A Measure of Quality of Life in Elderly Tourists

Applied Research Quality Life DOI 10.1007/s11482-014-9355-x

A Measure of Quality of Life in Elderly Tourists Eunju Woo & Hyelin Kim & Muzaffer Uysal

Received: 31 July 2014 / Accepted: 18 August 2014 # Springer Science+Business Media Dordrecht and The International Society for Quality-of-Life Studies (ISQOLS) 2014

Abstract This research enriches our knowledge of the tourist market in relation to the elderly, i.e., those age 65 and older and retired. The purpose of the research is to explore the missing link between travel behaviors of the elderly and how they contribute to Quality of Life (QoL). As a result, this study clarifies elderly tourist motivation and also aims to examine the relationship among motivation, constraints, leisure-life domain satisfaction, and overall life satisfaction by generating theoretical and practical implications related to those behaviors. Using a structural equation modeling approach, the research model identifies the relevant relationships among the constructs. The results reveal that motivation positively influences satisfaction with leisure-life domain. However, travel constraints do not affect leisure-life domain satisfaction. In addition, satisfaction with leisure-life domain is linked to overall life satisfaction among the elderly. Keywords Elderly tourist . Motivation . Constraints . Leisure-life domain satisfaction . Overall life satisfaction

Introduction With the aging of society, the elderly population has come to be considered an important market segment not only because of its substantial numbers but also for its purchasing power (Lohmann & Danielsson, 2001; Schröder & Widmann, 2007). Continued growth, as projected by the US Census Bureau, indicates that the elderly population will increase to 77.2 million by the year 2040 (Bureau of the Census 2005). By that year, as many as 1 in 5 Americans could be elderly (Bureau of the Census 1995). More importantly, the elderly today are healthier, better educated, and more E. Woo : H. Kim (*) : M. Uysal Department of Hospitality and Tourism Management, Virginia Polytechnic Institute & State University, 342 Wallace Hall (0429), Blacksburg, VA 24061, USA e-mail: [email protected] E. Woo e-mail: [email protected] M. Uysal e-mail: [email protected]

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independent compared to their counterparts in the past (Martin & Preston, 1994). This trend is also observable in several other developing nations (Marmot, 2005). Accordingly, the demand for leisure and tourism activities has grown steadily in our society, and these are coming to be seen as important aspects of life for enhancing psychological and physical well-being (Janke, Davey, & Kleiber 2006) and achieving a successful retirement (Silverstein & Parker, 2002). Thus, a scholarly literature review of tourism and leisure is replete with studies that have examined senior citizens or older adults and their travel behaviors in general. In addition, recent tourism studies have begun to support the view that tourism activities can be a means of pursuing a higher level of Quality of Life (QoL) (e.g., Mactavish et al. 2007; Neal et al. 1999; Uysal et al. 2012). However, there is scant research focusing on the link between travel behaviors of the elderly and how they contribute to their well-being and QoL. This research question should be the focus of empirical investigation in the tourism field. Before introducing the proposed study, the term “elderly” must be clearly defined. The existing literature offers inconsistent reports about the age at which a person starts becoming old; moreover, several different terms have been used to refer to the elderly, such as seniors, older adults, “baby boomers,” and the silent generation. Tourism researchers have generally defined senior travelers as those who are 55 and older, and older adults have also been defined according to the retirement age of 65 or older (Patterson, 2006). However, many studies have used the terms “senior” and “older adult” interchangeably without a clear definition. Furthermore, tourism research has defined age categories inconsistently. The elderly age group has included a range of different ages, from 50 or 55 to 60 or 65 years of age and older, depending on the specific study (Patterson, 2006). However, literature in gerontology has been reasonably consistent in defining the “elderly” according to their usual retirement age of 65 and older. A number of leisure studies have indicated that retiring from work is one of life’s major transitions and has a huge impact on one’s life, including leisure life (Gee & Baillie, 1999; Nimrod, 2008; Nuttman-Shwartz, 2004). Moreover, as leisure time becomes a greater part of a retired person’s life, it may have an increased influence on his or her well-being (Nimrod, 2008). In addition, certain circumstances and historical events can influence the attitudes and behaviors of individuals of the same age cohort. For example, most Americans over the age of 70 experienced the Great Depression and World War II. This historical background influences travelers’ behaviors (Norman et al. 2001). Accordingly, our study defines the elderly as individuals who are 65 years old or older as usually and consistently defined in gerontology studies. . Because engagement in leisure and tourism activities is an important part of later life for many individuals, the purpose of this study is to address the gaps in our understanding of tourist behaviors of the elderly (travel motivation and constraints) with respect to their QoL. This paper proceeds as follows. This paper first discusses the concept of QoL of the elderly. Gerontology and leisure research have acknowledged that QoL for this population is an important subject. However, limited attention has been devoted to the topic in tourism literature. A study by Dann (2002) strongly argued for the necessity to conduct research on senior tourism and QoL and addressed several avenues that need to be explored. Furthermore, as life expectancy in developed countries continues to rise, the management of the lives of the elderly has become a significant issue. Accordingly, new light should be shed on the QoL of the elderly from the perspective of travel behavior.

A Measure of Quality of Life in Elderly Tourists

Second, the study will specifically examine elderly tourist motivation and its relation to QoL. A review of tourism literature shows that motivation is an essential part of understanding tourist behavior; thus, identifying why elderly people want to travel and what motivates them will help us understand the elderly tourist market (Jang & Wu, 2006; Kim et al. 1996). A number of empirical studies have examined the motivations of senior travelers (e.g., Backman et al. 1999; Hsu et al. 2007; Jang & Wu, 2006; Sangpikul, 2008), tourist segmentation based on motivation (e.g., Boksberger & Laesser, 2009; Horneman et al. 2002; Shoemaker, 1989), and relationships between motivation and satisfaction (e.g., Huang & Tsai, 2003). The related literature has also examined motivation as an antecedent to perceived value of tourism experiences (Prebensen et al. 2013). However, motivation has not been explicitly examined as an antecedent of QoL previously in the literature. As known, motivation has been referred to as human needs and wants that arouse an individual’s behavior and activity (e.g., Dann, 1981; Pearce & Caltabiano, 1983; Uysal & Hagan, 1993). Iso-Ahola (1989) stated that motivational behavior influences tourists’ well-being. Therefore, motivation should be considered when attempting to improve QoL (Sirgy et al. 2010; Pearce & Packer, 2013). Third, this study expands upon previous studies in that it attempts to identify elderly tourists’ travel constraints. Understanding travel constraints helps us identify why people do not participate in specific tourism activities (e.g., Chen & Wu, 2008; McGuire et al. 1986). A number of studies have examined travel among seniors or the senior tourist segmentation based on constraints (e.g., Fleischer & Pizam, 2002; Lee & Tideswell, 2005; McGuire et al. 1986). McGuire (1982) categorized travel constraints related to seniors into five dimensions, providing the most significant contribution to the existing literature. These dimensions are external resources, time factors, approval, social factors, and physical well-being. However, limited attention has been devoted to the effects of travel constraints on tourist behaviors. In response to this shortcoming, this study investigates how travel constraints affect QoL of the elderly. In sum, the primary purpose of this study is to focus on the effects of the constructs of tourist behaviors (motivation and constraint) on QoL of the elderly.

Literature Review The Concept of Quality of Life (QoL) Gerontology and leisure researchers have acknowledged Quality of Life (QoL) of the elderly as an important subject. However, measuring QoL without consensus about its measurements has been problematic. A recent paper by Fernandez-Ballesteros (2011) reviewed the conceptualization of QoL of the elderly and identified the diversity of QoL components. He claimed that QoL of the elderly could be examined from two main perspectives by considering the population or the individual level and the objective or subjective nature of QoL assessment. First, the population level measures aging rates, residential facilities, pension systems, and the like, while the individual level of QoL indicators considers education, socioeconomic status, and health status. In terms of the nature of QoL assessment, subjective measures of QoL capture the respondents’ perceptions and evaluations of their lives, while objective data is often collected by government agencies. To date, the objective indicators of elderly QoL have

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been studied widely in many disciplines. Hence, this study will consider the perceptions of the elderly of their QoL at the individual level. Bottom-up spillover theory can be used to explain the concept of QoL (e.g., Diener, 1984; Diener et al. 1999; Sirgy, 2002; Sirgy & Lee, 2006). The fundamental concept of bottom-up spillover theory is that satisfaction with all of life’s subdomains, such as social life, material well-being, leisure life, work life, and the like, influences overall life satisfaction ( Sirgy et al., 2010). Overall life satisfaction can be placed at the final stage of the satisfaction hierarchy. Sirgy et al. (2011) described how specific experiences of travel contribute to positive and negative effects in various life domains, which spill over to overall life satisfaction. This study shows that travel experiences influence satisfaction not only with the leisure-life domain but also with other life domains, such as social engagement, love, culture, family, physical well-being, and others. Growing numbers of studies have used bottom-up spillover theory to explain consumptionrelated experiences in many fields (Sirgy et al., 2010). For instance, Grzeskowiak et al. (2006) concluded that satisfaction with housing leads to satisfaction in social life, leisure life, and financial life, and this satisfaction eventually affects overall life satisfaction. In this vein, QoL reflects two main dimensions, satisfaction with life domains and overall judgment of life satisfaction. Firstly, with respect to satisfaction with life domains, Brown et al. (2004) identified salient life domains of the elderly based on their comprehensive review of the literature in gerontology. These were as follows: health, relationship with others, family relationships, emotional well-being, independence, leisure, mobility, and autonomy. A different study by Kelley-Gillespie (2009) summarized six major life domains among the elderly as follows: social/leisure well-being, psychical well-being, psychological well-being, cognitive well-being, spiritual well-being, and environmental well-being. Among the many different life domains, the study found that leisure life is one of the most-important life domains among the elderly (e.g., London et al. 1977; Silverstein & Parker, 2002; Spiers & Walker, 2009). Hence, this study focuses more on leisure-life domain satisfaction. Secondly, global judgment of life satisfaction is another aspect of QoL captured in terms of overall life satisfaction influenced by lower life domains. It is usually measured using prompts such as the following: “How do you feel about your life (your happiness) overall?” and “How satisfied are you with your life?” The QoL studies in the field of tourism measured overall life satisfaction by using items such as the following: “My satisfaction with life in general increased shortly after the trip”; “I felt good about my life shortly after the trip”; “Overall, I felt happy upon my return from that trip”; and “I felt that I lead a meaningful and fulfilling life” (e.g., Andrews & Withey, 1976; Campbell et al. 1976; Neal et al. 2007; Sirgy et al., 2011). The measure of overall life satisfaction of the elderly has also been developed in gerontology studies. In the early 1960s, Neugarten et al. (1961) developed the Life Satisfaction Rating (LSR). Items were included such as the following: “Compared to other people, my life is better than most of their lives”; and “These are the best years of my life.” Another popular QoL measure, the SWLS (Satisfaction with Life Scale), has been used widely to capture global judgments of the elderly, and previous studies have shown that this measure is reliable. SWLS includes items such as the following: “In most ways my life is close to my ideal”; “The conditions of my life are excellent and I am satisfied with my life”; “So far I have gotten the important things I want in life”; “If I could live my life over, I would change almost nothing.” Thus, this study adopted the SWLS, and


additional QoL items were adopted from tourism studies (e.g., Diener et al. 1985; Pavot & Diener, 1993; Sirgy et al., 2010). Based on the literature review as reflected in the bottom-up spill over theory, the study measured QoL from a multidimensional perspective of leisure-life domain satisfaction and overall life satisfaction. Thus, the following hypothesis is proposed: Leisure-life domain satisfaction affects overall life satisfaction among the elderly.

Travel Motivation and Quality of Life (QoL) A number of studies have suggested that motivation reflects psychological needs and wants that influence an individual’s behavior (e.g., Crompton, 1979; Dann, 1981; Pearce & Caltabiano, 1983; Uysal & Hagan, 1993). Tourist motivations are generally defined as “socio-psychological motives that predispose the individual to travel” (e.g., Yuan & Mcdonald, 1990; Uysal et al. 2008). This definition implies that internal motives and desires to satisfy needs are significant motivational factors. Several studies have examined senior tourist motivation in the tourism field (e.g., Chen & Wu, 2008; Huang & Tsai, 2003; Jang & Wu, 2006). Jang and Wu (2006) found that “knowledgeseeking” and “cleanliness and safety” were important push and pull motivations, respectively. The result of the study by Huang and Tsai (2003) indicated that “relaxation,” “meeting new people,” and “spending time with family” were regarded as major motivations for senior tourists. Sellick (2004) segmented the Australian senior travel market according to travel motivation. The study clustered major travel motives into four segments as follows: discovery and self-enhancement, enthusiastic connectors, reluctant travelers, and nostalgic travelers. The literature reviews of these previous studies show that the most-common elderly tourist motivations are knowledge-seeking, rest and relaxation, social interaction, self-fulfillment, and nostalgia. Considering these motivations in terms of Maslow’s hierarchy of needs theory, elderly tourist motivations should be placed on the higher level of needs (Csikszentmihalyi & Kleiber, 1988; Maslow, 1954). Cordes and Ibrahim (1999) also argued that leisure should be placed above physiological needs and that leisure experience fulfills the needs of elderly people to attain self-satisfaction and personal potential. In social psychology, “motives are inextricably linked to the expected outcomes of behaviors” (Ross & Iso-Ahola, 1991). Motivation in leisure has been applied to activity preference (Virden & Knopf, 1989) and perceived leisure freedom (Deci & Ryan, 1987) in the study of leisure behavior consequences. These studies suggest that individuals’ differences in intrinsic motivation influence a variety of leisure behavior. In tourism studies, Yoon and Uysal (2005) showed that satisfaction could be the outcome of tourists’ pull and push motivations, and push motivation determines destination loyalty. Additionally, Devesa et al. (2010) found that motivation determines the level of satisfaction. Meng et al. (2008) examined the relationship between motivation and tourist satisfaction and showed that motivation to be with family as well as friends affects tourists’ satisfaction with the destination. However, there is limited empirical research that has examined the relationship between tourist motivation and QoL among the elderly. As stated earlier, elderly tourist motivations are considered to be related to growth needs (knowledge-

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seeking, rest and relaxation, social interaction, and self-fulfillment) rather than basic needs. According to the growth needs principle of goal selection in leisure (Sirgy, 2010), leisure life satisfaction can be enhanced by having leisure travel goal associated with growth needs such as self-actualization and social interaction more than basic needs. From this perspective, the study assumes that elderly tourist motivation has a positive association with leisure-life satisfaction. Furthermore, several studies support this relationship in leisure studies. For example, the study by Wang (2008) examined the effect of leisure motivation on overall life satisfaction. The study found that “intellectual motivation,” “social motivation,” “competence mastery,” and “stimulus avoidance” are major leisure motivations among the elderly in Taiwan, and that “intellectual motivation” was a significant predictor of life satisfaction. Additionally, the results showed that intellectual motivation and competence mastery (selfactualization) significantly influence life satisfaction, whereas stimulus avoidance (safety and security needs) was not a strong indicator of life satisfaction, implying that a higher level of human needs predicts a higher level of life satisfaction. A review of previous research suggests the existing relationships between elderly tourist motivation, leisure-life satisfaction, and overall life satisfaction. Therefore, we hypothesize the following: Tourist motivation affects leisure-life satisfaction among the elderly. Tourist motivation affects overall life satisfaction among the elderly.

Travel Constraints and Quality of Life (QoL) Understanding leisure and tourism constraints can help identify why people do not participate in specific tourism activities (e.g., Chen & Wu, 2008; McGuire et al. 1986). Constraints are usually defined as “obstacles, barriers, limitations, impediments, restrictions, and other factors placed in front of individuals either by themselves or by culture, society, or environment” (Edginton et al. 2002, p. 24). The most widely used leisure constraints model was originally developed by Crawford and Godbey (1987) and modified by Crawford et al. (19991). These studies recognized three hierarchical dimensions of constraints: intrapersonal, interpersonal, and structural. First, intrapersonal constraints are regarded as psychological states that affect individuals’ travel preferences, such as lack of interest, stress, depression, or perceived self-skills (Crawford & Godbey, 1987). Second, individuals may perceive interpersonal constraints when their friends, family members, or others do not participate in their activities of interest. These types of constraints tend to change across life stages, depending on other factors such as marital status, family size, and types of activities (Nyaupane & Andereck, 2008). Third, structural constraints are “the intervening factors between leisure preference and participation,” (Crawford & Godbey, 1987) such as family approval, financial consideration, health status, and difficulty accessing information (Walker & Virden, 2005). Along with those travel constraints in general, several studies focused exclusively on travel constraints of the elderly population (e.g., Lee & Tideswell, 2005; McGuire, 1984; Patterson, 2006). These studies suggest that it is important to recognize certain barriers that prevent seniors from travelling. McGuire (1984) categorized travel


constraints of the elderly into five dimensions as follows: external resources (including lack of information, financial difficulty, lack of appropriate travel items); time factors (insufficient time, tourism interrupting normal routine); approval (family and friends do not approve, feeling guilty about going on trips); social factors (spouse dislikes travel, no companion); and physical well-being (no energy, poor health). Furthermore, McGuire et al. (1986), Lee and Tideswell (2005), and Fleischer and Pizam (2002) studied travel constraints among the elderly and reported that lack of social networks, lack of information, physical and emotional costs, low energy, disability, and insufficient money are the most frequently cited barriers to travel. Based on the literature review of travel constraints of the elderly, this paper provides comprehensive insights on travel constraints among the elderly age 65 years and older. A number of leisure studies have found that increased participation in leisure activities would increase life expectancy, improve health conditions, and enhance QoL in old age (e.g., Hendricks & Cutler, 2003; Lu, 2011; Teaff, 1985). Van der Meer (2008) reported that individuals engage in leisure activities to take part in society, which positively influences personal well-being. Recently, much attention has been paid to tourism as a way to improve the level of well-being (e.g., London et al. 1977; Mactavish et al., 2007; Neal et al., 1999; Neal et al., 2007). Neulinger (1982) also argued that a lack of leisure activities decreases one’s QoL. The activity theory, which proposes that maintaining involvement in leisure activity among the elderly is essential for life satisfaction, supports the results of those studies (Havighurst & Albrecht, 1953). Thus, when individuals become less active or inactive because of retirement or other life transitions, it becomes important for them to find replacements for their previous activities or work (Lemon et al. 1972). However, it has been suggested that retired or older individuals with poor health are more likely to undertake passive and internal pursuits, preferring to spend time alone rather than engaging in activities with others (Cumming & Henry, 1961). That is, reducing participation in tourism and leisure activities increase life satisfaction in older adulthood (e.g., Ananian & Janke, 2010). Although disengagement theory appears to support this claim, scholars generally disagree with this assumption. Even though a growing number of scholars have explored the influence of individuals’ travel experiences on overall life satisfaction, many of them have ignored the effects of travel constraints on QoL. Thus, the purpose of this study is to address this gap in our understanding of the association between travel constraints, life domain satisfaction, and overall life satisfaction. Thus, we state the following hypotheses: Tourist constraints affect leisure-life domain satisfaction among the elderly. Tourist constraints affect overall life satisfaction among the elderly.

Methods Measurement of Constructs The main constructs investigated in this study were principally operationalized using scales found in the existing literature. To measure elderly tourists’ push motivation, 21

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push motivation items were developed based on previous literature reviews (Chen & Wu, 2008; Huang & Tsai, 2003; Jang & Wu, 2006; Sangpikul, 2008). These 21 items were categorized into five main motivations, based on previous research, as follows: novelty, entertainment, relaxation, socialization, and internal motivation. After checking the reliability of each motivation dimension, five summated variables were created and used as observed indicators to test the construct of motivation. That is, the construct of motivation was measured using five motivation dimensions (Appendix A). A total of 20 constraints were measured using a five-point Likert-type scale that ranged from “highly disagree” (1) to “highly agree” (5) and was based on scales by Chen and Wu (2008), Lee and Tideswell (2005), and McGuire (1984). Examples of the questions asked of participants included: “I have no information about the place to visit” and “I cannot afford to spend money on travel.” The 20 items were divided into four different categories, based on previous research: external resources, time, approval and social condition, and physical condition. After checking the reliability of each subdimension, four summated variables were created and used as observed indicators to test the construct of the constraints. That is, the construct of the constraints was measured using four observed indicators (Appendix A). Leisure-life satisfaction was measured by assessing three observed indicators: leisure life, leisure time, and spare-time activities (Grzeskowiak et al. 2006; Kelley-Gillespie, 2009). Leisure-life satisfaction was measured on a five-point Likert-type scale, ranging from “very unsatisfied” to “very satisfied.” To measure overall life satisfaction, six items were adopted from the SWLS (Satisfaction with Life Scale) (Diener et al. 1985; Sirgy, 2002). SWLS has been used widely to capture global judgments about the elderly, and previous studies have shown that this measure is reliable. These were: (1) “Overall, I felt happy upon my return from that trip”; (2) “My satisfaction with life in general increased shortly after the trip”; (3) “So far, I have gotten the important things I want in life”; (4) “Although I have my ups and downs, in general, I felt good about my life shortly after the trip”; (5) “Overall, my experience with this trip was memorable, having enriched my quality of life”; and (6) “After the trip, I felt that I led a meaningful and fulfilling life.” Leisure-life satisfaction and overall life satisfaction were measured on a five-point Likert-type scale, ranging from “very unsatisfied” to “very satisfied.” Data Collection A structured questionnaire was used to measure elderly tourists’ travel motivations, constraints, leisure-life satisfaction, and overall life satisfaction. The data collection instrument was based on scales existing in English and translated into Korean. The Korean version of the questionnaire was reviewed for clarity by three native-born Korean professors. A pretest was conducted with a group of volunteers in order to test the validity of scale items. Described in the measurement part, 21 items were developed for push motivation, and 20 items were developed in order to measure travel constraints. In addition, 3 items were used to measure leisure-life satisfaction. Finally, 6 indicators were applied for overall life satisfaction. The pretest was conducted using 50 scale items. The initial survey questionnaire was developed by the researchers, and a group of volunteers participated in the survey. To identify scale dimensionality, an Exploratory


Factor Analysis (EFA) with a principal component method was conducted for each construct. The results of the EFA and reliability coefficients showed that all dimensions presented unidimensionality and a satisfactory score of .7 or higher. Therefore, all items used to measure constructs were considered to be reliable and valid. The data used in this study were collected on Jeju Island, South Korea. This location was selected because its elderly population has experienced rapid growth in recent years. According to a national survey (Ministry of Health and Welfare, 2012), it will take only 18 years for South Korea to transition from being an aging society (in which “7 % of the total population is over age 65”) to becoming an aged society (in which “14 % of the population is over age 65”), while it will take the US 73 years to reach the same figures. The percentage of people on Jeju Island age 65 and older was about 12 % in 2012 (Ministry of Health and Welfare, 2012). Jeju has been designated one of the islands with the greatest longevity, along with Hawaii, Okinawa, and the Hainan Islands. Therefore, the population of Jeju is arguably an attractive market for the tourism industry. The self-administered survey used in this study was distributed by well-trained research assistants over a period of 3 months (from February to April) in 2013. The target sample of this study was persons 65 years of age and older living on Jeju Island, Korea. Around 300 senior Koreans were approached by well-trained assistants in welfare centers, elderly education centers, and elderly associations. The research assistants briefly stressed the importance of answering all questions based on the respondents’ most-recent tourism experience. Incentives such as gift cards were given at the data collection stands. A total of 290 seniors participated in the survey. Of these, the responses of 208 were selected for use in this study. Although the sample was populated conveniently as a result of generating data from select centers and places by intercepting residents of Jeju, some randomness was achieved. This would be consistent with normality of the data distribution, although not guaranteeing “full representativeness”. Furthermore, in order to ensure that the sample was a true representation of the sample population, distributions of some variables (e.g., gender, income source) were also compared to the distributions of the same variables in the Jeju Island census results or factual information. A similar pattern was observed between the two. Although there is little consensus on the recommended sample size for use with the structural equation model (SEM) (Sivo et al. 2006), Garver and Mentzer (1999) and Hoelter (1983) proposed a “critical sample size” of 200. In other words, as a rule of thumb, any sample above 200 is understood to provide sufficient statistical significance for data analysis.

Data Analysis and Results The main objectives of this study were (1) to test a proposed hypothetical model and (2) to understand the interrelationships between four constructs. Structural Equation Modeling (SEM) is a family of statistical models that examines the interrelationships among a number of variables (Hair Jr et al. 2010). SEM explains the structure of interrelationships expressed in a series of equations. Therefore, it is useful when examining the interrelationships among constructs simultaneously (the dependent and independent variables) (Hair Jr et al. 2010). To test the conceptual model and hypotheses of this

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study, SEM was operationalized using the AMOS 18 structural equation analysis package. Two different components of SEM were investigated: the measurement model and the structural equation model. The maximum likelihood (ML) technique, which has been widely used in research of structural equation modeling, was used in this study because it creates estimates that are unbiased, consistent, and efficient. Moreover, the ML method is suggested when the collected data are large enough and data are normally distributed. In this study, the collected data was quite large (n=208), and the normal distribution of the observed variables was met based on the results of skewness and kurtosis. Specifically, missing values, outliers, and normality checking were examined. Frequency distributions for each variable were examined to ensure that the data were “clean.” The results indicated that there were no errors. Next, a measure of central tendency was found for each variable. The mean score and standard deviation, as well as the skewness and kurtosis, of each variable were examined. The results showed that all of the variables in this study were reasonably free from skewness and kurtosis, which is less than the critical ratio of 3.0 (Kline, 2012). That suggests that the data used in the study did not violate normal distribution properties. Measurement Model Confirmatory factor analysis (CFA) was used to test the measurement model. CFA specifies the posited relations of observed variables to underlying constructs. The CFA approach examines whether or not collected data are consistent with a highly constructed hypothesized model or a priori specified model (Hair Jr et al. 2010). Therefore, CFA allows for the identification and clustering of the observed variables in a prespecified, theory-driven hypothesized model, permitting the evaluation of the extent to which a particular collected data set confirms what is theoretically believed to be its underlying constructs. The overall measurement of model fit with the total of 4 major constructs and 18 observed indicators was tested by CFA. The results of the initial CFA estimation were acceptable; χ 2 (124) =189 (p = .000), CFI = .97, GFI = .91, RMSEA = .05, and RMR=.026. Therefore, refinement was not needed. The completely standardized factor loadings were relatively high, ranging from .55 to .94 (Table 1), and all estimates were both reasonable and statistically significant. All of the composite reliabilities were above .80, ranging from .80 to .94. Most of the variance extracted estimates were also above .50, which indicated that the fit indices were satisfactory. Findings of the SEM and Construct Relationships The relationships between the main constructs in the proposed model were tested using a structural equation model (Fig. 1). Review of the theoretical structural model demonstrated that the Chi-square value was 193, with 1125° of freedom (p