| Literature DB >> 36135124 |
Silvia Puiu1, Liliana Velea2, Mihaela Tinca Udristioiu3, Alessandro Gallo2.
Abstract
The main objective of our research is to identify the impact of recycling and waste reduction behavior on the sustainable tourism decisions of Romanian youngsters (18-25 years old). We used the PLS-SEM method and introduced four variables in the model: sustainable tourism decisions, the interest in recycling, the interest in waste reduction, and the interest in natural and less polluted touristic destinations. The main results emphasize the direct influence of recycling and waste reduction behaviors on the decisions made by Generation Z regarding sustainable tourism and on their preference for destinations that are better preserved and less touched by human intervention. The novelty of our research consists of the fact that we introduced variables such as waste reduction from the perspective of tourists because most studies address it as a management approach of the companies in the tourism sector. The findings are useful for managers in the tourism sector to create better strategies for attracting the younger generation who are preoccupied by environmental issues and sustainability in general.Entities:
Keywords: Generation Z; healthy behavior; hospitality industry; sustainable tourism decision; waste reduction
Year: 2022 PMID: 36135124 PMCID: PMC9495311 DOI: 10.3390/bs12090320
Source DB: PubMed Journal: Behav Sci (Basel) ISSN: 2076-328X
Figure 1The research model. Source: Created using SmartPLS.
Constructs and items in the model.
| Constructs | Items | Codes | Source |
|---|---|---|---|
| Sustainable tourism decisions (STD) | I prefer to choose less polluting transportation means for traveling. | STD1 | [ |
| I prefer to practice a form of tourism that is more friendly with the environment. | STD2 | Own contribution | |
| I prefer eco-friendly accommodation options during my vacation. | STD3 | [ | |
| I come back to accommodation units that are more eco-friendly. | STD4 | [ | |
| I prefer restaurants which offer vegan and vegetarian menus. | STD5 | [ | |
| I would pay more for a more sustainable vacation. | STD6 | [ | |
| The interest in recycling (RCL) | I am interested in recycling even during vacations. | RCL1 | [ |
| The interest in waste reduction (WRED) | I prefer to use only one set of towels during short stays in an accommodation unit. | WRED1 | Own contribution |
| I prefer to not use resources offered by the housing unit (cosmetics, hygiene products, bathrobe etc.) if I do not need them. | WRED2 | Own contribution | |
| I prefer to use my own hygienic products in a housing unit to avoid waste. | WRED3 | Own contribution | |
| I avoid water waste during my vacations as much as possible. | WRED4 | [ | |
| I avoid energy waste as much as possible during my vacation. | WRED5 | [ | |
| During my vacation, I pay attention to not waste the food at restaurants or all-inclusive accommodation units. | WRED6 | [ | |
| When buying souvenirs, I prefer sustainable materials to avoid plastic waste. | WRED7 | Own contribution | |
| The interest in natural and less polluted touristic destinations (NLP) | I pay attention to the pollution level of the touristic destination I choose. | NLP1 | [ |
| I prefer touristic regions where nature is intact or there are few human interventions. | NLP2 | Own contribution |
Outer loadings and VIF values.
| Items | Outer Loadings | Collinearity Statistics (VIF) |
|---|---|---|
| STD1 | 0.665 | 1.735 |
| STD2 | 0.773 | 2.017 |
| STD3 | 0.816 | 2.088 |
| STD4 | 0.825 | 2.129 |
| STD5 | 0.608 | 1.403 |
| STD6 | 0.831 | 2.168 |
| RCL1 | 1.000 | 1.000 |
| WRED1 | 0.335 | 1.269 |
| WRED2 | 0.590 | 1.791 |
| WRED3 | 0.553 | 1.479 |
| WRED4 | 0.822 | 3.696 |
| WRED5 | 0.858 | 3.989 |
| WRED6 | 0.770 | 1.759 |
| WRED7 | 0.749 | 1.466 |
| NLP1 | 0.868 | 1.313 |
| NLP2 | 0.857 | 1.313 |
Source: Authors’ analysis using SmartPLS.
Figure 2The path coefficients and outer loadings. PLS algorithm calculation. Source: calculated with SmartPLS.
Construct reliability and validity.
| Construct | Cronbach’s Alpha | rho_A | Composite Reliability | AVE |
|---|---|---|---|---|
| NLP | 0.656 | 0.658 | 0.853 | 0.744 |
| RCL | 1.000 | 1.000 | 1.000 | 1.000 |
| STD | 0.849 | 0.864 | 0.889 | 0.574 |
| WRED | 0.837 | 0.848 | 0.890 | 0.669 |
Source: Authors’ analysis using SmartPLS.
Fornell–Larcker criterion.
| Construct | NLP | RCL | STD | WRED |
|---|---|---|---|---|
| NLP | 0.863 | |||
| RCL | 0.502 | 1.000 | ||
| STD | 0.627 | 0.643 | 0.758 | |
| WRED | 0.581 | 0.652 | 0.647 | 0.818 |
Source: determined using SmartPLS.
The bootstrapping test.
| T Statistics | Confidence Interval Bias Corrected | ||
|---|---|---|---|
| NLP -> STD | 3.181 | 0.002 | (0.113, 0.514) |
| RCL -> NLP | 2.125 | 0.034 | (0.012, 0.394) |
| RCL -> STD | 3.904 | 0.000 | (0.141, 0.459) |
| WRED -> NLP | 4.870 | 0.000 | (0.252, 0.611) |
| WRED -> STD | 2.800 | 0.005 | (0.083, 0.428) |
Source: calculated using SmartPLS.
Hypotheses’ validation.
| Hypothesis | Validation |
|---|---|
| RCL -> STD (H1) | Supported |
| WRED -> STD (H2) | Supported |
| NLP -> STD (H3) | Supported |
| RCL -> NLP (H4) | Supported |
| WRED -> NLP (H5) | Supported |
Source: Authors’ analysis.
The blindfolding test.
| Construct | SSO | SSE | Q2 |
|---|---|---|---|
| NLP | 316.000 | 234.932 | 0.257 |
| RCL | 158.000 | 158.000 | |
| STD | 948.000 | 648.063 | 0.316 |
| WRED | 632.000 | 632.000 |
Source: calculated using SmartPLS.
Descriptive statistics for the remaining items.
| Items | Mean | Standard Deviation | Outer Loading |
|---|---|---|---|
| NLP1 | 3.633 | 1.328 | 0.872 |
| NLP2 | 4.171 | 1.090 | 0.853 |
| RCL1 | 3.747 | 1.316 | 1.000 |
| STD1 | 3.684 | 1.232 | 0.666 |
| STD2 | 4.095 | 1.087 | 0.772 |
| STD3 | 3.127 | 1.339 | 0.816 |
| STD4 | 3.671 | 1.318 | 0.825 |
| STD5 | 2.538 | 1.422 | 0.609 |
| STD6 | 3.829 | 1.190 | 0.830 |
| WRED4 | 3.728 | 1.353 | 0.838 |
| WRED5 | 3.867 | 1.267 | 0.871 |
| WRED6 | 4.133 | 1.179 | 0.790 |
| WRED7 | 3.475 | 1.408 | 0.769 |
Source: calculated using JASP [60] and SmartPLS.