آزمون اثرات تجانس، محدودیت سفر، و خوداثربخشی در مورد مقاصد سفر: مدل تصمیم گیری های جایگزین
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|26322||2012||13 صفحه PDF||سفارش دهید||12022 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Tourism Management, Volume 33, Issue 4, August 2012, Pages 855–867
Travel decision making has been extensively studied. Various models and theories have been proposed to explain tourist behavior. Taking a new approach, this study applied the Motivation–Opportunity–Ability (MOA) model to explain travel intentions. The MOA model suggests that motivation, opportunity, and ability are major factors influencing travel intentions. This study explored the role of self-congruity, functional congruity, perceived travel constraints, constraint negotiation, and self-efficacy on travel intentions. The proposed model and hypotheses were tested in the context of cruise tourism. An online panel survey was conducted with cruisers. Structural Equation Modeling (SEM) was used to test both the proposed model and hypothesized relationships among the constructs. All hypotheses except one were supported by the data. The proposed model also had an acceptable fit to the data. Highlights ► This study proposes an alternative travel decision-making model. ► The proposed model and hypotheses were tested in the context of cruise tourism. ► An online panel survey was conducted with cruisers. ► All hypotheses except one were supported by the data.
Decision-making studies are multi-disciplinary in nature and have evolved from a wide range of fields including psychology (e.g., Harmon-Jones, 2000, Oyserman et al., 2007 and Pablo et al., 2007), sociology (e.g., Howard, 2000, Lawler et al., 2000 and Pierce et al., 2003), marketing (e.g., Cotte and Wood, 2004, Mandel, 2003 and Simonson et al., 2001), and communication (e.g., Homer, 2006, Katz, 1973 and Till and Baack, 2005). Although different theories or conceptual models (e.g., Theory of Planned Behavior by Ajzen, 1991; Goal Hierarchy of Motivation by Bettman, 1979; Elaboration Likelihood Model of Persuasion by Petty & Cacioppo, 1980; Brand Personality by Aaker, 1997) have been proposed for explaining consumers’ decisions, no one unifying theory has been agreed upon by scholars to fully explain decision making (Sirakaya & Woodside, 2005). Simonson et al. (2001, p. 251) suggested that this might be because “consumer behavior is too complex to be meaningfully captured in a single model.” Alternative approaches may enhance our understanding of decision making from different ways. The current study proposes an alternative model, situated in the Motivation–Opportunity–Ability (MOA) framework, for explaining travel intentions. An observation derived from past decision-making studies is that scholars usually consider decision making as a rational process which involves multiple stages (Sirakaya & Woodside, 2005) in which consumers logically derive their final decision. For instance, Crompton (1992) and Botha, Crompton, and Kim (1999) proposed a destination choice model in which people narrowed their choices from an awareness set, initial consideration set, and late consideration set to derive their final destination choice. Based on Assael’s (1984) work, Vogt and Fesenmaier (1998) introduced an information search model in which the information search process is comprised of five stages: input variables, information acquisition, information process, brand evaluation, and purchase. Sirakaya and Woodside (2005) summarized previous decision-making studies and suggested that people usually go through the following steps when making a travel decision: 1) recognizing the need for making a decision; 2) identifying goals; 3) formulating choice sets; 4) collecting information on each choice; 5) making a choice among the alternatives; 6) purchasing and/or consuming products/services; and 7) post-purchase evaluation. Although these models present a logical hierarchical process of decision making, some scholars (e.g., Crompton and Ankomah, 1993, Oppermann, 1998 and Petrick et al., 2007) have suggested that not everyone follows all the steps scripted above. People are more likely to skip some stages of decision making when they are brand loyal (Petrick et al., 2007), have previous experience (Oppermann, 1998), are familiar with the products/services (Prentice & Andersen, 2000), have social influences (Petrick et al., 2007), are more involved in their decision-making process (Crompton & Ankomah, 1993), and/or if their decisions are routinized (Crompton & Ankomah, 1993). Petrick et al. (2007) studied decision making of cruisers and found that Crompton’s (1992) destination choice set model, which is a multi-stage decision-making model, did not explain the phenomenon. This implies that the traditional multi-stage approach may not be applicable to explain tourists’ decision makings due to its sensitivity to the factors mentioned above. Using the Motivation–Opportunity–Ability (MOA) model (MacInnis & Jaworski, 1989) as a guiding framework, the current study will evaluate travel motivation, opportunity, and ability as well as their influences on travel intentions. This model differs from previous decision-making models in two ways. First, the model does not follow the traditional multi-stage approach of other decision-making models. Rather, the focus is more on identifying the key factors affecting behavioral intentions and examining the interactions among these factors. Second, the model incorporates both rational and hedonic components of decision making, to hopefully present a more holistic picture of decision making. It is hoped that the proposed model will offer an alternative understanding of travel intentions and the decisions that affect them.
نتیجه گیری انگلیسی
In summary, this study explored different factors which influence people’s intentions to take a cruise vacation. An alternative travel decision model was proposed and empirical tested. The proposed model was constructed based on the MOA framework, in which behavior is affected by three antecedents: motivation, opportunity, and ability. In the current study, motivation was measured by both self-congruity and functional congruity; opportunity was measured by constraints to cruising; and ability was measured by self-efficacy. The proposed model was tested in cruise tourism and was found to have an acceptable fit to the data, which provided evidence for validating the MOA model. Since the current study was an initial attempt to apply the MOA model to the context of cruise tourism, further investigation will be needed to validate the model in other study contexts. The study used self-congruity and functional congruity to measure travel motivation. However, in the tourism literature, travel motivations have been traditionally measured with the travel motivation scale originally developed by Crompton (1979). Without direct comparison, it is unknown which measurements are more effective in measuring travel motivation. Therefore it is believed that further investigation comparing Crompton’s (1979) measures of motivation with congruity measures of motivation would contribute to this body of knowledge. Past studies have also suggested that repeaters and first timers are different in many aspects such as their perceived value and quality (Petrick, 2004a), travel motivations and intended activities (Lau & McKercher, 2004), and visitation pattern (Oppermann, 1997). It would be interesting to investigate if the MOA model performs differently across non-cruisers, first timers and repeat cruisers. The study was pilot-tested among 293 undergraduate students. Although this presents a homogeneous sample for developing measurement scales, the results would have been more convincing if the profile of these respondents were more similar to the target market of cruise companies. Also, although panel surveys are a common data collection method and have been practiced widely in different fields including: consumer behavior (e.g., Lohse, Bellman, & Johnson, 2000), health (e.g., Contoyannis, Jones, & Rice, 2004), communication (e.g., Beaudoin, 2007), leisure (e.g., Kuentzel & Heberlein, 2006), and travel (e.g., Li and Petrick, 2008a and Li and Petrick, 2008b), non-internet users are excluded from the samples. Future research may test the proposed model with a sample including offline cruisers.