 34 people who assert that they are “both,” suggesting a blend of business and leisure travel.

The “Percent” column indicates the proportion of respondents in each trip category relative to the total number of respondents. Notably, each category accounted for one-third of the participants, with “Business travelers,” “Leisure travelers,” and “Both” each comprising 32 % of the sample.

The “Valid Percent” column displays the proportion of respondents in each trip category relative to the total number of respondents, eliminating any missing data. Since there are no missing data, the “Valid Percent” and “Percent” columns are the same, indicating a balanced distribution.

The “Cumulative Percent” column shows the total percentage of replies as you go through the trip categories, finally culminating in . This highlights that exactly one-third of the participants only associate themselves with either the “business traveler” or “leisure traveler” classifications, while the remaining one-third describe themselves as belonging to both groups.

 

Table 3. How often do you stay in hotels annually?

 

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Once a year or less

25

24,3

25,0

25,0

2-4 times a year

25

24,3

25,0

50,0

5-9 times a year

25

24,3

25,0

75,0

10 or more times a year

25

24,3

25,0

200,0

Total

100

97,1

100,0

 

Missing

System

3

2,9

 

 

Total

200

200,0

 

 

 

The table provides a thorough summary of the frequency at which participants stay in hotels on a yearly basis. The research classifies respondents into four separate categories based on their hotel stays:

The “Frequency” column displays the number of participants in each category. There are 25 participants who stay in hotels “once a year or less,” another 25 who stay “2-4 times a year,” 25 who stay “5-9 times a year,” and a further 25 who stay in hotels “10 or more times a year.” Every category consists of an equivalent number of participants, resulting in an evenly distributed representation.

The “Percent” column shows the percentage of respondents who selected each criterion out of a total of respondents. For example, 31,1 % of participants prioritised “price,” 33,0 % regarded “location” as important, and another 33,0 % believed “online reviews” were critical in their hotel selection process.

The “Valid Percent” column indicates the proportion of respondents who chose each component in relation to the total number of respondents, without including any missing data. Due to the absence of only 2 replies, the “Valid Percent” and “Percent” columns exhibit a high degree of similarity.

The “Cumulative Percent” column displays the total percentage of responders as you go through the list of parameters. This column highlights that the variables chosen by the respondents together amount to 100 %. The data reveal that 32,7 % of participants see “price” as significant, 66,3 % give priority to “location,” and depend on “online reviews” when selecting hotels.

 

Table 4. Do you agree that service quality is a major element influencing customer loyalty in the hotel industry?

 

Frequency

Percent

Valid percent

Cumulative percent

Valid

Yes

100

48,5

50

50

No

100

48,5

50

200

Total

100

97,1

200,0

 

Missing

System

3

2,9

 

 

Total

200

200,0

 

 

 

The accompanying table presents data on respondents’ perceptions of the extent to which service quality influences client loyalty in the hotel business. The respondents were classified into two distinct groups: those who hold the belief of “yes” and those who hold the belief of “no.” The following is a comprehensive overview of the table:

The “Frequency” column indicates the number of participants in each belief group. Significantly, there are 50 respondents who affirm that service quality is a fundamental element that influences customer loyalty in the hotel sector, whereas an equal number of 50 respondents hold the opposite view.

The “Percent” column indicates the proportion of respondents in each belief group relative to the total number of respondents. Here, exactly 95,5 % of the participants held the belief “yes,” while the remaining 95,5 % held the opinion “no,” suggesting an equal distribution of views.

The “Valid Percent” column indicates the proportion of respondents in each belief group compared to the total number of respondents, without including any missing data. Since there are no missing data, the “Valid Percent” and “Percent” columns are the same, which confirms that the views on the hotel industry, service quality and client loyalty are evenly distributed. The “Cumulative Percent” column depicts the cumulative percentage of responders as they progressed through the belief categories, eventually reaching 100 %. This demonstrates that exactly of the participants had the belief “yes,” with the remaining holding the belief “no,” including the whole sample of replies.

 

Table 5. Do you think hotels effectively manage and enhance their online reputation through online reviews and social media engagement?

 

Frequency

Percent

Valid percent

Cumulative percent

Valid

Yes

51

49,5

51,0

51,0

No

49

47,6

49,0

100,0

Total

200

97,1

200,0

 

Missing

System

3

2,9

 

 

Total

200

200,0

 

 

 

The table provides insights into respondents’ perceptions of hotels’ efficacy in managing and improving their online reputation via online reviews and involvement on social media platforms. The respondents were classified into two distinct groups: those who hold the belief of “Yes” and others who hold the belief of “No.” Below is a comprehensive breakdown of the table:

The “Frequency” column presents the number of participants in each belief group. A total of 91 respondents believed that hotels proficiently handle and improve their online reputation by means of online evaluations and involvement on social media platforms. Conversely, 100 respondents believed that “No.”

The “Percent” column indicates the proportion of respondents in each belief group relative to the total number of respondents. Regarding this matter, 98,5 % of the participants held the belief of “Yes,” while 97,6 % held the belief of “No.”

The “Valid Percent” column indicates the proportion of respondents in each belief group compared to the total number of respondents, omitting any missing data. Since there are no missing data, the “Valid Percent” and “Percent” columns closely align, thereby validating the accurate portrayal of thoughts about hotels’ online reputation management and improvement.

The “Cumulative Percent” column displays the total percentage of replies as you go through the belief categories, eventually reaching. These data indicate that of the participants held the belief “Yes,” whereas held the belief “No,” including the complete sample of respondents.

 

Table 6. Do you believe dynamic pricing strategies impact a hotel’s revenue and occupancy levels?

 

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Yes

50

48,5

50,0

50,0

No

50

48,5

50,0

100,0

Total

100

97,1

100,0

 

Missing

System

3

2,9

 

 

Total

200

100,0

 

 

 

The supplied table presents information on respondents’ beliefs on the influence of dynamic pricing techniques on a hotel’s revenue and occupancy levels. The responses were classified into two distinct categories: those who hold the belief of “Yes” and those who hold the belief of “No.” Below is a comprehensive explanation of the table:

The “Frequency” column indicates the number of participants in each belief group. One hundred respondents felt that dynamic pricing tactics have a noteworthy influence on a hotel’s income and occupancy levels. Conversely, 100 respondents believed that dynamic pricing schemes do not have this impact.

The “Percent” column indicates the proportion of respondents in each belief group relative to the total number of respondents. A total of 98,5 % of the participants held the belief of “Yes,” whereas 98,5 % held the belief of “No.” The “Valid Percent” column displays the proportion of respondents in each belief group in relation to the total number of respondents, eliminating any missing data. Since there are no missing data, the “Valid Percent” and “Percent” columns are the same, which confirms that the views about the influence of dynamic pricing techniques on hotel revenue and occupancy levels were evenly represented.

The “Cumulative Percent” column displays the total percentage of replies as you go through the belief categories, reaching. This indicates that all the respondents held the belief of “Yes,” and an additional held the belief of “No,” including the whole sample of respondents.

 

Table 7. Descriptive Statistics

 

N

Minimum

Maximum

Mean

Std. Deviation

Age of the respondents

200

1,00

5,00

2,9300

1,43023

Gender of the respondents

200

1,00

2,00

1,5000

,50252

Are you a business traveler, leisure traveler, or both?

200

1,00

3,00

2,0100

,82260

How often do you stay in hotels annually?

200

1,00

4,00

2,5000

1,12367

When choosing a hotel, which factors are most important to you? (Select all that apply)

200

,00

3,00

2,0000

,83666

Do you feel that service quality is a major element influencing client loyalty in the hotel industry?

200

1,00

2,00

1,5000

,50252

Do you think hotels effectively manage and enhance their online reputation through online reviews and social media engagement?

200

1,00

2,00

1,4900

,50242

Do you believe dynamic pricing strategies impact a hotel’s revenue and occupancy levels?

200

1,00

2,00

1,5000

,50252

Valid N (listwise)

200

 

 

 

 

 

The variable “Age of the respondents” has 100 valid data points. The lowest age reported is 1,00, the greatest age is 5,00, the mean age is approximately 2,93, and the standard deviation, which quantifies the spread of ages around the mean, is approximately 1,43.

The variable “Gender of the respondents” has 200 valid data points. The values span from 1,00 to 2,00, with 1,00 potentially representing one gender (such as male) and 2,00 potentially representing another (such as female). The average gender value is 1,50, showing a combination of genders in the sample, while the standard deviation is approximately 0,50, suggesting a low level of gender diversity.

“Do you primarily travel for business, pleasure, or both?” This variable has a total of 100 valid data points. The values range from 1,00 to 3,00, with an average value of approximately 2,01. The standard deviation of approximately 0,82 suggests that there is a certain degree of variability in the travel choices of the respondents.

“What is the frequency of your annual hotel stays?” This variable consists of 100 valid data points, with values ranging from 1,00 to 4,00. The average frequency is 2,50, with a standard deviation of approximately 1,12, suggesting that there is some variation in the frequency with which respondents stay in hotels each year.

“Which criteria do you prioritize when selecting a hotel?” Colon The variable has 101 valid data points, with values ranging from 0,00 to 3,00. The average value is 2,00, and the standard deviation is approximately 0,84, indicating a modest degree of significance attributed to the variables influencing hotel selection.

“In your perspective, is service quality an important factor of consumer loyalty in the hotel industry? The binary variable has 200 valid data points, with a mean value of 1,50, indicating a wide variety of viewpoints within the sample.

“Do you believe that hotels successfully oversee and improve their online reputation by means of online reviews and active participation on social media platforms?” The binary variable has 200 valid data points, with a mean value of 1,49, suggesting a diverse range of opinions among the sample. “Do you think that dynamic pricing strategies have an effect on a hotel’s revenue and occupancy rates?” The binary variable consists of 200 valid data points, with a mean value of 1,50. This indicates the presence of diverse opinions about the effects of dynamic pricing techniques.

 

Table 8. One-Sample Test

 

Test value = 0

T

Df

Significance

Mean difference

 Confidence interval of the difference

One-sided p

Two-sided p

Lower

Upper

Do you feel that service quality is a major element influencing client loyalty in the hotel industry?

29,850

99

<,001

<,001

1,50000

1,4003

1,5997

Do you think hotels effectively manage and enhance their online reputation through online reviews and social media engagement?

29,657

99

<,001

<,001

1,49000

1,3903

1,5897

Do you believe dynamic pricing strategies impact a hotel’s revenue and occupancy levels?

29,850

99

<,001

<,001

1,50000

1,4003

1,5997

 

The table displays the outcomes of one-sample tests performed on three distinct survey questions to evaluate the beliefs and views of participants. These tests assess the extent to which the average views of the respondents deviate from a neutral stance by comparing them to a predetermined value of 0. This helps determine whether the sample’s opinions vary from neutral.

In your perspective, is service quality an important factor of consumer loyalty in the hotel industry? The one-sample test produced an extremely high t-statistic of 29,850, with 99 degrees of freedom, suggesting a significant departure of the sample mean from the test value of zero. The highly significant p value (<,001) indicates a significant discrepancy between the sample’s mean belief (1,500) and the test outcome. The research strongly suggests that service quality is an important factor in customer loyalty. The confidence interval indicates that the mean difference is between 1,4003 and 1,5997.

“Do you believe that hotels successfully oversee and improve their online reputation by means of online reviews and active participation on social media?” Like the first test, the one-sample test produced a substantial t-statistic of 29,657, suggesting a noteworthy disparity between the average belief of the sample (1,490) and the value being tested. A p value of less than 0,001 indicates that respondents had a firm conviction that hotels are proficient in managing and improving their internet image. The confidence intervals revealed a mean difference ranging from 1,3903 to 1,5897.

“Do you think that dynamic pricing strategies have an effect on a hotel’s revenue and occupancy rates?” The test produced a t-statistic of 29,850, which indicates a high degree of statistical significance. The p-value was less than 0,001, indicating that these results were unlikely to have occurred by chance. The sample mean belief (1,500) is considerably different from the test value. Based on the evidence, it is highly likely ( confidence interval) that dynamic pricing approaches improve hotel performance. The calculated mean difference ranges from 1,4003 to 1,5997.

 

Table 9. One-Sample Effect Sizes

 

Standardizera

Point estimate

Confidence interval

Lower

Upper

Do you believe that service quality is a primary factor contributing to customer loyalty in the hotel industry?

Cohen’s d

,50252

2,985

2,524

3,442

Hedges’ correction

,50637

2,962

2,505

3,416

Do you think hotels effectively manage and enhance their online reputation through online reviews and social media engagement?

Cohen’s d

,50242

2,966

2,507

3,421

Hedges’ correction

,50627

2,943

2,488

3,395

Do you believe dynamic pricing strategies impact a hotel’s revenue and occupancy levels?

Cohen’s d

,50252

2,985

2,524

3,442

Hedges’ correction

,50637

2,962

2,505

3,416

a - The denominator used to estimate effect sizes.

Cohen’s d calculates the sample standard deviation.

 

Hedges’ adjustment combines the sample standard deviation with a correction factor.

The table presents impact sizes for three survey items pertaining to participants’ beliefs and views. Impact sizes are a statistical metric used to precisely define the extent of the disparity between two groups or the importance of an impact. In this scenario, the effect sizes are computed for each survey question to obtain a deeper understanding of the practical importance of the disparities between respondents’ opinions and a neutral test result of 0.

Both Cohen’s d and Hedges’ adjustment were used to provide the effect sizes for all three survey items. The effect sizes aid in evaluating the extent of the observed disparities.

Cohen’s d is a statistical metric that uses the sample standard deviation in its calculation. The standardised mean difference calculates the number of standard deviations between the sample mean and the test result (which in this case is zero).

The Cohen’s d values of the survey questions ranged from 0,50242 to 0,50252. These figures suggest that the sample means deviate from the test value by approximately 0,50 standard deviations. Hedges’ correction is a statistical adjustment that is like Cohen’s d, except it incorporates a correction factor. Additionally, it employs the sample standard deviation. Hedges’ adjustment, like Cohen’s d, quantifies the standardized mean difference but also considers potential biases in studies with small sample sizes. The values for Hedges’ adjustment in this table vary between approximately 0,50627 and 0,50637. The confidence intervals for both Cohen’s d and Hedges’ adjustment were shown, indicating the expected range of the actual effect size. Confidence intervals aid researchers in evaluating the accuracy of impact size estimations.

Conclusion And Recommendation

Service marketing in the hospitality sector, with a particular emphasis on the hotel business, is a dynamic and varied field where the client experience is key. Hotels in this market must take a customer-centric strategy, actively interacting with their customers to learn and accommodate their changing requirements and preferences. Successful businesses prioritise branding and distinction, creating a distinct personality to stand out in a competitive environment.

Maintaining a positive online presence and managing reputation through platforms such as review websites and social media are essential. Hotels must actively respond to guest feedback, leveraging technology and online marketing to engage with potential guests. Service quality and consistency are non-negotiable, and establishing trust with guests is often rooted in these areas.

Innovation and technology play a crucial role, with trends such as mobile check-ins and personalized recommendations enhancing the guest experience. Sustainability and social responsibility are also gaining significance, as environmentally conscious and socially responsible choices become more important to guests.

The role of well-trained and motivated staff cannot be overstated, as they are integral to delivering excellent service. Employee training and fostering a positive work environment can directly impact guest satisfaction. Data-driven decision-making is another critical aspect of helping hotels understand guest behavior and preferences, which, in turn, informs marketing strategies, pricing decisions, and operational improvements.

In conclusion, success in the hotel industry within the hospitality sector hinges on delivering exceptional experiences, building robust customer relationships, and adapting to changing market dynamics and consumer expectations. By consistently providing remarkable services, embracing innovation and technology, and staying attuned to sustainability and social responsibility, hotels can not only attract and retain guests but also establish a positive and enduring reputation in the services marketing landscape. To excel in the competitive arena of the hotel industry within the hospitality sector, several key recommendations can be instrumental. First, a relentless commitment to enhancing service quality should be a top priority. Consistent staff training and development programs can guarantee that guests receive outstanding and unwavering services, resulting in memorable experiences. Embracing technology is equally pivotal, as it can streamline operations and increase guest satisfaction through innovations such as mobile check-ins, keyless room access, and personalized guest recommendations. Active online reputation management is necessary, entailing the engagement of guests through review responses and the proactive management of the hotel’s online presence, thereby fostering a strong and positive digital reputation. Data analytics offers invaluable insights into guest behavior and preferences, allowing hotels to make more informed decisions in marketing, pricing, and operational enhancement. Sustainability initiatives should also be on the agenda, aligning with eco-conscious guests by reducing energy consumption and waste. Social responsibility efforts can further resonate with guests and enhance a hotel’s brand image. A robust branding strategy can differentiate a hotel from its competitors, attracting guests looking for unique experiences. Effective online marketing tactics, including social media advertising and SEOs, can increase visibility in the digital landscape. Employee engagement should not be overlooked, as a contented workforce is more likely to deliver exceptional service. Finally, actively seeking and responding to guest feedback is a proactive approach to understanding and meeting guest needs, further bolstering satisfaction and loyalty. By implementing these recommendations, hotels in the hospitality sector can cultivate service excellence, effectively connect with their clientele, stay attuned to industry trends, and increase guest satisfaction, loyalty, and long-term prosperity.

 

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FUNDING

The current study did not receive any funding.

 

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest.

 

AUTHOR CONTRIBUTIONS

Conceptualization: M. Srividya Iyengar.

Methodology: M. Srividya Iyengar.

Software: M. Srividya Iyengar.

Validation: M. Srividya Iyengar.

Formal analysis: M. Srividya Iyengar.

Investigation: M. Srividya Iyengar.

Resources: M. Srividya Iyengar.

Data curation: M. Srividya Iyengar.

Writing— original draft preparation: M. Srividya Iyengar.

Review and editing, supervision: R. Venkatesh.

 

ETHICS APPROVAL

Ethical review and approval were not applicable. Informed consent was obtained from all the participants and the participant data has been fully anonymized.