Importance-Performance Matrix Analysis (IPMA) to Evaluate Servicescape Fitness Consumer by Gender and Age

International Journal of Environmental Research and Public Health Article Importance-Performance Matrix Analysis (IPMA) to Evaluate Servicescape Fitness Consumer by Gender and Age Jerónimo García-Fernández 1, Jesús Fernández-Gavira 1,*, Antonio Jesús Sánchez-Oliver 2, Pablo Gálvez-Ruíz3, Moisés Grimaldi-Puyana 1and Gabriel Cepeda-Carrión4 1Department of Physical Education and Sport, Universidad de Sevilla, 41013 Sevilla, Spain; [email protected] (J.G.-F.); [email protected] (M.G.-P.) 2Departamento de Motricidad Humana y Rendimiento Deportivo, Universidad de Sevilla, 41013 Sevilla, Spain; [email protected] 3Máster Universitario en Gestión Deportiva, Universidad Internacional de Valencia, 46002 Valencia, Spain; [email protected] 4Management and Marketing Department, Universidad de Sevilla, 41018 Sevilla, Spain; [email protected] *Correspondence: [email protected] Received: 20 July 2020; Accepted: 4 September 2020; Published: 9 September 2020   Abstract: The fitness sector has always been linked to the analysis of the loyalty of its consumers. Different studies have shown the importance of sports service and human resources for greater customer loyalty. However, few works have studied how the physical environment or servicescape influences the behavior of consumers in fitness centers based on gender and age. Therefore, the objective of the study was to analyze the relationship between servicescape and the loyalty of fitness center consumers, analyzing through the Importance-Performance Matrix Analysis (IPMA) what the aspects to improve according to gender and age are. The sample was 10,368 fitness center customers (5864 women and 4504 men). After the IPMA, it was concluded that the main improvement margins in general in fitness centers were the equipment and the facility condition, and the facility layout. In turn, in relation to gender and age, the aspects with room for improvement were to a greater extent for equipment and facility condition in women over 21 years of age, and in facility layout for women between 21 and 40 years old and 51–60 years old. Regarding men, the aspects with the highest performance margins were the equipment and facility condition in all the age groups, the facility layout in men up to 50 years old, and the signage in men up to 40 years old and from 51 to 60 years old. Keywords: importance-performance matrix analysis (IPMA); fitness industry; sport consumer; servicescape; gender; age 1. Introduction The fitness sector is a rapidly growing industry [ 1 ]. According to the International Health, Racquet, and Sportsclub Association [ 2 ], there has been a significant growth in the last decade in terms of the number of gyms, fitness clubs, and other fitness centers, as well as the amount of people who join them. Although at a slower rate than in previous years, the fitness market continues to grow and be consolidated in Europe and Spain [ 3 ]. Precisely, the Spanish fitness sector in 2019 increased its membership by 3.3%, with a penetration of this activity of 11.7%. This has meant 2.352 million euros in terms of income, meaning the fifth position in the ranking of European countries in this regard and continuing its upward trend in the fitness sector. Observing the evolution of recent years, it can be said that the Spanish fitness sector is in a state of maturity, but still with room for growth [3]. Int. J. Environ. Res. Public Health 2020,17, 6562; doi:10.3390/ijerph17186562 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020,17, 6562 2 of 19 Despite the growth of the fitness industry, the current situation in the sector makes stakeholders increasingly concerned about the good performance of their organizations in order to determine priorities and/or needs, and to formulate better strategies [ 4 , 5 ]. In this way, the fitness industry immersed in the service sector, has been focused on the value proposition of its offer, which is ultimately intangible. Yet, tangible aspects such as the space where the sports service is produced cannot be hidden and could have a strong effect on the perceptions of consumers themselves [ 6 ]. From this perspective, the servicescape concept emerges, which is understood as “the physical environment that impacts the behavior of customers and employees in service organizations” [ 7 ]. In fact, it is understood that both customers and employees perceive the physical environment through a variety of objective environmental factors and that both groups can respond cognitively, emotionally, and physiologically to this tangible environment [ 6 ]. Precisely, the fitness sector has realized the importance of the physical environment to improve the quality of its service, having hardly analyzed the servicescape in fitness centers [8,9]. Studying this is essential in fitness centers, since their knowledge and their proposals for improvement can have an impact on the increase in the number of users and, above all, on customer loyalty [ 8 ]. Therefore, if the consumer perceives the aspects of servicescape as positive, they will have a greater chance of being a loyal customer [ 10 ]. In turn, gender or age could be presented as determining variables in the perception of the different aspects that make up the servicescape, due to their differences in the perception of sports services [11,12]. In light of these considerations, one of the most useful tools for directors and managers is the importance-performance analysis, which starts from the modeling of partial least squares structural equations (PLS-SEM). This analysis, called importance-performance matrix analysis (IPMA) [ 13 ], is a useful technique to identify the areas of improvement that management activities must address [ 14 ], and which has hardly been used in studies of the fitness sector. Therefore, this analysis would help to understand the influence of servicescape on consumer loyalty and would identify those aspects with room for improvement by gender and age. The findings would help managers to propose improvement actions segmented by gender and age to achieve more loyal consumers. Based on the above and despite the high customer turnover in the fitness industry and its growing global importance [ 4 ], there is a research gap regarding the relationship between servicescape and consumer loyalty in fitness centers. Similarly, although there are studies in the fitness sector that have analyzed the importance and performance of fitness consumer [ 12 ], there is also a breach in the sports management literature analyzing through IPMA the variables servicescape and loyalty according to gender and age. Based on these limitations in the academic literature, the research questions are: Is there a relationship between servicescape and consumer loyalty in fitness centers? Moreover, what are the aspects of servicescape that can be improved in fitness centers from the point of view of the consumers’ gender and age? Based on the comments and answering to the research questions, this study aims to analyze the relationship between servicescape and the consumers’ loyalty of fitness center, analyzing through the IPMA what the aspects to improve according to gender and age are. 2. Theoretical Foundation 2.1. Servicescape and Its Relationship with Consumer Loyalty in Fitness Centers Servicescape is considered the design of service environments made up of environmental dimensions such as space and its function, signs, symbols, instruments, and the people who interact in the service delivery environment. In this sense, the concept understands space as holistic, understood as a whole [ 15 ]. That is, “everything that is physically present around the consumer during the service encounter transaction” [ 16 ]. In fact, servicescape is a vital concept to know and intervene in customer behavior. Hence, it is considered a facilitator that adds or hinders the behavior of both employees and customers when carrying out their activities in the service sector [ 17 , 18 ]. For this reason, service providers must offer environments that enhance consumer experiences and facilitate operational ease and promote overall business efficiency [19]. Int. J. Environ. Res. Public Health 2020,17, 6562 3 of 19 The study of the servicescape has been carried out in different contexts, such as banks [ 20 ], healthcare settings [ 17 , 21 ]; game rooms [ 22 , 23 ], travel [ 24 ] and sports environment [ 25 , 26 ]. Although there are several articles focused on the sports context, few have focused on examining the servicescape in the environment of fitness centers [ 8 , 9 ]. Thus, since fitness centers are highly dependent on their physical environment, it is essential to understand the impact of the physical environment on users and their behavior, obtaining a better understanding of environmental concepts and what factors could influence consumers’ perceptions. If these perceptions had a positive influence on customer behavior, consumer loyalty would be increased. This loyalty is desired by fitness centers which, due to continuous competition, has led them to find ways of differentiating themselves to retain existing members and attract new ones [ 1 ]. A solution could be linked to servicescape. In fact, Chang (2016) examined a service firm and identified a positive relationship between the servicescape and customers’ behavioral intentions [ 27 ]. Others, such as Madzharov, Block and Morrin (2015), also examined how ambient scents affect consumers’ perceptions and feelings and, subsequently, their purchase intention [ 28 ]. Recently, the study of Tran, Dang and Tournois (2020) has revealed the positive relationship between servicescape and consumer loyalty in coffee stores [ 29 ], as also indicated by the studies of Lee, Fu and Tsai (2019) and, Rai and Anirvinna (2019) [ 30 , 31 ]. Regarding studies that have analyzed both variables in the sports sector, Hightower, Brady, and Baker (2002) showed that there was a positive relationship in sporting event customers [ 32 ]. In particular in the fitness sector, only one study is found that has confirmed the positive relationship between servicescape and loyalty [ 8 ], although the study had the limitations of a small sample and a limited number of servicescape dimensions. For all this, taking into account that loyalty is one of the main objectives of the fitness sector [ 33 ], different studies have analyzed what could be the variables that would influence customer loyalty in the fitness sector [ 34 – 36 ]. Nevertheless, although the literature has shown that there is a positive relationship between servicescape and consumer loyalty, there is a gap in the sports management literature that has examined the relationship between servicescape and loyalty. 2.2. Differences in the Perception of Sports Services by Gender and Age Providing information to sports organizations about the relative proportion of users with a certain profile, what their sociodemographic characteristics are, as well as their perception and consumer loyalty, allows improving the design of the service offer and therefore improving consumer satisfaction [11]. Despite the existence of research in the field of fitness centers on consumer perception [ 37 – 39 ], most is focused on demonstrating the relationship between antecedent variables of fidelity, without having examined whether these relationships differ according to gender and age, even though they are extremely important variables [11]. Thus, although there are few contributions in this regard, some studies have shown that women have more positive perceptions and that they in turn have better fidelity indexes [ 40 – 42 ]. Notwithstanding, recent studies, such as that by Le ó n-Quismondo et al. (2020), have indicated that dissatisfaction was greater in women [12]. On the other hand, age is one of the sociodemographic variables that can most condition the behavior or perception of clients in fitness centers. The large number of sports services and types of clients has generated the need to carry out studies that help focus an offer according to the existing demand in the fitness centers [ 43 , 44 ]. Specifically, studies such as that of Yu et al. (2014) point out that age is directly related to the importance given to both the type of physical activity that is practiced in these centers and the aspects that are important for these people during their consumption [ 45 ]. For example, older people have a greater inclination for activities that provide them with healthy benefits with which to face their day-to-day life compared to younger people, whose centers of interest are more varied, challenging health with exercise that will allow them to achieve a more esthetic body [ 46 , 47 ]. For this reason, directing the marketing strategy toward one public or the other, or achieving loyalty to both, are very delicate and important decisions that managers must face in Int. J. Environ. Res. Public Health 2020,17, 6562 4 of 19 their professional performance. In fact, perhaps due to the orientation that each age group has to physical activity, it has been shown that older people give less importance to superfluous aspects of the facilities, compared to other age groups (young and young adults) who do [ 47 ]. Finally, almost all the population groups agree on the importance of having pleasant monitors and the accessibility of the center itself as fundamental factors in fidelity [47,48]. 2.3. Important-Performance Matrix Analysis Strategic management of service perceptions and the physical environment of sports spaces are very important for success in sports organizations [ 49 ]. For this reason, as has been shown, studies analyzing consumer perceptions in the fitness industry are increasingly more frequent [1,12]. One of the tools to assess consumer perceptions and position where managers should direct their efforts is the importance-valuation analysis (IPA), published by Martilla and James (1977) [ 14 ]. In fact, in the academic literature on sport management, there are some studies that have used it, providing what aspects must be improved for a better service [ 12 , 50 , 51 ]. The simplicity of its use and its applicability to explain customer satisfaction and suggest management strategies on how to prioritize resources makes this methodology very attractive for use in sports management [ 52 ]. Despite its simplicity and application, the IPA analysis offers a series of shortcomings that question its validity and usefulness due to the lack of standard criteria, along with conceptual and methodological issues [13,53]. For this reason, looking for an approach from a more global perspective, the importance-performance matrix analysis (IPMA) emerges. This analysis can allow directors and managers to improve their management strategies, since it points out the main factors that require an immediate response [ 54 ]. The IPMA allows for prioritizing constructs to improve a certain target construct, identifying the most important areas for specific actions. The findings provided by the IPMA are important in practical studies that identify the different impacts that certain dimensions of the construct have on the phenomena [ 13 ]. The decision to apply this technique is supported by its usefulness in other sectors (Table 1). Table 1. Importance-Performance Matrix Analysis (IPMA) studies. Authors Sector/Context Principal Findings Related Sample Tailab [55] Bank The study indicates that managers who use the IPMA to prioritize their financial decisions will obtain useful conceptual insights and are unlikely to be misled. 140 banks Ting, Yahya & Tan [56] Education The IPMA shows that the importance and performance of domain knowledge are high in contributing to sustainability entrepreneurship. 121 students Ebrahimi, Hajmohammadi & Tan [57] Tourism The IPMA indicates that place image had the highest importance, but the lowest performance. 135 users Wook, Ismail & Yusop [58] Education The findings revealed through the IPMA that perceived usefulness is the most important antecedent, followed by perceived ease of use, and optimism. 211 students Groß [59] Mobile Thanks to the IPMA, this study provides new insights into the acceptance and profile of m-shoppers. 734 customers Palos-Sánchez, Martin-Velicia & Saura [60] Internet users The IPMA showed the most important attributes for Internet search engine developers. 445 users Carranza, Díaz & Martín-Consuegra [61] Restaurants The IPMA indicate that service quality is one of the three most-valued attributes among those examined in fast-food restaurants studies. 456 customers Reyes-Menéndez, Palos-Sánchez, Saura & Martín-Velicia [62]Restaurants companies The IPMA indicated that Wi-Fi has the highest valuation, although it is the one that obtains the least performance. It is in this construct that improvements in performance must be made. 117 customers Int. J. Environ. Res. Public Health 2020,17, 6562 5 of 19 Table 1. Cont. Authors Sector/Context Principal Findings Related Sample Ramayah, Chiun, Rouibah & May [63] Online banking The IPMA identified that the two most important variables in the use of Internet banking are perceived ease of use and perceived ease of use. 239 customers Rigdon, Ringle, Sarstedt & Gudergan [64] Utilities and hotels The IPMA illustrates differences between three segments of customers and provides the basis for managerial implications. 5398 customers In addition, its application is based on three motivations: IPMA facilitates more rigorous management decision making (1), IPMA is a powerful tool that can help managers set better priorities and better allocate scarce resources (2), and IPMA has guidelines for performance evaluation that serve both a company and the people who invest in it (3) [ 65 ]. In other words, IPMA provides a better understanding of where management should focus its attention. However, although IPMA has been used in different sectors helping with the findings to better management [ 13 ], so far it has not been applied in the fitness sector. Based on the previous literature and responding to the objectives of the study, Figure 1exemplifies the theoretical framework. Int. J. Environ. Res. Public Health 2020, 17, x 5 of 20 Palos-Sánchez, Martin-Velicia & Saura [60] Internet users The IPMA showed the most important attributes for Internet search engine developers. 445 users Carranza, Díaz & Martín-Consuegra [61] Restaurants The IPMA indicate that service quality is one of the three most-valued attributes among those examined in fast-food restaurants studies. 456 customers Reyes-Menéndez, Palos-Sánchez, Saura & Martín-Velicia [62] Restaurants companies The IPMA indicated that Wi-Fi has the highest valuation, although it is the one that obtains the least performance. It is in this construct that improvements in performance must be made. 117 customers Ramayah, Chiun, Rouibah & May [63] Online banking The IPMA identified that the two most important variables in the use of Internet banking are perceived ease of use and perceived ease of use. 239 customers Rigdon, Ringle, Sarstedt & Gudergan [64] Utilities and hotels The IPMA illustrates differences between three segments of customers and provides the basis for managerial implications. 5398 customers In addition, its application is based on three motivations: IPMA facilitates more rigorous management decision making (1), IPMA is a powerful tool that can help managers set better priorities and better allocate scarce resources (2), and IPMA has guidelines for performance evaluation that serve both a company and the people who invest in it (3) [65]. In other words, IPMA provides a better understanding of where management should focus its attention. However, although IPMA has been used in different sectors helping with the findings to better management [13], so far it has not been applied in the fitness sector. Based on the previous literature and responding to the objectives of the study, Figure 1 exemplifies the theoretical framework. Figure 1. Theoretical framework of the study. IMPA: Importance-Performance Matrix Analysis; PLSSEM: Partial Least Squares Structural Equations. 3. Materials and Methods 3.1. Participants Participation was completely voluntary. We collected data for this study by using the convenience sampling method from November 2018 to March 2019. We distributed forms to 56 lowcost Spanish fitness centers. After describing the study’s purpose and significance, the people who agreed to take part completed the survey. The sample was 10,368 fitness center customers. 56.55% of the respondents (n = 5864) were women and 43.44% (n = 4504) were men. In terms of age, 3.74% (n = 388) were less than 20 years old, 26.82% (n = 2781) between 21 to 30 years old, 28.65% (n = 2970) were 31 to 40 years old, 26.52% (n = 2750) were 41 to 50 years old, 11.27% (n = 1168) were 51 to 60 years old, and 3.00% (n = 311) were aged over 60 years. In terms of period of time the respondents had been Figure 1. Theoretical framework of the study. IMPA: Importance-Performance Matrix Analysis; PLS-SEM: Partial Least Squares Structural Equations. 3. Materials and Methods 3.1. Participants Participation was completely voluntary. We collected data for this study by using the convenience sampling method from November 2018 to March 2019. We distributed forms to 56 low-cost Spanish fitness centers. After describing the study’s purpose and significance, the people who agreed to take part completed the survey. The sample was 10,368 fitness center customers. 56.55% of the respondents (n=5864) were women and 43.44% (n=4504) were men. In terms of age, 3.74% (n=388) were less than 20 years old, 26.82% (n=2781) between 21 to 30 years old, 28.65% (n=2970) were 31 to 40 years old, 26.52% (n=2750) were 41 to 50 years old, 11.27% (n=1168) were 51 to 60 years old, and 3.00% ( n=311 ) were aged over 60 years. In terms of period of time the respondents had been enrolled and had exercised at the center, 46.1% (n=4789) of the participants had been enrolled for less than a year, 25.7% (n=2668) had been enrolled for more than a year and less than two years, and 27.2% (n=2911) had been enrolled for more than two years. Table 2shows data from the sample regarding weekly frequency, previous fitness experience, and training prescription. Int. J. Environ. Res. Public Health 2020,17, 6562 6 of 19 Table 2. Sample characteristics. Question Answer Women Men Total n%n%n% Weekly frequency Less than 1 time/week 149 2.54% 100 2.22% 249 2.40% Once/week 242 4.13% 143 3.17% 385 3.71% Twice/week 1369 23.35% 646 14.34% 2015 19.43% Three times/week 2423 41.32% 1813 40.25% 4236 40.86% Four more times/week 1681 28.67% 1802 40.01% 3483 33.59% Total 5864 100.00% 4504 100.00% 10,368 100.00% Previous fitness experience No previous experience 1558 26.57% 899 19.96% 2457 23.70% Yes, in the current fitness center 259 4.42% 187 4.15% 446 4.30% Yes, in another fitness center 4047 69.01% 3418 75.89% 7465 72.00% Total 5864 100.00% 4504 100.00% 10,368 100.00% Training prescription Fitness staff758 12.93% 434 9.64% 1192 11.50% Friend 296 5.05% 203 4.51% 499 4.81% Personal trainer 247 4.21% 220 4.88% 467 4.50% Fitness App 202 3.44% 130 2.89% 332 3.20% Myself 3840 65.48% 3303 73.33% 7143 68.89% Others 521 8.88% 214 4.75% 735 7.09% Total 5864 100.00% 4504 100.00% 10,368 100.00% For this study it has not been experimented with humans or animals, nor has any vulnerable group been used in an inadequate way, all the data obtained was done through anonymous online surveys of 10,368 adults who were asked to fill out a questionnaire confidentially and to be honest with their answers, they were informed that the data obtained would be processed and published for the improvement of the fitness sector and that any personal information that they provided would be treated anonymously. To which they gave their express consent, without making it necessary for these studies to be evaluated by an external ethical committee for approval, since it has not affected in any way the ethical principles for research with humans contained in the Declaration of Helsinki. 3.2. Instruments The instrument used to measure the variables of this study is a structured questionnaire. All the variablesincluded in thestudywere measured withmulti-item scales validatedbyotherresearchers, usinga Likert scale ranging from 1 to 7. 21 items were used for servicescape were based on Kim, et al. (2016) [9] . The future intentions were measured using three items developed by Zeithaml et al. (1996) [66] . This measure has been used in other studies to measure customer loyalty in the fitness industry [ 35 ]. To adapt the study to the Spanish context, the scales of servicescape and future intentions went through a process of translation and synthesis to ensure that there were no errors of interpretation. To that end, the procedures were followed in accordance with Martín-Consuegra et al. (2015) [67] . Based on this, it is possible to compare and examine the equivalence and precision of the translated questionnaire. 3.3. Procedure The chains of low-cost fitness centers with more than five sport facilities were contacted according to data from [ 68 ]. These fitness chains added a total of 213 low cost fitness centers, of which 56 low cost fitness centers decided to participate. The objectives of the study, the instrument, and the practical implications were explained to the managers of the fitness centers. Finally, five fitness center chains chose to participate, with a total of 56 fitness centers. Data collection took place after obtaining permission from the administration of each participating center. The participants gave their consent. Voluntary participation and the confidentiality of their responses was assured. Data collection was carried out through an online questionnaire using Google Forms over a period of five months. The participants took approximately 10 min to complete the questionnaire. 3.4. Data Analysis All our measures were operationalized as composites [ 69 , 70 ]. Therefore, we decided in favor of using PLS-SEM as the best data analysis tool. All the composites were estimated in Mode A because Int. J. Environ. Res. Public Health 2020,17, 6562 7 of 19 the indicators that compound composites are correlated. Our study adopted both an exploratory and a predictive approach following Cepeda-Carri ó n et al. (2019) [ 71 ]. To assess models using IPMA with PLS-SEM an additional procedure implemented by SmartPLS (Version: 3.2.9; SmartPLS GmbH Company, Bönningstedt, Germany) has been established [ 72 ]. Additionally, we test our path model using the classical two step assessment proposed by Hair et al. (2014): (1) the assessment of the measurement model and (2) the assessment of the structural model [ 73 ]. In order to find out the significance of parameters, we used a bootstrap procedure [ 74 ]. Bootstrapping is a resampling procedure that is able to determine the significance of: path coefficients and the weights and loadings of the indicators for each composite (i.e., the latent variables). To detect potential issues of common method bias (CMB) due to the instrument used, a full collinearity test based on variance inflation factors (VIFs) was carried out. According to Kock and Lynn (2012) [ 75 ] when a VIF achieves a value greater than 3.3, there would be an indication of pathological collinearity. This would warn if a model may be contaminated by CMB. The present model, with a maximum VIF of 1.02, may be considered free of CMB. 4. Results According to the assessment of the measurement model, our outcomes show that the measurement model meets all the requirements suggested by Hair et al. (2014) [ 73 ]. First, the individual items are reliable because all the standardized correlation weights exceed 0.7 (Table 3), except one indicator. Yet, as this does not affect the rest of the indices of reliability and validity, we decided to keep it in our analysis. Second, because consistent measures for all the composites are greater than 0.8 (Table 4). These measures (composite reliability, Cronbach’s alpha and Dijkstra-Henseler ´ s rho) are suggested by Hair et al. (2019) [ 76 ] and Henseler et al. (2016) [ 77 ]. Furthermore, the values for average variance extracted (AVE) exceed the threshold of 0.5 (Table 4) for convergent validity. Finally, all the composites exhibit discriminant validity, since all the HTMT are below 0.85 (except one of 0.87, below than threshold of 0.9) [78] (Table 4). Table 3. Indicator loadings. Construct Indicator Loadings Loyalty I would sign up for this fitness center if I unsubscribed (FI1) 0.895 I will make positive comments to a friend about the programs and services of this fitness center (FI2) 0.954 If you ask me, I will recommend this fitness center (FI3) 0.967 Signage The signs used are helpful (SC1) 1 Equipment/Facility condition The equipment used is always in good working condition (SC10) 0.894 Fitness center is well equipped with surrounding facilities (lounge, concession) (SC11) 0.587 The facilities and equipment are safe (SC12) 0.877 Physical facilities are well maintained (SC13) 0.903 The equipment used is of high quality (SC9) 0.879 Ambient Condition The background noise level at fitness center is acceptable (SC14) 0.854 Fitness center is kept clean (SC15) 0.800 Fitness center’s atmosphere is comfortable (SC16) 0.885 The music used in fitness center makes workout environment a more enjoyable place (SC17) 0.812 Facility System Lighting levels are comfortable (SC18) 0.789 Temperature and humidity are comfortable (SC19) 0.913 Air quality is acceptable (SC20) 0.917 Heating, Ventilation and Air-conditioning (HVAC) system are well maintained (SC21) 0.898 Facility Layout Fitness center is expansive and large in scale (SC2) 0.869 Fitness center is designed for all levels of ability (SC3) 0.838 Fitness center have more than enough space for me to be comfortable (SC4) 0.909 Fitness center is designed to minimize my waiting time (SC5) 0.860 Facility Design The buildings’ exterior layout is pleasing (SC6) 0.862 The buildings’ interior layout is pleasing (SC7) 0.944 The buildings in fitness center are decorated in an attractive fashion (SC8) 0.934 FI: Future Intentions; SC: Servicescape. Int. J. Environ. Res. Public Health 2020,17, 6562 8 of 19 Table 4. Reliability and validity of construct measurement. Composites Cronbach’s Alpha (α) Dijkstra- Henseler’s Rho (ρA) Composite Reliability (ρc) Average Variance Extracted Maximum HTMT a Ambient Condition 0.859 0.870 0.904 0.703 0.838 Facility Design 0.901 0.914 0.938 0.836 0.741 Facility Layout 0.892 0.894 0.925 0.756 0.869 Equipment/Facility Condition 0.887 0.912 0.919 0.700 0.778 Signage 1 1 1 1 1 Facility System 0.902 0.902 0.932 0.776 0.881 Loyalty 0.933 0.938 0.957 0.882 0.839 aHeteroTrait-MonoTrait ratio of correlations for discriminant validity. As to the assessment of the structural model, Hair et al. (2014) comment that the use of bootstrapping (5000 resamples) produces standard errors and confidence intervals to assess the statistical significance of the path coefficients [ 73 ]. All the path coefficients in Table 5and Figure 2were significant. Our model provided R-squares of 0.55 for loyalty. Table 5. Construct effects on endogenous variables (incl. lower and upper limits of 95% confidence interval). Relationships Original Sample (O) Average of the Sample (M) 5.0% 95.0% Ambient Condition ->Loyalty 0.258 0.258 0.233 0.282 Facility Design ->Loyalty 0.036 0.036 0.010 0.061 Facility Layout ->Loyalty 0.198 0.198 0.176 0.220 Equipment/Facility Condition ->Loyalty 0.233 0.233 0.208 0.257 Signage ->Loyalty 0.096 0.096 0.076 0.115 Facility System ->Loyalty 0.032 0.032 0.012 0.053 Int. J. Environ. Res. Public Health 2020, 17, x 9 of 20 Figure 2. Relationship between constructs. Apart from the PLS-SEM results of a structural model, IPMA helps PLS-SEM results through a four-quadrant diagram as depicted in Figure 3. The vertical axis represents the performance of the attributes from poor performance to good performance. The horizontal axis represents the perceived importance of the attributes from not very important to very important. Following Martilla and James (1977) [14] and Hair et al. (2019) [76], for the importance-map creation, four quadrants are illustrated as Q1 (Management is fine), Q2 (Something important that needs to be improved), Q3 (Too much performance for a non-important issue), and Q4 (it does not matter and no performance). These quadrants are delimited using the mean of performance and mean of importance reported in the table of the IPMA results. Figure 3. IPMA full data set. Note: Red: ambient condition; Strong blue: facility design; Green: facility layout; Yellow: equipment/facility condition; Pink: signage; Light blue: facility system. Facility systemSignage Facility layout Facility design Equipment/Facility condition Ambient condition Loyalty 0.032 0.098 0.198 0.036 0.233 0.268 R 2 : 0.553 Figure 2. Relationship between constructs. Apart from the PLS-SEM results of a structural model, IPMA helps PLS-SEM results through a four-quadrant diagram as depicted in Figure 3. The vertical axis represents the performance of the attributes from poor performance to good performance. The horizontal axis represents the perceived importance of the attributes from not very important to very important. Following Martilla and James Int. J. Environ. Res. Public Health 2020,17, 6562 9 of 19 (1977) [14] and Hair et al. (2019) [76], for the importance-map creation, four quadrants are illustrated as Q1 (Management is fine), Q2 (Something important that needs to be improved), Q3 (Too much performance for a non-important issue), and Q4 (it does not matter and no performance). These quadrants are delimited using the mean of performance and mean of importance reported in the table of the IPMA results. Int. J. Environ. Res. Public Health 2020, 17, x 9 of 20 Figure 2. Relationship between constructs. Apart from the PLS-SEM results of a structural model, IPMA helps PLS-SEM results through a four-quadrant diagram as depicted in Figure 3. The vertical axis represents the performance of the attributes from poor performance to good performance. The horizontal axis represents the perceived importance of the attributes from not very important to very important. Following Martilla and James (1977) [14] and Hair et al. (2019) [76], for the importance-map creation, four quadrants are illustrated as Q1 (Management is fine), Q2 (Something important that needs to be improved), Q3 (Too much performance for a non-important issue), and Q4 (it does not matter and no performance). These quadrants are delimited using the mean of performance and mean of importance reported in the table of the IPMA results. Figure 3. IPMA full data set. Note: Red: ambient condition; Strong blue: facility design; Green: facility layout; Yellow: equipment/facility condition; Pink: signage; Light blue: facility system. Facility systemSignage Facility layout Facility design Equipment/Facility condition Ambient condition Loyalty 0.032 0.098 0.198 0.036 0.233 0.268 R 2 : 0.553 Figure 3. IPMA full data set. Note: Red: ambient condition; Strong blue: facility design; Green: facility layout; Yellow: equipment/facility condition; Pink: signage; Light blue: facility system. The results are demonstrated in Table 6, while Figure 3shows the ‘importance-performance map’ of each exogenous latent variable along with its influence on the endogenous latent variable (i.e., loyalty). Table 6. IPMA results full data set. Latent Variable Loyalty Total Effect (Importance) Index Value (Performance) Ambient Condition 0.333 76.082 Facility Design 0.045 75.254 Facility Layout 0.228 71.483 Equipment/Facility Condition 0.283 71.073 Signage 0.108 74.762 Facility System 0.039 74.108 Mean 0.173 73.794 Additionally, we decided to distinguish between gender and age, responding to the papers’ call for this special issue. 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