Exploring Influential Factors with Structural Equation Modeling–Artificial Neural Network to Involve Medicine Users in Home Medicine Waste Management and Preventing Pharmacopollution
Abstract
:1. Introduction
2. Materials and Methods
2.1. Hypothesis Formulation
2.1.1. The User’s Attitude
2.1.2. Marketing Campaigns
2.1.3. Collection Points
2.1.4. Handling Safety
2.1.5. Medical Prescription
2.1.6. Package Contents
2.1.7. Public Policies and Laws
2.2. Research Universe and Sample
2.3. Data Collection Instrument
2.4. Data Analysis
3. Results and Discussion
3.1. Demographic Profile of the Respondents
3.2. Model Fitting
3.3. Estimations of Structural Equation and Hypotheses Testing
3.4. Estimations of Artificial Neural Networks Models
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Demographic Feature | Option | Responses | Percentage (%) |
---|---|---|---|
Gender | Female | 536 | 56.30% |
Male | 416 | 43.70% | |
Age | 18–21 | 127 | 13.30% |
22–28 | 408 | 42.90% | |
29–35 | 176 | 18.50% | |
36–41 | 143 | 15.00% | |
Above 41 | 98 | 10.30% | |
Income (103 reais—Brazilian currency) | Less than 1.32 | 147 | 15.40% |
1.32–3.96 | 290 | 30.50% | |
3.96–6.6 | 195 | 20.50% | |
6.6–13.2 | 173 | 18.20% | |
13.2–26.4 | 109 | 11.40% | |
Above 26.4 | 38 | 4.00% | |
Education | Illiterate | 0 | 0.00% |
Primary | 52 | 5.40% | |
High school | 179 | 18.80% | |
College degree | 489 | 51.40% | |
Graduate | 232 | 24.40% |
Variable | Item | Standard Loading | CR | AVE | Cronbach’s Alpha |
---|---|---|---|---|---|
Attitude | 0.913 | 0.612 | 0.933 | ||
ATD1 | I have a favorable attitude towards HWM | 0.744 | |||
ATD2 | I have a favorable attitude to the view that disposing of HWM adequately is a benefit | 0.757 | |||
ATD3 | I have a favorable attitude to the view that disposing of HWM adequately has good effects on the environment | 0.720 | |||
ATD4 | I have a favorable attitude to the view that disposing of HWM adequately has good effects on society | 0.923 | |||
Marketing Campaigns | 0.958 | 0.572 | 0.872 | ||
MKT1 | I have access to marketing campaigns on the adequate management of HWM | 0.907 | |||
MKT2 | I know about the adequate management of HWM because of marketing campaigns | 0.913 | |||
MKT3 | I will certainly manage HWM adequately if the marketing campaigns show me how to do this | 0.814 | |||
Collection Points | 0.973 | 0.641 | 0.922 | ||
CLT1 | I am familiar with where there are collection points that you can get to easily | 0.721 | |||
CLT2 | I know where there are collection points I can get to easily from my home | 0.989 | |||
CLT3 | I can easily get to collection points on my way to my place of work | 0.732 | |||
Safety in Handling | 0.781 | 0.501 | 0.849 | ||
SFT1 | I am able to handle HWM safely | 0.753 | |||
SFT2 | I have total control over the handling of HWM | 0.757 | |||
SFT3 | It would be great if they could collect HWM from my home | 0.720 | |||
Medical Prescription | 0.812 | 0.706 | 0.860 | ||
PRS1 | The prescription for medicines is never bigger than necessary | 0.989 | |||
PRS2 | I am willing to pay to purchase the amount of medication prescribed by the doctor | 0.819 | |||
PRS3 | I am confident in the amount of medication prescribed by the doctor | 0.972 | |||
PRS4 | I will take the amount of medication prescribed by the doctor if my treatment is effective | 0.844 | |||
Content of the Packaging | 0.953 | 0.533 | 0.963 | ||
QTY1 | I sometimes buy a larger number of medications on the contents of the packaging than is on the doctor’s prescription | 0.723 | |||
QTY2 | I will pay to buy a larger amount of medication because of the contents declared on the packaging | 0.714 | |||
QTY3 | I buy a larger amount of medication when the sales team offers me benefits because of the contents present on the packaging | 0.734 | |||
Public Policies and Laws | 0.873 | 0.674 | 0.892 | ||
PPL1 | I am familiar with public policy and law regarding the proper management of HWM | 0.713 | |||
PPL2 | I intend to dispose of HWM properly if public policy and laws will be more restrictive | 0.726 | |||
Cooperative Behavior | |||||
CB1 | My behavior as to properly managing HWM is cooperative when I know what the risks involved are | 0.718 | 0.832 | 0.553 | 0.829 |
CB2 | I behave cooperatively as to properly managing HWM when I know what the public policies and laws that regulate this activity are | 0.837 | |||
CB3 | I will manage HWM properly because it will be good for society and the environment | 0.824 |
Variable | ATD | MKT | CLT | SFT | PRS | QTY | PPL |
---|---|---|---|---|---|---|---|
ATD | [0.782] * | ||||||
MKT | 0.432 | [0.756] * | |||||
CLT | 0.283 | 0.472 | [0.801] * | ||||
SFT | 0.547 | 0.274 | 0.423 | [0.708] * | |||
PRS | 0.387 | 0.343 | 0.264 | 0.527 | [0.840] * | ||
QTY | 0.102 | 0.641 | 0.511 | 0.321 | 0.320 | [0.730] * | |
PPL | 0.397 | 0.467 | 0.605 | 0.432 | 0.220 | 0.764 | [0.821] * |
Hypothesis | Hypothesized Path | Path Coefficient (β) | p-Value * | Decision | VIF | R2 |
---|---|---|---|---|---|---|
H1 | ATDCB | 1.562 | 0.0700 | Accept | 1.381 | 0.64 |
H2a | MKTCB | 0.362 | 0.0300 | Accept | 1.416 | |
H3 | CLTCB | 0.552 | 0.0400 | Accept | 1.361 | |
H4 | SFTCB | −0.090 | <0.0001 | Reject | 1.245 | |
H5 | PRSCB | 0.321 | 0.0018 | Reject | 1.323 | |
H7 | PPLCB | 0.164 | 0.0150 | Accept | 1.056 | |
Moderating Effect | ||||||
H2b | MKTATD | 0.591 | 0.0700 | Accept | 1.321 | |
H6 | QTYPRS | −0.336 | 0.0300 | Accept | 1.116 |
Model 1: Training | N | SSE | RMSE | Model 1: Testing | N | SSE | RMSE |
---|---|---|---|---|---|---|---|
ANN1 | 857 | 12.159 | 0.126 | ANN1 | 95 | 0.857 | 0.119 |
ANN2 | 849 | 11.588 | 0.128 | ANN2 | 103 | 1.416 | 0.138 |
ANN3 | 847 | 11.520 | 0.129 | ANN3 | 105 | 1.261 | 0.124 |
ANN4 | 854 | 10.728 | 0.130 | ANN4 | 98 | 1.345 | 0.122 |
ANN5 | 856 | 11.117 | 0.144 | ANN5 | 96 | 1.223 | 0.125 |
ANN6 | 848 | 11.502 | 0.145 | ANN6 | 104 | 1.281 | 0.138 |
ANN7 | 850 | 11.422 | 0.127 | ANN7 | 102 | 1.445 | 0.127 |
ANN8 | 852 | 10.222 | 0.138 | ANN8 | 100 | 1.523 | 0.132 |
ANN9 | 854 | 10.785 | 0.144 | ANN9 | 98 | 1.656 | 0.136 |
ANN10 | 855 | 11.142 | 0.143 | ANN10 | 97 | 1.785 | 0.132 |
ANN11 | 856 | 10.532 | 0.129 | ANN11 | 96 | 1.822 | 0.134 |
ANN12 | 857 | 10.012 | 0.132 | ANN12 | 95 | 1.433 | 0.135 |
ANN13 | 855 | 11.152 | 0.154 | ANN13 | 97 | 1.525 | 0.131 |
ANN14 | 853 | 10.789 | 0.136 | ANN14 | 99 | 1.322 | 0.128 |
ANN15 | 852 | 11.123 | 0.133 | ANN15 | 100 | 1.454 | 0.127 |
Mean | 0.126 | Mean | 0.129 | ||||
SD | 0.008 | SD | 0.006 | ||||
Model 2: Training | N | SSE | RMSE | Model 2: Testing | N | SSE | RMSE |
ANN1 | 856 | 16.159 | 0.146 | ANN1 | 96 | 1.481 | 0.107 |
ANN2 | 850 | 14.588 | 0.140 | ANN2 | 102 | 1.516 | 0.128 |
ANN3 | 848 | 13.520 | 0.159 | ANN3 | 104 | 1.461 | 0.104 |
ANN4 | 855 | 11.728 | 0.140 | ANN4 | 97 | 1.345 | 0.123 |
ANN5 | 853 | 12.117 | 0.143 | ANN5 | 99 | 1.423 | 0.124 |
ANN6 | 849 | 13.502 | 0.146 | ANN6 | 103 | 1.281 | 0.135 |
ANN7 | 852 | 14.422 | 0.147 | ANN7 | 100 | 1.445 | 0.122 |
ANN8 | 853 | 15.222 | 0.148 | ANN8 | 99 | 1.423 | 0.122 |
ANN9 | 854 | 13.785 | 0.145 | ANN9 | 98 | 1.156 | 0.137 |
ANN10 | 855 | 12.142 | 0.142 | ANN10 | 97 | 1.252 | 0.135 |
ANN11 | 856 | 11.532 | 0.152 | ANN11 | 96 | 1.353 | 0.136 |
ANN12 | 857 | 12.012 | 0.142 | ANN12 | 95 | 1.444 | 0.133 |
ANN13 | 855 | 12.152 | 0.153 | ANN13 | 97 | 1.445 | 0.135 |
ANN14 | 853 | 11.789 | 0.152 | ANN14 | 99 | 1.246 | 0.128 |
ANN15 | 852 | 12.123 | 0.136 | ANN15 | 100 | 1.237 | 0.127 |
Mean | 0.146 | Mean | 0.126 | ||||
SD | 0.006 | SD | 0.010 | ||||
Model 3: Training | N | SSE | RMSE | Model 3: Testing | N | SSE | RMSE |
ANN1 | 855 | 15.559 | 0.136 | ANN1 | 97 | 1.481 | 0.139 |
ANN2 | 852 | 13.488 | 0.150 | ANN2 | 100 | 1.516 | 0.136 |
ANN3 | 848 | 12.320 | 0.149 | ANN3 | 105 | 1.461 | 0.134 |
ANN4 | 853 | 13.428 | 0.150 | ANN4 | 104 | 1.345 | 0.132 |
ANN5 | 852 | 14.317 | 0.153 | ANN5 | 100 | 1.423 | 0.135 |
ANN6 | 849 | 12.302 | 0.136 | ANN6 | 103 | 1.281 | 0.138 |
ANN7 | 851 | 15.322 | 0.147 | ANN7 | 101 | 1.445 | 0.127 |
ANN8 | 854 | 16.422 | 0.158 | ANN8 | 98 | 1.423 | 0.134 |
ANN9 | 854 | 14.685 | 0.135 | ANN9 | 98 | 1.156 | 0.135 |
ANN10 | 855 | 13.442 | 0.152 | ANN10 | 97 | 1.252 | 0.136 |
ANN11 | 856 | 12.332 | 0.142 | ANN11 | 96 | 1.353 | 0.134 |
ANN12 | 857 | 13.112 | 0.152 | ANN12 | 95 | 1.444 | 0.136 |
ANN13 | 855 | 14.252 | 0.143 | ANN13 | 97 | 1.445 | 0.132 |
ANN14 | 853 | 12.489 | 0.155 | ANN14 | 99 | 1.246 | 0.138 |
ANN15 | 852 | 15.223 | 0.156 | ANN15 | 100 | 1.237 | 0.139 |
Mean | 0.148 | Mean | 0.135 | ||||
SD | 0.008 | SD | 0.003 |
Model 1: Output = CB | ATD | MKT | CLT | SFT | PRS | PPL | Model 2: Output = MKTATD | Model 3: Output = QTY PRS |
---|---|---|---|---|---|---|---|---|
ANN1 | 0.232 | 0.143 | 0.146 | 0.012 | 0.014 | 0.113 | 1 | 1 |
ANN2 | 0.259 | 0.102 | 0.148 | 0.013 | 0.015 | 0.112 | 1 | 1 |
ANN3 | 0.257 | 0.132 | 0.149 | 0.013 | 0.023 | 0.122 | 1 | 1 |
ANN4 | 0.231 | 0.122 | 0.150 | 0.014 | 0.024 | 0.103 | 1 | 1 |
ANN5 | 0.243 | 0.125 | 0.154 | 0.016 | 0.106 | 0.105 | 1 | 1 |
ANN6 | 0.282 | 0.126 | 0.155 | 0.017 | 0.116 | 0.116 | 1 | 1 |
ANN7 | 0.285 | 0.124 | 0.157 | 0.010 | 0.015 | 0.114 | 1 | 1 |
ANN8 | 0.284 | 0.125 | 0.148 | 0.007 | 0.008 | 0.106 | 1 | 1 |
ANN9 | 0.276 | 0.121 | 0.154 | 0.021 | 0.024 | 0.123 | 1 | 1 |
ANN10 | 0.263 | 0.127 | 0.153 | 0.026 | 0.036 | 0.120 | 1 | 1 |
ANN11 | 0.238 | 0.128 | 0.149 | 0.030 | 0.105 | 0.124 | 1 | 1 |
ANN12 | 0.240 | 0.129 | 0.142 | 0.012 | 0.106 | 0.112 | 1 | 1 |
ANN13 | 0.248 | 0.124 | 0.154 | 0.017 | 0.108 | 0.114 | 1 | 1 |
ANN14 | 0.243 | 0.123 | 0.146 | 0.013 | 0.110 | 0.111 | 1 | 1 |
ANN15 | 0.244 | 0.120 | 0.153 | 0.060 | 0.109 | 0.110 | 1 | 1 |
Average Importance | 0.232 | 0.143 | 0.146 | 0.012 | 0.014 | 0.113 | 1 | 1 |
Normalized Importance (%) | 100.000 | 61.637 | 62.931 | 5.172 | 6.034 | 48.706 | 100.000 | 100.000 |
SEM | ANN | |||||
---|---|---|---|---|---|---|
Independent Variable | Path Coefficient | Result | Ranking as per Path Coefficient | Ranking | Normalized Importance (%) | Remark |
ATD | 1.562 | Significant | 1 | 1 | 100.000 | Matched |
MKT | 0.362 | Significant | 3 | 3 | 61.637 | Matched |
CLT | 0.552 | Significant | 2 | 2 | 62.931 | Matched |
SFT | −0.090 | Non-Significant | - | 6 | 5.172 | |
PRS | 0.321 | Non-Significant | - | 5 | 6.034 | |
PPL | 0.164 | Significant | 4 | 4 | 48.706 | Matched |
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Silva, W.D.O.; Morais, D.C.; da Silva, K.G.; Carmona Marques, P. Exploring Influential Factors with Structural Equation Modeling–Artificial Neural Network to Involve Medicine Users in Home Medicine Waste Management and Preventing Pharmacopollution. Sustainability 2023, 15, 10898. https://doi.org/10.3390/su151410898
Silva WDO, Morais DC, da Silva KG, Carmona Marques P. Exploring Influential Factors with Structural Equation Modeling–Artificial Neural Network to Involve Medicine Users in Home Medicine Waste Management and Preventing Pharmacopollution. Sustainability. 2023; 15(14):10898. https://doi.org/10.3390/su151410898
Chicago/Turabian StyleSilva, Wesley Douglas Oliveira, Danielle Costa Morais, Ketylen Gomes da Silva, and Pedro Carmona Marques. 2023. "Exploring Influential Factors with Structural Equation Modeling–Artificial Neural Network to Involve Medicine Users in Home Medicine Waste Management and Preventing Pharmacopollution" Sustainability 15, no. 14: 10898. https://doi.org/10.3390/su151410898