September 28, 2021
Community Development in a Tourism Context; A Sociological Perspective
by Luis Meruine & Alejandro Bolaño
Abstract
To assess the psychometric properties of the SPCS-T, a CFA (Confirmatory Factor Analysis) was utilized. Harrington asserts that the CFA can examine the construct validity of a scale and assess its consistency or invariance across different groups, populations, or time. Additionally, she explains that the CFA can evaluate the extent to which a hypothesized construct can be identified in a new sample. One major similarity between EFA (Exploratory Factor Analysis) and CFA is that they both identify the factor structure that explains the variation and covariation among a series of observed variables. However, while EFA establishes a primary model that fits the data, CFA confirms the fit of that data and tests the validity of a pre-determined model.
Introduction
We conducted a CFA using maximum likelihood estimation on the remaining 16 items of the SPCS-T. The software used for analysis was AMOS 19.0, which allowed us to assess the overall model fit of the SPCS-T. Several statistical indicators were generated to evaluate the model’s fit.
The first indicator is the ratio of chi-square (χ2) divided by the degrees of freedom (df), denoted as CMIN/DF. Carmines and McIver (1981, p. 80) suggest that a ratio within the range of 2 to 1 indicates an adequate fit between the hypothetical model and the sample data.
Another indicator is the goodness of fit index (GFI), developed by Jőreskog and Sőbom (1984), which ranges from 0 to 1. A GFI value of 1 indicates a perfect fit of the model to the data.
The Tucker-Lewis index (TLI), representing the non-normalized fit index (NNFI), is used in the context of analysis of moment structures (Hoe, 2008). Similarly, the comparative fit index (CFI) measures the relative noncentrality index (RNI) by comparing the fit of the target model with that of an independence model (Schilling, 2002). Higher values for both TLI and CFI, closer to 1, indicate a better fit.
The root mean square error of approximation (RMSEA) assesses the degree to which the analyzed data approximates the population covariance matrix (Raykov, 2008). Browne and Cudeck (1993) suggest that an RMSEA value of less than 0.05 indicates a close fit of the model in terms of the degrees of freedom.
Table 3 displays the proposed CFA goodness-of-fit indices and their respective ranges for this study.Results of the CFA showed that the hypothetical measurement model fit the data (Table 3). Even though the LC and TPC scales were hypothesized as independent, they were allowed to correlate. The Chi-square value for the overall model was significant, χ2 (103) = 389.79, p<0.001 suggesting good model t. Other indices also showed acceptable mode fit with CMIN/DF=3.784, GFI=0.942, TLI=0.932, and CFI=0.942. Thus, all of the model fit indices met the recommended fit values in Table 3 except for RMSEA.
Modification indices suggested freeing the covariance between two error terms in order to improve model fit (Yoon & Uysal, 2005). The study, therefore, re-conducted the CFA after employing the modification indices; as a result, a subsequent model was found to have better fit than the constrained model, χ2 (84) = 146.49, p<0.001, CMIN/DF=1.744, GFI=0.951, TLI=0.982, CFI=0.987, and RMSEA=0.046. Given the significant improvement in overall model fit, allowing the two error terms to covary was considered the better model.
The composite reliability test examines the internal consistency of the SPCS. The composite reliability of the two scales ranged from 0.955 (LC) and 0.950 (TPC), which indicated a good internal consistency in the model (>0.70, Kline, 2005). Average variance extracted (AVE) was utilized to test discriminant validity that the two subscales, LC and TPC, are pure measures of discrete traits (Throndike & Throndike-Christ, 2010). The AVE of the two constructs exceeded the recommended standard 0.50 (Table 4). Lastly, standardized factor loadings for items ranged from 0.692 to 0.883 (Table 4). The ideal factor loading is greater than 0.70 (Fornell & Larker, 1981), but recent studies suggest that at least 0.50 for standardized factor loadings is generally acceptable (Dimitrov, 2012; Kline, 2005; Woosnam & Norman, 2012).
Since the rise in importance of community-based and sustainable tourism practices, citizen participation and empowerment have become significant areas of study in tourism research. Active and voluntary citizen participation enhances policy control and leadership competence, which are two key ingredients of psychological empowerment. Psychologically empowered individuals can contribute to the development of creative solutions in policy-making processes, including local tourism development. The sociopolitical control scale (SPCS) has been widely employed to investigate the relationship between the intrapersonal component of psychological empowerment (PE) and citizen participation in public policies and programs in the last few decades.
Tourism research has accentuated that individuals who have strong ties to their communities become empowered to take part in policy-making relevant to local-based tourism development (Blackstock, 2005). Tourism development has also been acknowledged as a leading economic force and has the potential to contribute to the rejuvenation of the economy of nations as well as local communities. Yet, mass tourism is often associated with some negative impacts such as destruction of natural, social, and cultural circumstances of host communities of tourism destinations (Chapman & Speake, 2011). Tourism is a system, of which host communities and tourism development are integral parts, but many impacts of tourism development are mainly evaluated with economically-oriented indices. Social impacts, such as the level of citizen participation and empowerment, represent additional important indicators of successful tourism development. Citizen participation and empowerment are critical factors in the minimization of tension between economic growth and protection of tourism resources. The realization of this concept has encouraged tourism scholars to study the issues of host community involvement and citizen participation in tourism. These two subjects are significantly vital to the overall tourism system because knowing how empowered citizens feel, whether or not they see themselves as participants and or are taking actions to be active participants, should be of broad interest to policymakers. Engaging residents in the planning process can improve the quality of development initiatives and support for tourism policy, creating better experiences at tourism destinations.
In this regard, this study examined whether the SPCS can be modified for use in the tourism domain (SPCS-T) as a valid and reliable quantitative scale to measure individuals’ internal status of psychological empowerment toward tourism development. After one item in the tourism policy control (TPC9) was deleted from the full measurement model, CFA results showed evidence of scale reliability. As evidence of construct validity, standardized factor loadings and model fit indices demonstrated that the model fit the recommended requirements for scale construction. The results also provide discriminant validity by showing sufficient variance in the two dimensions of psychological empowerment, LC and TPC (Said, Badru, & Shahid, 2011). To wit, the SPCS is a robust scale that can be readily adapted to various domains, including tourism, and the SPCS-T scale developed in this study holds much promise for researchers and practitioners who are interested in assessing the outcomes of local residents in tourism policies and programs.
The items of the tourism policy control scale (TPC) of this study were reworded to be appropriate to the tourism domain. The rephrased items of the TPC were found to be problematic in terms of citizens’ engagement in local tourism development. One item of the TPC was removed because of its low factor loading, which indicated a possible validity concern. In other words, the study revised the original items of the SPCS for the tourism domain, and was tested in a different sociopolitical setting. It is recommended that future studies more clearly reflect a research area’s sociopolitical circumstances in order to develop the SPCS-T as a standardized construct of the intrapersonal component of psychological empowerment toward tourism development. Next, in order to use the SPCS-T as a more generalized construct in the tourism domain, it is necessary for future studies to test the construct across different communities. Lastly, individuals’ psychological empowerment is an internal state. As such, it is continuously changing. Hence, research needs to assess individuals’ internal change over time. Increasing recognition of longitudinal data has made the importance of the latent growth model (LGM) more appealing and, the LGM will become an effective analytical tool in examining individuals’ social and psychological changes temporally. In turn, this will help the SPCS-T maintain and improve its capability for modeling.
Note: LC refers to leadership competence; and TPC represent tourism policy control. The observed variables were tested based on the measurement by employing the factor loadings of the observed variables and their error term. The error term was omitted in this figure.
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