Tadayuki Miyamoto, Curtin University of Technology, Western Australia Kunihiko Iwasaki, University of Shizuoka, Japan. Abstract

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1 Development Of A Standard Convention City Attractiveness Scale: In Search Of Generalisability In Construct Validity Through Simultaneous EFA And Multi-Group CFA With Five Sample Data Sets Tadayuki Miyamoto, Curtin University of Technology, Western Australia Kunihiko Iwasaki, University of Shizuoka, Japan Abstract This paper addresses marketing scale development and proposes a standard convention host city attractiveness (CHCA) scale for the study of convention tourism marketing. More specifically, it develops a standard CHCA scale and tests its proposed second-order factor construct model based on five sample data sets through simultaneous exploratory factor analysis (EFA) and multi-group confirmatory factor analysis (CFA). Key words: convention city attractiveness, scale development, multi-group CFA Background Convention tourism plays an important role in regional and national economic development (McCabe et al. 2000). In recent decades, convention tourism has come to be recognised for its economic significance, such as an enriched property redevelopment opportunities in the area surrounding convention centres (Sanders 1998) and the economic multiplier effect of expenditure on tourism related activities (Mistils and Dwyer 1999). Not only that, when managed successfully, convention tourism can also ensure future economic benefit to convention host cities and regions through the attendee revisits as holiday-makers with their travel companions as well as visits of those tourists who are induced by attendees positive word-of-mouth recommendations. It is this multi-fold economic potential that has fuelled interest in convention tourism marketing and management among policy makers and management and marketing researchers. Despite the prevalent interest in convention tourism marketing and management, however, research on the subject seems to remain relatively in the infant stage. This is most evident in the absence of vigorous research efforts in attitudinal scale development and on-going construct validation in the filed. The importance of reliable and valid (convergent and discriminant) scales is well documented in the literature as a catalyst for theory development and subsequent knowledge development. For example, Churchill (1979, p.64) asserts that the development of better measures of the variables is the critical element in the evolution of a fundamental body of knowledge in marketing, as well as for improved marketing practice. Here it is important to note that the scale development refers to on-going empirical scale validation by different researchers beyond a single project. Lively discourse in the previous decade on the SERVQUAL scale and its construct validity illustrates this point. In essence, scale development research seeks empirical evidence of generalisability of the goodness of measures (i.e., reliability and construct validity) across contexts, times, and respondents, in the same manner with theory development research where different researchers put a theory to test for its external validity. This paper initiates scale development research effort in convention tourism marketing by exploring a possibility of a development of standard measures of convention host city ANZMAC 2005 Conference: Marketing Research and Research Methodologies (quantitative) 69

2 attractiveness (CHCA). It is, in large part, motivated by our experience with idiosyncratic psychometric properties of different sample data sets generated from over-night attendees of five different conventions held in two cities in Shizuoka, Japan in Even in light of DeVellis (2003) caution about the influence of sample size upon the factorial properties of data, we felt that the observed idiosyncrasy was highly indicative of the presence of unique effects of convention attributes - e.g., scale (national vs. regional) and season (summer vs. winter) among others) in the data upon key convention tourism marketing variables of our interest (e.g., attendee satisfaction with and loyalty to a convention host city). For a systematic study of convention tourism marketing and its substantial theory development, we believe the development of a universally robust scale with construct validity and reliability essential. Only with such a scale, researchers can study unique effects of convention attributes upon convention tourism marketing variables. This study initiates such a research endeavour by developing a standard CHCA scale based on five data sets on hand. In this study, we define the construct as an overall attractiveness of a convention host city as a convention destination. We operationalise the construct as a second-order factor which manifests itself in different quality domains of the host city attributes. In other words, we posit that covariation among those first-order factor constructs can be explained by the posited higher order factor, CHCA. Marketing Literature on Scale Development Among many marketing scholars, Churchill s article, A Paradigm for Developing Better Measures of Marketing Constructs (1979), has been a popular reference source on scale development. The article prescribes an eight-step regime for scale development from the perspective of the domain sampling model in classical test theory (c.f., Nunnally and Bernstein 1994). Although the prescribed procedure and its content, in large part, remain intact, there have been some important developments in the scale development methodology since his seminal work. For instance, Churchill s coefficient alpha driven scale purification approach has been questioned in light of emergent consensus about multi-dimensionality of many socio-psychological constructs. Now scale developers are advised to explore first the multi-dimensionality of a construct or check a posited multi-faceted property of a construct through exploratory factor analysis (EFA) and then move onto assessment of simultaneous test of EFA and internal consistency of each sub-domain scale (Miller as cited in Flynn and Pearcy 2001, p. 414; John and Benet-Martínez 2000). Another significant development is fuelled by ever growing computing power. The availability of sophisticated computer programs like LISREL and EQS has promoted CFA as an alternative, popular research tool for construct validation to the conventional multitrait multimethod matrix. Finally, noteworthy is growing interest in formative construct indicators in the measurement model. Recent authors on the subject matter (e.g., Diamantopoulos and Winklhofer 2001; Jarvis, Mackenzie & Podsakoff 2003, Williams, Edwards & Vandenberg 2003) urge for researchers due-consideration into validity of a theory embedded or implied in their proposed measurement model as in many cases, indicators could be viewed as causing rather than being caused by the latent variable measured by the indicators (MacCallum and Browne 1993, p. 533). Nonetheless, their claim has not yet gained consensual support in the literature. Flynn and Pearcy (2001) have reviewed twenty four marketing journal articles with explicit reference to Churchill s scale development regime for a new scale development, to examine how faithfully the regime was followed across those sample articles. Through their analyis, they have identified the following four likely shortcomings among the new scale developers: ANZMAC 2005 Conference: Marketing Research and Research Methodologies (quantitative) 70

3 1) a small developmental sample size; 2) insufficient pilot study; 3) heavy reliance on the internal consistency measure or coefficient alpha over EFA for scale improvement; and 4) situation specific emphasis on different type(s) of validity (i.e., applied vs. basic research). Compared to the first unsettled issue of what constitutes an ideal case/variable ratio, the other three are rather a straightforward prescription. For instance, as noted earlier, the goal of scale development parallels with that of theory development i.e., the accumulation of empirical evidence to support or refute a tentatively upheld particular approach to operationalisation of a construct or abstractisation of a research phenomenon. Churchill s test-retest regime in a single project as part of new scale development reflects this essence of scientific investigation from a practical perspective (i.e., data collection is a costly exercise). The third point is self-explanatory given the preceding discussion on multi-dimensionality of many constructs. Finally as for the last point, Flynn and Percy (2001) caution that four types of validity (i.e., content, predictive, convergent and discriminant) are not equally relevant and important to all research as their relative relevance and importance to the researcher all depends on the nature of research. They advise that when researchers are concerned with diagnostic or managerial reasons in their investigation, their primary concern rests on content and predictive validity in their scale; on the other hand, when searching theoretical explanation like the current study, researchers emphasis should be directed to more to construct validity (convergent and discriminant) (p. 415). Notably this issue also applies to the aforementioned two types of indicators. For reflective indicators, construct validity and reliability are the fundamental requirement as the domain sampling theory suggests whereas for formative indicators, content and predictive validity constitute the heart of the measurement property. Methods and Conclusion The original research instrument in this study was developed by the second author of this paper. Prior to the scale development, he conducted a series of meetings and interviews with tourism/convention specialists in public sector (i.e., tourism/convention bureau staffs in three cities in Shizuoka, Japan and the prefecture government tourism promotion staffs) as well as in-depth interviews with over-nights convention attendees to those cities. Analysis of these multi-sourced qualitative data led to the identification of the following six convention host city attributes as the determinant of convention attendee satisfaction with the hosting city, namely 1) the city s transportation infrastructure; 2) the convention centre and surrounding infrastructure; 3) hotel services and facilities; 4) the city s tourism attractions; 5) catering service providers in the city; and 6) market offering of souvenirs in the city. For each attribute, scale items were generated based on qualitative information from the early qualitative research. Then his draft research instrument was assessed for content and face validity through further meetings and interviews with practitioners and over-night convention attendees in the cities, respectively. For this study, each of his original 35-items, which are designed to tap one of the aforementioned six attributes, was re-examined for its face validity and content validity with emphasis on its heterogeneous content within its assigned homogeneous domain group. This process was necessary as we needed items which tap the quality domain of each convention city attribute for the development and testing of our CHCA measurement model. In addition, we also examined their correlations with the average of three convention attendee host city satisfaction items across the five sample data sets. While the evaluation of face and content validity led to exclusion of six items, a series of the correlation analyses resulted in deletion ANZMAC 2005 Conference: Marketing Research and Research Methodologies (quantitative) 71

4 of two items for their non-significant correlations. In total, 27 items were included for the present study of a development of a standard CHCA scale. This study consisted of two phases of data analysis: 1) EFAs for identification of scale items with construct validity (in particular convergent validity) and reliability across the five sample data sets and 2) multi-group confirmatory factor analysis (MGCFAs) for testing of configural invariance of the scale and evaluation of the adequacy of the proposed second-order factor model across the data sets. First in the search of universally robust scale items with construct validity and reliability, we conducted a series of EFAs in light of Churchill s (1979) scale development regime and the aforementioned recent methodological developments. To take full advantage of the five data sets accessible to us, we conducted EFAs simultaneously, so that we could generate diagnostic information on psychometric behaviour of each scale item. Furthermore, we also adopted a nested approach to our model development (Jöreskog 1993) in the simultaneous EFAs. This approach has been widely debated in structural equation modelling community (e.g., Structural Equation Modelling, vol. 7 (1)) in recent years. Nonetheless, in this study setting where EFAs were conducted with five data sets with varying sample sizes (ranging from 88 to 181), the conservative and somewhat restrictive approach was viewed necessary. Otherwise, we would have had no measures throughout our EFAs to deal with instability in covariance estimates in EFA associated with small case/item ratio reported in the literature. EFAs were conducted with principal component and varimax rotation extraction method in parallel across the five data sets first for each of six sub-domain scales and then, sequentially for paired sub-domain scales, and the final round of paring with all scale items retained from previous EFAs. At every stage, each scale item was examined for its magnitude of factor loading on the target factor and internal consistency across the five data sets. Items with confounding factor loading behaviour (i.e., heavy or heavier loading on nontarget factor(s) and/or poor factor loading on the target factor) in more than two sample data sets were dropped one by one, and each time another EFA was run with the remaining items in search of a clear and universal factorial structure across the five data sets. In this course, 14 variables were dropped and this led to a merger of two sub-domains (4 and 6) and disappearance of sub-domain 5. Table 1 presents key statistics of the final factor solution with 11-items, four sub-domains scales (SD1: quality of city transportation infrastructure; SD2: quality of convention centre and surrounding infrastructure; SD3: quality of hotel services and facilities; and SD4: quality of city tourism attractions) as well as Cronbach s alpha coefficient of each of the four sub-scales. These 11 items were then submitted to EQS 6.0 to test first its configural invariance at the first-order factor level across the five data sets through MGCFA. The obtained goodness of fit measures (χ 2 (d.f.=190) = ; p<.00; Bentler-Bonett Non-Normed Fit Index (B-B NNFI) =.97; Comparative Fit Index (CFI) =.98; Bollen (IFI) Fit Index =.98; and Root Mean Square Error of Approximation (RMSEA) =.022.) were all supportive for the configural invariant property of the scale at the first-order factor level, except for the usual suspect of χ 2. In the measurement model, all correlation estimates were sufficiently large and statistically significant across the five data sets, except for r SD1,SD4 =.10 (t = 1.11) in Data 1. Next this measurement model was converted into a second-order factor model to account for the covariance between the four sub-domains with the higher order factor. This MGCFA produced the following statistics (χ 2 (d.f.=200) = ; p<.00; ), B-B NNFI =.97, CFI =.98; IFI =.98; and RMSEA =.021), indicating the hypothesised configural invariance of the standard CHCA scale at the second-order level. Finally, for the assessment of the adequacy ANZMAC 2005 Conference: Marketing Research and Research Methodologies (quantitative) 72

5 of the proposed (parsimonious) second-order factor model, Marsh and Hocevar s (1985) target coefficient was calculated with χ 2 statistics obtained from the two successive MGCFAs. The obtained coefficient of.96 (i.e., / ) provided strong support for the adequacy of the proposed second order factor model. Table 1 presents the obtained estimates of structural regression coefficients and their respective t-values in parentheses for each sample group. In conclusion, this study initiated scale development research in convention tourism marketing by developing a standard CHCA scale based on five data sets. As seen in Table 1, the 11-item scale appears to possess a desired measurement property to reflect unique effects of different convention attributes on perceived convention attendee CHCA with universal factorial structure and reliability; thus, the scale seems to be a useful addition of a research tool in the study of convention tourism marketing. Table 1: Summary of Key Statistics from EFAs and MGCFAs SD1 Accessibility of the convention centre from the city s central station Travel expense from the city s central station to the convention centre Location of the convention centre SD2 Synergistic commercial development of the convention centre surrounding to the convention centre Availability of catering facilities/services at the convention centre SD3 Hospitality of hotel staff.75 (.56) Value of Hotel meals (.82).75 Pricing of hotel room SD4 Attractive community heritage for tourism.91 ().91 Attractive tourism facilities () Encountered tourism spot/facility unique to the local community. Data 1 (R; CC1; City 1, W) Data 2 (N; Hotel; City 2; W) Data 3 (R; CC1; City 1; W) Data 4 (N; CC2; City 2; S) Data 5 (N; CC1; City 1; S).77 (.62) (N = 159: KMO =.68) (N = 88: KMO=) (N = 181: KMO=.79) (N = 120: KMO=.76) (N = 92: KMO=.79)) (.85) (.94).64 () () (.79).89 (.88) (.91) (.64) (.77).82 (.73).45 (.75).88 (.91) (.76).92 (.94) (.95) (.68).75 (.65) (.68).79 (.72).81 (.85).85 () (.91).93 (.95).41 (.70).85 (.82) (.71) (.75) (.75).72 (.60) (.65).75 (.62) ().89 (.97).91 (.82) (.88).87 (.97).88 ().79 (.76) (.76) (.72).88 () (.85).31 Cumulative % of variance extracted Cronbach s α Factor Loading Coefficients on the second-order factor (t value).38 (3.29).68 (4.79).63 (3.86).36 (3.20).59 (4.11).79 (5.16) Note: The table presents two sets of factor loading coefficients: one from EFA and, in parenthesis, another from MGCFA All factor loadings from MGCFA are significant at p<.01. Attributes of each sample data are described in parenthesis: by a scope of convention (regional or national), a convention venue (convention centre or hotel), a host city (city 1 or city 2), and a time of data collection (winter or summer)..91 (8.88).63 (5.46).58 (5.31).82 (9.23).65 (6.20).66 (7.43).67 (5.73).62 (4.23) (5).47 (3.71).52 (4.22) (5.27).63 (3.55).73 (5.59) ANZMAC 2005 Conference: Marketing Research and Research Methodologies (quantitative) 73

6 References Anderson, J.C., Gerbing, D.W., Some methods for respecifying measurement models to obtain unidimesional construct measurement. Journal of Marketing Research 19 (5), Baker, J.A., Lamb, C.W., Jr., Measuring architectural design service quality. Journal of Professional Services Marketing 10 (1), Churchill, G.A., Jr., A paradigm for developing better measures of marketing constructs. Journal of Marketing Research 16 (1), DeVellis, R.F., Scale development: theory and application (Second ed.), Sage Publications, California. Diamantopoulos, A., Winklhofer, H.M., Index construction with formative indicators: an alternative to scale development. Journal of Marketing Research 38 (2), Flynn, L.R., Pearcy, D., Four subtle sins in scale development: Some suggestions for strengthening the current paradigm. International Journal of Market Research 43 (4), Jarvis, C.B., Mackenzie, S.B., Podsakoff, P.M., A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research 30 (2), John, O.P., Benet-Martínez, V., Measurement: reliability, construct validation, and scale construction. In: Reis, H.T., Judd, C.M. (Eds.), Handbook of Research Methods in Social and Personality Psychology, Cambridge University Press, Cambridge, pp Jöreskog, K.G Testing structural equation models, in Testing structural equation models. In: Bollen, K.A., Long, J.S., Sage Publications, Newbury Park, California, pp Marsh, H.W., Hocevar, D Application of confirmatory factor analysis to the study of self-concept: first- and higher order factor models and their invariance across groups. Psychological Bulletin 97 (3), McCabe, V., Poole, B., Weeks, P., Leiper, N., The business and management of conventions, John Wiley & Sons, Brisbane. Mistilis, N., Dwyer, L., Tourism gateways and regional economies: The distributional impacts of MICE. The International Journal of Tourism Research, 1 (6), Nunnally, J.C., Bernstein, I.H., Psychometric Theory (Third Ed.), McGraw-Hill, New York. Sanders, H.T., Convention centre follies, Public Interest. 132 (Summer), 132, Available from ABI/Inform Global, accessed 7 June Steenkamp, J-B.E.M., Baumgartner, H., Assessing measurement invariance in crossnational consumer research. Journal of Consumer Research 25 (1), ANZMAC 2005 Conference: Marketing Research and Research Methodologies (quantitative) 74

7 Williams, L.J., Edwards, J.R., Vandenberg, R.J., Recent advances in causal modelling methods for organizational and management research. Journal of Management 29 (6), ANZMAC 2005 Conference: Marketing Research and Research Methodologies (quantitative) 75

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