Dataset Information

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

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.

SUBMITTER: Garcia-Fernandez J

PROVIDER: S-EPMC7557594 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

García-Fernández Jerónimo J Fernández-Gavira Jesús J Sánchez-Oliver Antonio Jesús AJ Gálvez-Ruíz Pablo P Grimaldi-Puyana Moisés M Cepeda-Carrión Gabriel G

International journal of environmental research and public health 20200909 18

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, ana ...[more]

PMID: 32916936

Similar Datasets

Project description:Introduction: Physical fitness is an adaptive state that varies with an individual's growth and maturity status. Considering that the difference in skeletal maturity already existed among preschool children, this study was designed to determine the influence of skeletal age and chronological age on preschoolers' physical fitness performance. Methods: This cross-sectional study was conducted in 945 healthy preschoolers (509 males, 436 females) aged between 3.0 and 6.0 years in Shanghai, China. We used the method of TW3-C RUS to determine skeletal age. Chronological age was measured by subtracting the date of birth from the test date. Sit and reach, 2 × 10 m shuttle run test, standing long jump, tennis ball throw, 5 m jump on both feet, and balance beam walk were considered for physical fitness performance. Correlation coefficients and partial correlations adjusting height and weight were used to determine the relationships among the variables of skeletal age/ relative skeletal age, chronological age/relative chronological age, and physical fitness items. Results: Skill-related physical fitness was weakly to moderately associated with skeletal age (the absolute value of r: 0.225-0.508, p < 0.01) and was moderately to strongly associated with chronological age (the absolute value of r: 0.405-0.659, p < 0.01). Health-related physical fitness items (BMI and sit and reach) showed a fairly weak to no correlation with skeletal age and chronological age. After adjusting the height and weight, an extremely weak to no correlation was observed between skeletal age and both health- and skill-related physical fitness, and weak-moderate correlations were noted between chronological age and skill-related physical fitness (the absolute value of r: 0.220-0.419, p < 0.01). In children in Grade 1, skill-related physical fitness (except for balance beam walk) showed a weak to moderate correlation with relative chronological age (the absolute value of r: 0.227-0.464, p < 0.05). Conclusion: (1) both skeletal age and chronological age are associated with skill-related rather than health-related physical fitness performance, and after adjusting height and weight, chronological age, rather than skeletal age, is associated with skill-related physical fitness performance; (2) for preschool children, skill-related physical fitness performance is influenced by relative chronological age rather than individual differences in skeletal maturation, especially in the lower grades.

Project description:BackgroundA great deal of consumer data, collected actively through consumer reporting or passively through sensors, is shared among apps. Developers increasingly allow their programs to communicate with other apps, sensors, and Web-based services, which are promoted as features to potential users. However, health apps also routinely pose risks related to information leaks, information manipulation, and loss of information. There has been less investigation into the kinds of user data that developers are likely to collect, and who might have access to it.ObjectiveWe sought to describe how consumer data generated from mobile health apps might be distributed and reused. We also aimed to outline risks to individual privacy and security presented by this potential for aggregating and combining user data across apps.MethodsWe purposively sampled prominent health and fitness apps available in the United States, Canada, and Australia Google Play and iTunes app stores in November 2015. Two independent coders extracted data from app promotional materials on app and developer characteristics, and the developer-reported collection and sharing of user data. We conducted a descriptive analysis of app, developer, and user data collection characteristics. Using structural equivalence analysis, we conducted a network analysis of sampled apps' self-reported sharing of user-generated data.ResultsWe included 297 unique apps published by 231 individual developers, which requested 58 different permissions (mean 7.95, SD 6.57). We grouped apps into 222 app families on the basis of shared ownership. Analysis of self-reported data sharing revealed a network of 359 app family nodes, with one connected central component of 210 app families (58.5%). Most (143/222, 64.4%) of the sampled app families did not report sharing any data and were therefore isolated from each other and from the core network. Fifteen app families assumed more central network positions as gatekeepers on the shortest paths that data would have to travel between other app families.ConclusionsThis cross-sectional analysis highlights the possibilities for user data collection and potential paths that data is able to travel among a sample of prominent health and fitness apps. While individual apps may not collect personally identifiable information, app families and the partners with which they share data may be able to aggregate consumer data, thus achieving a much more comprehensive picture of the individual consumer. The organizations behind the centrally connected app families represent diverse industries, including apparel manufacturers and social media platforms that are not traditionally involved in health or fitness. This analysis highlights the potential for anticipated and voluntary but also possibly unanticipated and involuntary sharing of user data, validating privacy and security concerns in mobile health.

Project description:BackgroundNew fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects.ObjectiveThe objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research.MethodsWe searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands.ResultsWe identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands.ConclusionsThe wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets