The Relationships between Service Quality, Reputation, and Performance in Hospitality

Abstract

The importance of service quality and a positive reputation, individually leading to enhanced performance of hospitality organizations, is undisputed. However, little is known about their interplay and how they collectively explain the variability in performance. The purpose of this article is to contribute to the understanding of the individual and collective impact of service quality and reputation on variability in performance, categorized as customer satisfaction and loyalty. This study was conducted using quantitative research methods with data derived from hotel guests in Iceland in the summer of 2023. We gathered 1487 valid answers from guests staying at any of the 9 selected hotels, all belonging to the same Icelandic chain. The factor analysis revealed a credible four-factor model that consists of tangible service, performed service, reputation, and performance. All three independent factors had a statistically positive connection with variabilities in performance, and the collective explanation ratio for the model was strong. The independent factors were correlated to a satisfactory level, and tangible service had the strongest unique connection to performance.

1. Introduction

Hospitality, similar to other service industries, revolves around service and service quality to surpass competition in a fiercely competitive industry. Service quality is commonly defined as the difference between customer expectations and the perceived performance of a service instance [1]. Measuring service quality entails receiving feedback from customers through various instruments, such as questionnaires and online platforms [2,3]. Increased service quality is believed to be a key influencing factor for enhanced performance [4,5,6]. Likewise, in an era of online reviews and social media, creating and maintaining a positive reputation is crucial for organizational performance. Reputation has been categorized in different ways in different industries [7]. In hospitality, reputation is usually based on customers’ perceptions of a company or service provider and is, therefore, closely related to image [8]. In this industry, reputation is measured primarily by recommendations through electronic word of mouth (E-WOM) using various online and app platforms [9]. Performance also encompasses different aspects within hospitality research, ranging from financial metrics and productivity to marketing capabilities, customer satisfaction, and/or customer loyalty [10]. Regardless of how performance is defined, research seemingly agrees that service quality and reputation have a positive effect on performance variability [4,5,6]. Moreover, research reveals much about the individual connections between service quality and reputation, on the one hand, and performance on the other. Furthermore, the interplay of service quality and reputation has been extensively researched, with service quality believed to have a positive effect on reputation [11,12], and vice versa [13,14]. However, since service quality and reputation are commonly measured separately, a gap in research remains regarding how service quality and reputation collectively explain variabilities in performance.

The purpose of this article is to contribute to narrowing this research gap, with the aim to explain the individual and collective impact that service quality and reputation have on variabilities in performance, categorized as customer satisfaction and customer loyalty. Understanding this relationship and impact will contribute to the prioritization of attributes that underpin service quality, reputation, and performance. This is crucial both theoretically, to create more targeted research, and practically, for managers to make improvements that matter to guests and ultimately to the company’s performance. We, therefore, ask the following questions:

• Can a credible model be generated that explains variations in performance based on service quality and reputation?
• To what extent can variations in performance be explained by such a model, and can it be argued that one factor has more weight than another?

To answer these questions, we introduce a hypothetical model called QuReP (Quality–Reputation–Performance), which aims to measure service quality, reputation, and performance simultaneously to understand the interplay and collective interactions among them. The model allows for investigation of the interaction, enabling prioritization of service and/or reputation toward better performance. A similar method has been tested in the hospitality industry with reliable results [15]. The results presented in this research are derived from data gathered from guests staying at any of the nine selected hotels, belonging to the same hotel chain in Iceland. Guests were asked to evaluate their service experiences during their stay, and their perception of the hotel’s reputation. Furthermore, guests were asked to grade their overall satisfaction with their accommodation, likelihood of reuse, and recommendation to others. The article proceeds as follows: First, we provide a theoretical overview of service quality, reputation, and performance and the interplay among them. Second, we explain the methods and research model. Finally, we present the findings, discussion, and conclusions.

2. Literature Review

This section presents the concepts underpinning the QuReP model, namely, service and service quality, reputation, and performance. Special attention is paid to their connections and the common methods used in gathering data in this regard.

2.1. Service Quality

Service has many different definitions, although it is commonly understood as a process involving interactions and cocreation between those who deliver service and those who receive service. Service differs from tangible products, as service is usually consumed at the same time as it is produced; thus, service performance must be assessed as close to consumption as possible [16,17]. Considering the characteristics of service—intangibility, inseparability, perishability, and variability—measuring service performance can seem an impossible task for managers [18]. Additionally, customers’ perceptions of service can differ greatly within and across markets in terms of what is expectable, outstanding, or even important [1,5,17]. Therefore, when service performance is measured, it is generally referred to as quality of service rather than performance in certain service factors, as it depends on the occasion, importance, and taste of the consumer [19,20]. Different people perceive the quality of service differently, but service quality is commonly understood as how well a service meets customer expectations and requirements, considering the importance of the service instance in the customer’s mind [21]. Hence, if organizations aim to increase their level of service quality, the first step is to ensure sufficient understanding of customers’ expectations and the derivation thereof [17,22,23]. Customer expectations originate from numerous sources, some of which a company can influence, such as promises made and promises kept, whereas other factors are beyond the reach of the organization, such as the importance of the service instance according to the consumer, the customer’s experience, or even their mood [16,23].

Measuring service quality thus entails obtaining feedback from customers regarding their experience during or immediately after service encounters and matching that performance with customer expectations. The main purpose of measuring service quality is to enable prioritization of improvement initiatives within a company and, therefore, a focus on the attributes that matter most to the customer and/or the organizational strategy [20,24]. Different methods can be used to gather data from customers regarding service valuation, such as interviews, focus groups, and questionnaires, depending on the context and aim of the research [25]. Questionnaires are the most-used methods, as they are relatively inexpensive and easily comparable over time and can reach many customers simultaneously [10].

One of the best-known instruments for measuring service quality is SERVQUAL, designed and validated in the early 1990s [24,26]. The instrument assesses service quality as the difference between customer expectations and perception, considering the importance of the service instance to the customers. SERVQUAL is based on five dimensions of service quality: reliability, assurance, tangibles, empathy, and responsiveness [17]. Reliability refers to the ability to perform a service dependably and accurately. A reliable service is delivered as promised, on time, and meticulously [26,27]. Reliability is crucial because it helps build trust between the service provider and the customer and is often considered the most important dimension [28]. Assurance relates to employees’ knowledge, courtesy, competence, and ability to inspire trust and confidence. It also includes factors such as the safety of the environment. Tangibles encompass everything that can be seen, touched, or felt about the service, from the physical facilities to the tools and equipment used to provide the service and the appearance of personnel. Empathy involves the care and individualized attention the firm provides to its customers. This dimension assesses whether the service is delivered such that the customer’s personal needs and circumstances are considered, showing that the company cares about its clients as individuals. Responsiveness concerns the willingness to help customers and provide prompt service. It includes responding quickly to customer requests, resolving issues, and keeping customers informed about service delays or changes [17,26,27,29]. As previously mentioned, the complexity of service delivery often makes it difficult for customers to value or choose one service offering over another. Although reliability is generally deemed to be the most important dimension when selecting a service offering, customers often value service through the tangible dimension when other dimensions are considered equal. In other words, tangibles become important if service is, for example, complicated and, therefore, difficult to value, if the service is good enough but does not excel prior experience with competitors, or even if the customers are indifferent with the service instance [30,31].

When measuring service quality using instruments, such as SERVQUAL, modification is not uncommon for a better fit to various industries or research aims [17]. Tools, such as SERVINN, LODGEQUAL, HOTLSERV, and LODGESERV, are amended for better use in hospitality research, especially in the accommodation sector [32,33]. These tools are SERVQUAL-based, utilizing the methodology and/or quality dimensions, although they differ if all the dimensions are used. For instance, LODGEQUAL suggests that a guest only values interactions between people and tangible experiences, while LODGESERV introduces new dimensions of security, access, and communication [34].

Increased service quality has been shown to be positively related to organizational performance, regardless of the definition of performance, for example, internal, such as employee job satisfaction, motivation, and loyalty toward the organization [35,36], financial metrics, such as return on investment [37], or (as categorized in this study) customer satisfaction and loyalty [1,5]. Therefore, service quality is a critical aspect of any service- and customer-oriented organization that strives to positively influence its performance, when it comes to customer satisfaction and loyalty, and surpass the competition [30,38,39]. Furthermore, increased service quality has proven to have a positive impact on organizational reputation and image, resulting in more positive recommendations through E-WOM that builds expectations for future guests [40,41,42,43,44].

2.2. Reputation

Although the importance of a positive reputation is undisputed (e.g., [44,45,46]), scholars have not agreed on a single definition of the phenomenon or how it is generally represented and interpreted in research. It has been argued that two theory schools regarding reputation exist, with fundamental differences in definition. On the one hand, reputation is considered to be the same as or an extension of image and is simply a way to measure image. On the other hand, reputation and image are distinct but interrelated [47]. Moreover, Chun (2005) [7] highlighted that reputation can be divided into three main components: (1) what the organizational unit is, or existence (identity), (2) what stakeholders believe the organizational unit stands for (image), and (3) what the organizational unit claims to stand for, or planned existence (desired identity).

Reputation as a construct has been studied across different disciplines, such as marketing, auditing, economics, and sociology, from different perspectives, such as buying behavior and strategy, and in different types of organizations [48]. In marketing- and service-related theories, reputation is generally considered to be a perceptual representation of a company’s past actions and prospects that describes the firm’s overall appeal to all its key constituents when compared with other leading rivals. A common denominator for reputation is that it concerns the perception of the stakeholders of the organization in question and is similar to what is often defined as the brand image [8]. This perception can be based on people’s experience dealing with organizations or media coverage of them [49]. In other words, reputation is the valuation of customers’ perceptions or beliefs of an organization’s overall performance based on their experience. A positive reputation can be seen as an intangible organizational asset that can, if well managed, differentiate a company from the competition on the market. In the hospitality sector, managing reputation is believed to be the most important task for managers [50], and claims have been made that reputation even has a stronger connection to performance than service quality [15].

Measurements of reputation in the hospitality sector are generally derived from recommendations or E-WOM, through third-party websites or apps, such as TripAdvisor, Booking, and others. Customers can easily share their opinions (even anonymously) regarding their experience, which can affect future customers’ decisions [9]. Such third-party platforms are prolific and popular, and they can be problematic for managers and researchers to address. First, because of the lack of control or ownership of the data gathered; second, because there is no way of comparing the grades for each attribute to performance indicators (e.g., customer satisfaction and loyalty) [2,51]; third, according to Chun (2005) [7], this only measures one aspect of reputation, which refers to image. Furthermore, assessing reputation through grading specific attributes only describes the experience for that attribute, and such attribute valuations could also represent the valuation of service experience. It is, therefore, recommended to assess reputation in a more abstract manner, through customers beliefs on how the organization is, how it has been, or how it will be in the future, based on their experience [52]. Reputational factors regarding sustainability and, moreover, related to environmental and social factors have also been used within the industry [45,51]. These factors are believed to be increasingly important for guests when choosing between destinations or firms. Guests are more likely to use services or travel to destinations that align with their values and that they can identify with [53].

Research indicates that reputation, alongside many personal and situational factors, such as experience with the same or a similar company, alternatives, and promises made, plays a significant role in building up customers’ expectations of service provision and will, therefore, affect the perceived service quality [1,17,23]. Furthermore, reputation has a positive relationship with performance, regardless of how performance is categorized [6,51,54].

2.3. Performance

Organizational performance is often described as the ability to reach goals and optimize results. Performance is, therefore, measured in many ways, depending on those goals, the market situation, the sector, and/or the organizational strategy [10]. Performance has been categorized as being hard vs. soft, internal vs. external, and customer defined vs. company defined [4,17,21,35]. Hard metrics encompass attributes that can be measured with little or no deviation, such as waiting time and financial records [4,21]. Soft metrics are derived from people’s perceptions or opinions, for example, through question surveys or interviews [55]. Internal performance indicators are defined as internal data gathering to achieve set goals for internal use [16,56]. By contrast, external metrics are gathered from market audits or customer opinions to shed light on the situation as it is or as it could be [16,57]. Company- and customer-defined standards concern how goals are set and if they reflect the customer’s expectations or needs or only the company’s needs and ability to perform [58,59].

It has been claimed that performance metrics, for example, revenue per available room (REVPAR), average daily rate (ADR), and occupancy rate (OCR), have been predominant within the hospitality sector [4], especially in the practical sense. These metrics represent hard (financial), internal (data from internal records), and company-defined standards, but guests are oblivious to the meaning and importance of those metrics. Hence, without undermining the importance of such metrics, service-related theory states that organizations should predominantly focus on customer-defined performance metrics based on customers’ expectations and experiences—that is, soft and external metrics. If an organization works successfully with these, then other metrics will also prove to be successful in the long run [16,17,60,61,62].

Customer satisfaction as a performance indicator is often used in hospitality research, especially when assessing service quality [32,54]. Although service quality is believed to be the single most important factor in customer satisfaction, it is also underpinned by other factors, namely, product or tangible quality, price, and perceived value for money, alongside personal traits and situational factors [63]. Likewise, customer loyalty is also often used as a measurement of performance and usually as a consequence of customer satisfaction [64,65]. Hayes (2008; 2013) [61,62] suggested that the most effective means of measuring performance is through a combination of customer satisfaction and loyalty. In this study, we follow Hayes’ recommendation and categorize performance as customer satisfaction and customer loyalty.

Notably, in the statistical evaluation of performance, it has been suggested that when dealing with service, customer satisfaction, and loyalty performance, the score must be well above average to succeed [62,66]. Hayes (2013) [62] asserted that if a five-point interval scale is employed to assess service, only individuals scoring above 4.12 are likely to portray loyalty toward the service by reusing or recommending it to others. He also stated that scores below 3.64 indicate dissatisfaction, and scores between 3.64 and 4.12 indicate indifference by customers, i.e., service is perhaps sufficient but not exceptional [61,62]. Similarly, Reichheld (2003) [66] referred to the net promoter score (NPS) on an 11-point interval scale (ranging from 0 to 10): only individuals who score 9 or 10 are truly promoters of the firm or the services rendered.

2.4. Connecting Service Quality, Reputation, and Performance

Regardless of how performance is defined (e.g., financial metrics or customer satisfaction and loyalty), both reputation and service quality are believed to play key roles in optimizing performance and driving better results (e.g., [4,5,51,62,67]). As mentioned before, the individual effects that service quality and reputation have on performance have been extensively researched. Therefore, the list of the following studies is by no means exhaustive but provides recent examples of research within the hospitality sector on the matter. Mohammad et al. (2022) [45] explored the relationship between employee performance and service quality, using reputation as a moderator, and found that reputation had a positive effect on service quality. Mmutle and Shonhe (2017) [12] found a connection between the valuation of service quality from guests and the reputation reflected on E-WOM platforms. Similarly, Redditt et al. (2022) [42] found commonalities between service attributes and E-WOM, mediated by satisfaction. According to Shah et al. (2018) [24], service quality had a positive effect on performance through customer satisfaction, and González-Rodríguez et al. (2021) [68] observed a positive effect of reputation on performance. Furthermore, Wilson (2020) [69] found that both reputation and service quality had a significant positive impact on customer loyalty, both directly and through trust. However, little research exists regarding the interplay of service quality and reputation toward variabilities in performance, even though service-related theories identify reputation as a core element of service quality, as it influences expectations [13,70,71].

The proposed hypothetical model was derived from Asgeirsson and Gudlaugsson (2024) [15], where the interplay between service quality and reputation in relation to variability in performance is measured simultaneously (see Figure 1).

As can be seen from the figure, the proposed three-factor model looks at the interplay between the two components, service quality and reputation, and their connection to the variability in performance. In Asgeirsson and Gudlaugsson’s (2024) [15] findings, both independent components were significant when predicting the variability in organizational performance, categorized as customer satisfaction and loyalty. According to their findings, reputation had a greater unique contribution than service quality in explaining the variability in performance, but because of the interplay between the two independent components, they had a greater explanatory power together than individually.

3. Materials and Methods

3.1. Research Design

The instrument we used for this study was based on a questionnaire that featured 3 sections, each of which represented one component in the hypothesis model: service quality, reputation, and performance, for a total of 25 questions. Of those, 19 questions measured service quality, 3 measured reputation, and 4 questions measured performance. The questions related to service quality were derived and combined from three sources. First, we used validated SERQUAL-based instruments from various scholarly articles concerning service quality in hotels and other forms of accommodation. We split our service questions into three sections: general hotel service and appearance, service rendered from individual staff members and their attitude, and general room quality and comfort. Questions regarding the hotel in general were derived from Ali et al. (2021) [54] and Wilkins et al. (2007) [29]. For questions relating to employee service performance and room quality and comfort, we followed Getty and Getty (2003) [33] and Ali et al. (2021) [54]. Second, we sourced attributes from online review sites, such as Booking and TripAdvisor. Third, we looked at attributes that the hotel chain in question utilized internally regarding guests’ valuation of service and added attributes of interest to the hotel management to the list. We compared the wording and meaning of previously defined questions to what was used by the hotel and amended them according to their vocabulary. Using an expansive foundation to create the questionnaire allows for opportunities to connect findings to different sources that may provide more meaningful comparisons in the future. Reputation-related questions were methodologically based on ideas and studies by González-Rodríguez et al. (2021) [68] and Hannington (2016) [52], where participants shared their opinions and beliefs on various aspects of reputation. The three areas underpinning reputation were: overall reputation, reputation in social responsibility, and reputation in environmental responsibility, which were drawn from resent research regarding the importance of the social and environmental responsibility of hospitality organizations [45,51,53]. The four questions used to portray performance: overall satisfaction, willing to recommend, would you choose again, and would you have chosen the same, followed the work of Hayes (2008; 2013) [61,62] on customer loyalty and satisfaction. All items regarding service quality and reputation were measured on a 5-point interval scale (1 = strongly disagree to 5 = strongly agree). Performance variables were scored on an 11-point scale (0 = strongly disagree to 10 = strongly agree).

3.2. Sample

The population of interest was comprised of guests who stayed at any of the nine selected hotels from June to September 2023. The hotels belong to the same hotel chain, are all based downtown in Reykjavík, Iceland, and all offer a similar structure of service, rooms, and service areas. All the hotels have 24 h front desk availability, breakfast buffet and all-day restaurants, room service, and suites, and all score four stars or more on review sites, such as Booking. See

Table 1. Breakdown of valid answers between months.

Month	Valid	Percent	Cumulative
June	314	21.1%	21.1%
July	377	25.4%	46.5%
August	398	26.8%	73.2%
September	398	26.8%	100%
Total	1487	100%

for a breakdown of valid answers between months.

Guests were contacted by email following their check-out with one iteration, using the hotel database. A total of 6505 emails were sent out. Of those, 2529 recipients opened the survey link, and 2114 began answering but dropped out, leaving 1497 responses. During data analysis, 10 respondents were deemed outlaws; thus, 1487 valid responses remained and formed the basis for the findings in this research.

Most of the respondents (70%) were visiting Iceland for the first time, but about 13% had visited Iceland once or twice before. Most traveled for leisure (86%), and they stayed at several different hotels within the chain for an average of three to four nights. The majority of participants came from the US (49.9%), followed by Canada (11.2%), Great Britain (10.2%), and other Nordic countries (7.4%). Other participants were primarily from Europe, followed by Australia, Japan, Brazil, and other countries. Of the participants who chose to answer the gender question, 35.31% were male, 50.37% were female, and 0.27% defined gender in a different way. The age of the respondents was quite high, with about 33% being older than 66 years and about 70% being 46 years old or older. According to the data available from the hotel chain in question, the demographics described here are good representatives of their guest portfolio.

3.3. Data Analysis

The survey data were downloaded from QuestionPro and SPSS data analyzers, and Excel was used for data analysis. To check whether the hypothesis model was supported, we conducted a principal component analysis (PCA) in SPSS. Prior to the PCA, we assessed the suitability of the data for factor analysis. We then performed a correlation matrix examination to determine the interrelationship of the independent components, and finally, linear regression to assess both the individual and collective influence of the independent components on variability in the dependent component.

4. Findings

This section presents the key findings from the research, offering a detailed analysis of the data collected during the study. The results are organized according to the research questions and objectives outlined in the introductory section.

4.1. Building the Model

As previously stated, following the guidelines of Asgeirsson and Gudlaugsson (2024) [15], we created a hypothesized model containing three factors: quality, reputation, and performance. Inspection of the correlation matrix revealed the presence of many coefficient values of 0.3 or higher. The Kaiser–Meyer–Olkin value exceeded the recommended value of 0.6 [72,73], and the Bartlett (1954) [74] test of sphericity reached statistical significance, supporting the factorability of the correlation matrix. The PCA, using Oblimin rotation, revealed the presence of four factors, as opposed to three in the hypothesized model, with eigenvalues exceeding 1. Those factors built on the hypothesized model, where reputation and performance were the same, but the quality factor split into two: tangible quality (TANGSQ) and performance quality (PERFSQ). Resulting from the PCA, one question, regarding the quality of breakfast, did not load on any of the four factors and was removed from the question set. The alpha values for each factor, along with means and standard deviations for each question, are presented in

Table 2. Components’ alpha values, means, and standard deviations for each variable.

	(TANGSQ) Tangible Service Quality, Cronbach’s Alpha = 0.91
Number	Text	Mean	Std
1	This hotel has up to date equipment	4.25	0.959
2	This hotel’s physical facilities are visually appealing	4.16	0.971
3	This hotel has hygienic bathrooms and toilets.	4.59	0.790
4	This hotel has timely housekeeping service.	4.62	0.771
6	This hotel on-line presence and information was clear and up to date	4.44	0.873
16	My room was comfortable, relaxing, and welcoming.	4.22	1.002
17	My bed was comfortable and clean (mattress, pillow, sheets and covers)	4.64	0.717
18	My room offered a variety of basic products (soap, shampoo, towels, toilet paper…)	4.59	0.764
19	My room equipment was in working order (lighting, toilet, kettle, fridge, TV)	4.54	0.851
	(PERFSQ) Performed Service Quality, Cronbach’s Alpha = 0.95
5	This hotel provides timely and accurate check-in and check-out procedures.	4.68	0.737
8	The hotel employees are well dressed and appear neat.	4.76	0.577
9	The hotel employees perform service accurately upon arrival.	4.74	0.665
10	The hotel employees perform service at the promised time.	4.74	0.653
11	The hotel employees appear to be well trained and knowledgeable.	4.70	0.692
12	The hotel employees have good communication skills	4.74	0.638
13	The hotel employees are helpful, friendly, and courteous.	4.78	0.615
14	The hotel employees give special attention to guests.	4.55	0.788
15	The hotel employees deliver excellent service to guests.	4.63	0.715
	Reputation, Cronbach’s Alpha = 0.91
25	I believe that this hotel has a positive overall reputation	4.57	0.717
26	I believe that this hotel has a good reputation in terms of social responsibility.	4.48	0.743
27	I believe that this hotel has a good reputation in terms of environmental responsibility.	4.48	0.737
	Performance, Cronbach’s Alpha = 0.98
22 *	How likely are you to recommend this hotel?	8.45	2.072
23 *	How likely would you be to select the same hotel, If you were starting your journey now?	8.37	2.304
24 *	How likely would you be to select this hotel again, if you were travelling to Iceland	8.15	2.605
28 *	Overall satisfaction with your stay at this hotel	8.01	2.711

* Questions on 11-point scale (0–10).

.

As can be seen in Table 2, the questions loaded on TANGSQ related to the managerial implications of creating comfort, functionality, cleanliness, and visual appeal. Questions concerning PERFSQ referred to the service delivered by hotel employees and their attitude and willingness to serve. Reputation was measured by three questions, asking participants to state their perceptions of social, environmental, and overall reputation. The value of performance was measured by four questions related to guests’ satisfaction and loyalty. The alpha value for each component was very good [75], exceeding 0.9 in all instances, thus confirming that the questions measured the same construct. The scores were overall high, which might suggest that guests were happy with their accommodation and service. The mean for each question ranged from 4.16 to 4.78 where the 5-point interval scale was used, and from 8.01 to 8.45 where the 11-point interval scale was used. Questions 1–4 and 16–19 were loaded on TANGSQ, questions 5 and 8–15 on PERFSQ, questions 25–27 on reputation, and questions 22–24 and 28 on performance. Figure 2 presents the adjusted proposed QuReP model, listing the questions underpinning each factor.

As can be seen in Figure 2, the general idea of the hypothesized model was supported, i.e., that it is built up by service quality, reputation, and performance. However, in the proposed model using this dataset, there were three independent components, rather than two, since the quality factor split into two parts. The resulting model suggested that all three independent components had an impact on variability in performance. Furthermore, it was assumed that the independent components were interrelated.

4.2. Testing the Relationships between Components

To build the QuReP model, a correlation matrix was produced between the three independent components (i.e., TANGSQ, PERFSQ, and reputation) to determine whether, statistically, all components were independent. Then, a linear regression was conducted with the three independent components and performance as the dependent component. This was carried out to ascertain if and how the independent components contributed to variabilities in performance, both individually and collectively. Correlations between factors are listed in

Table 3. Correlation matrix for structure components.

Components	n	Mean	Std	CA (α)	1	2	3	4
TANGSQ (1)	923	4.50	0.62	0.91	1
PERFSQ (2)	1207	4.75	0.50	0.95	0.63	1
Reputation (3)	566	4.60	0.63	0.92	0.68	0.56	1
Performance (4)	1327	8.30	2.30	0.98	0.67	0.52	0.62	1

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The table shows that the total number of instances used in this analysis varied between factors, because when the tests were run, participants who failed to answer questions related to each factor were not considered. The correlations between factors (TANGSQ, PERFSQ, reputation, and performance) were strong in all instances (r > 0.52), and none exceeded 0.7, which is acceptable [76]. This is important to know since it is one of the foundations for being able to use multiple regression, as demonstrated in the next section.

4.3. Building QuReP

To build up the QuReP model, we employed multiple regression to test and validate the model, using performance as the dependent component and TANGSQ, PERFSQ, and reputation as the independent components. Such a method is sensitive to correlation and possible outliers within the data. As demonstrated before, the correlation between components was at a satisfactory level. Furthermore, the Mahalanobis distance (MAH) was sufficient (lower than 16.27), as was Cook’s distance, which was lower than 1 [77]. Findings from the multiple regression are displayed in

Table 4. Findings from the regression analysis.

Components	Std Beta (β)	t	Sig	Part	Tolerance	VIF
TANGSQ	0.41	8.0	<0.001	0.36	0.451	2.215
PERFSQ	0.11	2.4	=0.018	0.12	0.576	1.735
Reputation	0.29	6.1	<0.001	0.28	0.510	1.962

As seen in the table, the standardized beta (β), ranging from 0.11 to 0.41, suggested that all independent components had a connection to variability in the dependent component. According to Pallant (2020), the greater this number, the more weight the component has in explaining the variability. Tolerance exceeded 0.1, as suggested, and the variance inflation factor (VIF), which is the inverse of the tolerance value (1 divided by tolerance), was lower than 10. All three factors exhibited a significant relationship with performance (sig = 0.001–0.018). The part value squared (P²) shows how the component individually explained the variance in the dependent component [76].

Figure 3 illustrates the QuReP model. This model explained (R²) 51% of the variability in performance, which means that 49% of the variance in performance remained unexplained by the model and was outside the scope of this research. Pallant (2020) [76] argued that the explanation ratio (R-square) in peer-reviewed academic papers is commonly lower than 45%. Kline (2005) [78], however, argued that in the physical sciences, the value is often higher than 50%. With this considered, the model has certain strengths.

Of the three independent factors, TANGSQ had the strongest connection (β = 0.41) and the strongest unique contribution (13%, P² = 0.13) to explaining variabilities in performance. This was followed by reputation (β = 0.29), which individually explained approximately 8% (P² = 0.078) of the variance, and finally PERFSQ (β = 0.11), which explained 1% (P² = 0.014). Individual contributions of the independent components totaled 22%, which is far less than the explanatory power of the model. This is because, as previously mentioned, all the components were correlated to a degree; therefore, they were stronger together than individually. Moreover, because they were all statistically connected to the variability in performance, it is safe to assume that all the independent components are important when it comes to explaining variances in performance.

5. Discussion

The purpose of this article was to contribute to narrowing a research gap that adhered to the individual and collective impact of service quality and reputation on variabilities in performance, categorized as customer satisfaction and loyalty. We proposed two research questions to address this objective, the former referring to the building of a model that comprised service quality, reputation, and performance, and the latter that described the relationship and interactions between the components of the model.

To answer the first research question, “Can a credible model be generated that explains variations in performance based on service quality and reputation?”, we performed a factor analysis on the question set. The findings revealed three independent factors, as opposed to two in the hypothesized model derived from Asgeirsson and Gudlaugsson (2024) [15]. The service factor was split into two factors, namely, TANGSQ and PERFSQ, each supported by nine questions from the dataset. Interestingly, the set initially comprised 19 service questions, but one question regarding the quality of breakfast had no statistical connection to either the service factors or other factors of the model. The questions underpinning TANGSQ related to the tangible assets of service: what customers could see or feel, the atmosphere, and surroundings [26,27,29]. Revealing TANGSQ as a separate factor suggested that participants in the survey considered other things to be equal or not decisive factors. In other words, tangibles become important if service is, for example, complicated and, therefore, difficult to value, if the service is good enough but does not excel prior experience with competitors, or even if the customers are indifferent with the service instance [30,31]. The PERFSQ factor encompassed the managerial implications of standards, employee performance, and customer orientation. These relate to the sequential dimensions of empathy, responsiveness, and reliability, of which reliability is generally referred to as the most important dimension of service quality [17,28]. Despite these data, we still cannot fully understand whether customer orientation refers only to employees or if a company’s standards are customer-oriented and support or guide employee behavior, thus highlighting the need for further investigation [38].

The reputation factor involved three questions reflecting participants’ views and beliefs on company benchmarking in terms of overall reputation and in terms of environmental and social focus [52]. These findings are in line with recent claims that the environmental and social focus of a hospitality firm is gaining in importance for guests when valuing performance. Guests increasingly align between their own and a company’s identity and values when assessing or recommending services [45,53]. We measured performance using four questions: three that measured loyalty and one that measured customer satisfaction, in line with Hayes (2008; 2013) [61,62].

The alpha values exceeded 0.9 for all four factors, which confirmed that the questions measured the same construct [75]. The means for all questions on the 5-point scale were quite high (4.16–4.76), and all exceeded Hayes’s (2013) [62] criteria (>4.12) for assumed loyalty through reuse and recommendation intentions. Although the scores on the 11-point scale were also quite high (8.01–8.45), they did not fulfil Reichheld’s (2003) [66] criteria (>9) for recommendations. Perhaps alarmingly, the lowest score in the performance component was overall satisfaction (8.01). This might suggest that, while service quality (both tangible and performance) is highly valued by guests, other aspects, such as value for money, situational factors, location, or personal traits, influenced participants’ scoring [63].

The second question asked, “To what extent can variations in performance be explained by such a model, and can it be argued that one factor has more weight than another?” To answer this question, we assessed the correlation between the components and then performed linear regression using the independent components against the dependent component of performance. The correlation assessment revealed that quality and reputation attributes were correlated (r = 0.52–0.68), with 0.0 meaning no correlation at all and 1.0 meaning a perfect correlation or the same factor. At the risk of those factors being statistically identical, this correlation should ideally not exceed 0.7, unless a substantial argument exists for that exceedance, but should exceed 0.3 to be considered part of the model [76]. Among the independent components, TANGSQ showed the strongest correlation (PERFSQ = 0.63 and reputation = 0.68), while PERFSQ and reputation were also correlated (0.56), all in line with the guidelines of Pallant (2020) [76]. These strong correlations are in line with the previous statement that quality and reputation are linked [17,23,42], but through these data, the degree to which these factors are interconnected can be statistically understood.

Linear regression found the three independent components to be statistically significant (Sig. = 0.001–0.018) in explaining variances in performance. TANGSQ had the strongest standalone connection to performance, explaining 13% (P² = 0.13) of the variance, compared with PERFSQ’s 1% (P² = 0.014) and reputation’s 8% (P² = 0.078). This finding is in line with the notion that service is often assessed based on tangible quality when other things seem equal, difficult to value, or customers are indifferent with the performance [30,31]. The role of reputation also played a significant role in explaining variabilities in performance, which is in line with the importance of the factor for hospitality organizations [50]. Although reputation had a notably stronger unique contribution than PERFSQ, the findings contradicted those of Asgeirsson and Gudlaugsson (2024), where they found that reputation had the strongest unique contribution to the variances in performance.

The interplay of TANGSQ, PERFSQ, and reputation yielded a model summary that explained 51% (R² = 0.51) of the variance in performance, which is considerably higher than the factors explained by the combined individual score (P² = 0.06 + P² = 0.10; P² = 0.16). This means that because of their interplay, reputation and service quality together explained far more than they did individually, which underlines the importance of considering both aspects simultaneously [15]. The model thus has certain strengths, especially considering Pallant’s (2020) [76] argument that the explanation ratio (R-square) in academic papers in the social sciences is often lower than 45%. However, Kline (2005) [78] argued that in the physical sciences, the value is often higher than 50%. This model strength, although exceeding both those ratios, was assessed based on the attitudes of individuals and should, therefore, follow the lower explanation ratio, as per Pallant (2020) [76]. Other factors (49%) also contributed to guests’ satisfaction and loyalty, but they were out of scope when measuring specific attributes. Some of these include loyalty programs, participants’ mood and circumstances while answering, participants’ recollection of the service instance, and their understanding of the questions [17].

6. Conclusions

To conclude, based on our findings, we claim that a statistically credible model, called QuReP, could be generated. The model was useful to explain the relationships between service quality, reputation, and performance when categorized as customer satisfaction and loyalty. Furthermore, the model displayed the interactions between the factors of service quality and reputation, and thus how they both individually and collectively contributed to the variability in customer satisfaction and loyalty. Therefore, the results contributed to the understanding of the interaction between service quality and reputation, and thus the purpose of the article was achieved.

The model derived from our findings somewhat contradicted previous findings set forth in the hypothetical model. First, through factor analysis, our findings revealed three independent factors of tangible service, performed service, and reputation, as opposed to two in the hypothesized model. Second, our findings from regression analysis showed that tangible service had the strongest unique connection to variability in performance, whereas reputation was deemed to have the strongest unique connection in the study related to the hypothesized model. The contradictions in the findings using the same questionnaire and hypothesized model are intriguing and suggest that further work is needed within this topic.

The fact that tangible service was produced as an individual factor is interesting, and even more so that the factor had the strongest unique connection to performance, as categorized in this study. This could be a consequence of several things. First, guests could have deemed other service instances to be on par, or not a decisive factor, to what was expected based on their experience dealing with similar service providers. In other words, the rendered service was not outstanding, or not enough to deliver a competitive advantage between similar rivals. Second, guests could have found the service produced to be complicated and, therefore, hard to evaluate. Third, guests could be indifferent to the service rendered, considering the delivery as sufficient norms. Finally, these findings might as well indicate that the tangibles in the hotels were outstanding and/or tangibles were the decisive factor for their satisfaction and loyalty. Importantly, although tangibles were deemed the most important of the three, because of the factors’ covariance, the model strength exceeded the explanatory power of individual factors. This suggests that all three factors were, through their interplay, important when it came to customer satisfaction and loyalty among hotel guests in Iceland. Therefore, all factors, namely, tangible service, performed service, and reputation, should receive adequate attention from managers.

6.1. Practical Implications

Research such as ours is imperative for managers to be able to prioritize and, therefore, focus resources on what matters for guest satisfaction and loyalty. Our findings suggested that managers should focus on managing a tangible service and reputation. At the same time, they should be aware of the interplay of service quality and reputation in relation to performance and how these three elements interact.

6.2. Theoretical Implications

Our findings contributed to an improved understanding of the interplay of service quality and reputation and their collective and individual connections to variabilities in performance. Nevertheless, the findings here somewhat contradicted previous findings by Asgeirsson and Gudlaugsson (2024) [15], who applied similar methods in the hospitality industry. Thus, there is still work to be carried out in this regard.

6.3. Research Limitation

This study has some limitations. First, only partial information about the population could be obtained from the hotel database; therefore, knowledge about the exact degree to which our sample represented the population is limited. However, from the data available, and according to managers of the hotels, the demographics seemed to be a good fit to guests’ demographics. Second, through factor analysis, many responses were deleted, as participants did not complete all questionnaire items. Third, the mean value of all items was very high. This may underlie a prestige effect when answering the survey that may bias the result of the work.

6.4. Further Research

This research emphasized the importance of measuring quality and reputation simultaneously, since their interplay strongly contributes to variability in performance. We, therefore, suggest continuing to use the QuReP model through various aspects of hospitality and tourism to gain an understanding of this interplay across different sectors of the industry. Furthermore, using the model in other countries and/or other seasons in the accommodation sector would also contribute to further knowledge and validation of the construct. It is also imperative to introduce CFA and SEM/PLS to the data, which would enrich these findings and help both researchers and managers prioritize attributes related to service quality and reputation. Segmentation on the data, using demographics of the respondents, should also be conducted to determine if the results differ between groups. Finally, research is needed to discover what actions within the organization will push for better results regarding QuReP. Since we employed only external data (derived from guests), we cannot fully understand whether customer orientation refers only to employees (PERFSQ) or if a company’s standards are customer-oriented and support or guide employee behavior.