Research model and data collection
Figure 1 depicts the research model. Packaging positively affects both attitudes and behavioral intentions. Attitude also positively impacts behavioral intention. Antidrug information significantly moderates the relationship between packaging and attitude.
This research used a survey for data collection via the Google survey platform. For data collection, this work adopted a clickworker (https://www.clickworker.com/). Many studies have adopted clickworker data for statistical inference (Garaus & Hudáková, 2022; Kremer et al., 2022; Racat & Plotkina, 2023). Such outcomes are likely to provide evidence for ensuring the quality of clickworker data. This research initially collected 317 observations. This research asked about participants’ experience with drug tourism at the beginning of the survey. In this work, drug tourism was defined as traveling where drugs are legal, which was addressed at the beginning of the survey. That is, the actual consumption of drugs is beyond the scope of this work. Then, a total of 117 participants who had no experience in drug tourism were excluded from the valid sample because this research pertained to consumers’ experience with packaging in drug tourism. Thus, this research used 200 observations for data analysis. The period of data collection was between 17th January and 20th January in 2024.
Table 1 displays the information of the survey participants. There were 144 males and 56 females. The numbers of people under $2,500, $2,500-4,999, $5,000-7,499, $7,500-9,999, and over $10,000 are 57, 71, 28, 12, and 32, respectively. Table 1 presents the age of the survey participants (20 s or younger: 61, 30 s: 80, 40 s: 44, 50 s: 11, and older than 60: 4). Table 1 also shows the percentages of annual international travel frequency (fewer than once: 50.0%, 1-2 times: 42.0%, and more than 3 times: 8.0%) and terminal academic degrees (fewer than a bachelor’s degree: 41.5%, a bachelor’s degree: 43.5%, and more than a graduate degree: 15%).
Table 1 Demographic profile of survey participants (n = 200)
Item
|
Frequency
|
Percent
|
Male
Female
Monthly household income
Under $2,500
$2,500-4,999
$5,000-7,499
$7,500-9,999
Over $10,000
20 s or younger
30 s
40 s
50 s
Older than 60 s
Annual international travel frequency
Less than 1 time
1-2 times
More than 3 times
Terminal academic degree
Less than bachelor degree
Bachelor degree
More than graduate degree
|
144
56
57
71
28
12
32
61
80
44
11
4
100
84
16
83
87
30
|
72.0
28.0
28.5
35.5
14.0
6.0
16.0
30.5
40.0
22.0
5.5
2.0
50.0
42.0
8.0
41.5
43.5
15.0
|
Measurement items
Table 2 presents the information of the measurement items. This research derived measurement items referencing the extant literature on packaging (Muslim et al., 2020; Theben et al., 2020), attitudes (Moon et al., 2022; Liao et al., 2023), and behavioral intentions (Jung et al., 2020; Mailizar et al., 2021). Most items were measured by a five-point Likert scale (1= strongly disagree, 5 = strongly agree), whereas this study measured attitudes using a semantically different scale (1 = bad, 5 =good). Moreover, the measurement items for antidrug information were developed through consultation with experts in the tourism research domain. The definition of packaging is how individuals assess the packaging of drug products in tourism destinations. The definition of antidrug information is how an individual possesses knowledge about the risk of an illegal drug. Additionally, this study defined attitude as the evaluation of drug tourism from the perspective of survey participants. In addition, behavioral intention is defined as how participants are willing to travel to a drug tourism destination.
Table 2 Construction illustration
Construct
|
Code
|
Item
|
Packaging
|
PA1
PA2
PA3
PA4
|
Packaging for drug product is beautiful in tourism destination.
Packaging for drug product is aesthetic in tourism destination.
Packaging for drug product is charming in tourism destination.
Packaging for drug product is attractive in tourism destination.
|
Antidrug information
|
AI1
AI2
AI3
AI4
|
I am exposed to warnings about the risk of drugs.
I experienced with the risks of drugs.
I watched content that warns about the risk of drugs.
There are many contents for warning the risk of drug around of me.
|
Attitude
|
AT1
AT2
AT3
AT4
|
For me, drug tourism is (negative - positive)
For me, drug tourism is (bad - good)
For me, drug tourism is (unfavorable - favorable)
For me, drug tourism is (undesirable - desirable)
|
Behavioral intention
|
BI1
BI 2
BI 3
BI 4
|
I have an intention to visit a drug tourism destination.
I intend to go to a drug tourism destination.
I am going to a drug tourism destination.
I intend to visit the place for drug tourism.
|
Data analysis
This study derived survey participants’ demographic information through frequency analysis. Confirmatory factor analysis was implemented to examine convergent validity by following the following criteria: loading > 0.5, average value extracted (AVE) > 0.5, and construct reliability (CR) > 0.7 (Fornell & Larcker, 1981; Hoyle, 1995). The extant literature (Hoyle, 1995; Hair et al., 2010) has shown that the goodness of fit for confirmatory factor analysis is acceptable according to the following criteria: goodness-of-fit index (GFI), normed fit index (NFI), relative fit index (RFI), incremental fit index (IFI), Tucker–Lewis index (TLI), comparative fit index (CFI) > 0.8, and root mean square error of approximation (RMSEA) < 0.1. Next, this work computed the mean values and standard deviations (SDs) for the constructs. Then, the correlation matrix was analyzed to examine the discriminant validity, referencing the rule that the square root of the AVE is greater than the correlation coefficient (Fornell & Larcker, 1981; Hoyle, 1995; Hair et al., 2010). Moreover, this work selected the process macro model to assess the relationships between variables. PROCESS Model 7 was selected. PROCESS Model 7 is based on an ordinary least squares regression for the path analytic model to analyze the conditional indirect effects of the mediated moderation model (Hayes, 2012). The model employed bias-corrected 95% bootstrap confidence intervals for conditional indirect effects using 5000 bootstrap samples (Hayes, 2012). The interaction variable was produced to explore the moderating effect (packaging × antidrug information). Additionally, this work performed a median split analysis computing the mean value to further scrutinize the moderating effect by calculating the mean value for the four groups. Using the results of the median split analysis, this work generated four groups: 1) high packaging × high antidrug information, 2) high packaging × low antidrug information, 3) low packaging × high antidrug information, and 4) low packaging × low antidrug information, using attitude as the dependent variable.