# A Sustainable Iterative Product Design Method Based on Considering User Needs from Online Reviews

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. A Sustainable, Iterative Approach to Product Design

#### 3.1. Mining User Needs Based on the BTM Model

_{i}

_{,1}and w

_{i}

_{,2}, that is, $b=\{{w}_{i,1},{w}_{i,2}\}$, the two words in each biterm are sampled from the same topic ${\rm Z}$. ${N}_{B}$ Biterms form a set ${\rm B}={\{{b}_{i}\}}_{i=1}^{{N}_{B}}$. In addition, the symmetrical $\theta $ and ${\varphi}_{k}$ in the Dirichlet prior are used, which have single-value hyperparameters $\alpha $ and $\beta $, respectively. The BTM is generated as follows:

- The topic distribution is generated for $\alpha $ with parameters in the Dirichlet prior, $\theta \sim Dir\left(\alpha \right)$;
- For each topic ${\rm K}\in \left[1,k\right]$, the topic distribution is generated for $\beta $ with parameters in the Dirichlet prior, ${\varphi}_{k}\sim \mathrm{Dir}\left(\mathsf{\beta}\right)$;
- For each biterm ${b}_{i}\in {\rm B}$:

#### 3.2. Evaluating User Requirements and Technical Modules using Probabilistic Semantic Term Sets

_{4}and the probability is 0.5. The probability of a product being of good quality is S

_{3}and the probability is 0.25. The probability of the product being of average quality is S

_{2}and the probability is 0.25, and $L\left(p\right)=\{({S}_{2},0.25),\left({S}_{3},0.25\right),({S}_{4},0.5)\}$.

#### 3.3. Calculating User Requirement Weights Using the Improved Probabilistic Semantic DEMATEL Method

- A direct correlation matrix X
^{k}between the indexes was established. The LTS term collection for the correlation between the evaluation index is ${S}^{r}=\left\{{s}_{g}|g=0,1,2,\dots ,e\right\}$. The evaluation indexes are ${C}_{j}\left(j=1,2,\dots ,n\right)$. Expert ${E}_{k}\left(1\le k\le t\right)$ evaluation of the correlation between indexes, according to the collection Sr, was used to establish a direct correlation matrix between indexes.$${X}^{k}=\left[\begin{array}{cccc}0& {s}_{g(12)}^{k}& \cdots & {s}_{g(1n)}^{k}\\ {s}_{g(21)}^{k}& 0& \cdots & {s}_{g(2n)}^{k}\\ \vdots & \vdots & & \vdots \\ {s}_{g(n1)}^{k}& {s}_{g(n2)}^{k}& \cdots & 0\end{array}\right]$$_{i}by expert Ek on C_{j}. It takes a value of 0 if there is no influence. - All expert evaluations of the inter-influence of relationships between the indicators were assembled according to the example in Definition 1 to obtain a direct correlation matrix between the indicators in the form of a probabilistic semantic term set for all experts.$$X=\left[\begin{array}{cccc}0& L{(p)}_{12}& \cdots & L{(p)}_{1n}\\ L{(p)}_{21}& 0& \cdots & L{(p)}_{2n}\\ \vdots & \vdots & & \vdots \\ L{(p)}_{n1}& L{(p)}_{n2}& \cdots & 0\end{array}\right]$$

- 3
- The score function and the degree of deviation were calculated and then converted into a direct correlation matrix after obtaining exact values using the ${\mathsf{\Delta}}^{-1}$ function.$$X=\left[\begin{array}{cccc}0& {\Delta}^{-1}\left(E\left(L{\left(p\right)}_{12}\right),\sigma \left(L{\left(p\right)}_{12}\right)\right)& \cdots & {\Delta}^{-1}\left(E\left(L{\left(p\right)}_{1n}\right),\sigma {\left(L\left(p\right)\right)}_{1n}\right)\\ {\Delta}^{-1}\left(E\left(L{\left(p\right)}_{21}\right),\sigma \left(L{\left(p\right)}_{21}\right)\right)& 0& \cdots & {\Delta}^{-1}\left(E\left(L{\left(p\right)}_{2n}\right),\sigma {\left(L\left(p\right)\right)}_{2n}\right)\\ \vdots & \vdots & & \vdots \\ {\Delta}^{-1}\left(E\left(L{\left(p\right)}_{n1}\right),\sigma \left(L{\left(p\right)}_{n1}\right)\right)& {\Delta}^{-1}\left(E\left(L{\left(p\right)}_{n2}\right),\sigma \left(L{\left(p\right)}_{n2}\right)\right)& \cdots & 0\end{array}\right]$$
- 4
- The directly normalized correlation matrices were then calculated. A common method for normalizing directly correlated matrices is based on the sum of the vector factors of every row of the matrix [32]. Let the normalization coefficient of $X$ be $\lambda $, the normalization coefficient is calculated with the score function and the degree of deviation in the PLTS, the calculation form of $\lambda $ is calculated as follows:$$\lambda =1/\underset{1\le i\le n}{\mathrm{max}}(\underset{j=1}{\overset{n}{\Sigma}}{\Delta}^{-1}(E(L{(P)}_{ij}),\sigma (L{(P)}_{ij}))$$The normalized direct correlation matrix Z is:$${\rm Z}=\lambda X$$
- 5
- The total correlation matrix was calculated using T. According to references [33,34], T is calculated as follows:$$T=\left[\begin{array}{cccc}0& {t}_{12}& \cdots & {t}_{1n}\\ {t}_{21}& 0& \cdots & {t}_{2n}\\ \vdots & \vdots & & \vdots \\ {t}_{n1}& {t}_{n2}& \cdots & 0\end{array}\right]$$$$T=Z{(1-Z)}^{-1}$$
- 6
- Index importance ${\omega}_{j}$ was calculated.

_{j}and the sum of j in column is defined as F

_{j}.

#### 3.4. Sequencing Technology Modules with Consideration of User Requirements Interaction

## 4. Case Studies

#### 4.1. Cases

_{iter}was set to 1000 times by default, according to the sample data. During model training, the results were saved after every 100 iterations. Repeated tests on the reviews of the UAV product showed that when the number of topics, k, was set to 12, the extraction effects were the best; α was set to 10, and β was set to 0.5. Finally, the content of each topic was inferred according to the topic clustering results and ranked by probability from high to low. Each topic’s five high-frequency words and five low-frequency words were screened as keywords. The topic contents focused on product price, function, promotion, users’ needs for the product itself, experience, and services, etc. Figure 3 presents the results for these topics.

- The user topics that needed to be extracted were divided by probability. The repeated user needs information was integrated and divided into six topics, from high to low: quality (${R}_{1}$), battery (${R}_{2}$), shooting (${R}_{3}$), convenient operation (${R}_{4}$), signal (${R}_{5}$), and cost performance (${R}_{6}$), as shown in Figure 4.
- The technical structure modules were divided by analyzing the patents of this UAV brand and the experts’ advice. There were eight categories of technical modules: power module (${C}_{1}$), including the motor; photography module (${C}_{2}$), including photography component and picture transmission signal transmitter; gimbal module (${C}_{3}$), including gimbal motor; control module (${C}_{4}$), including remote control technology; interaction module (${C}_{5}$), including monitor and control panel; flight module (${C}_{6}$), including controller, propeller, and wing; efficiency module (${C}_{7}$), including battery power cable; and carrier module (${C}_{8}$), including the overall weight and volume of the UAV.
- The evaluation and scoring were performed by an expert group. The expert group comprised six experts, including professional product structure designers, brand experts, and product development technicians. In this case, a collection of five-granularity semantic terms was defined: ${S}^{5}=\{{S}_{0}=$ Extremely low, ${S}_{1}=$ low, ${S}_{2}=$ medium, ${S}_{3}=$ high, ${S}_{4}=$ extremely high}. Owing to limited space, the evaluation of only one expert, ${E}_{1}$, is presented herein. The evaluation indexes for user needs and technical structural modules by expert ${E}_{1}$ were converted into the form of a PLTS, as shown in Table 1. Expert ${E}_{1}$ converted the probabilistic semantic term evaluation of the user needs and technical modules into a score function, as shown in Table 2.

#### 4.2. Experimental Results

## 5. Conclusions and Outlook

- The introduction of multi-attribute decision making into sustainable product iterative design, which ensures longer product life cycles, reduces the costs and risks for small and medium-sized manufacturing industries, and minimizes the waste of available resources.
- The improvement of the association function of DEMATEL, which accurately expresses the hesitation and uncertainty of expert evaluations, and solves the problem traditional multi-attribute decision making makes by not considering the interaction between indicators.
- The improvement of the technical method of mining user demand information by combining it with the product structure module, which enables more accurate user demand screening and the identification of product components and modules for improvement using online reviews’ information about user requirements.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Brozzi, R.; Forti, D.; Rauch, E.; Matt, D.T. The Advantages of Industry 4.0 Applications for Sustainability: Results from a Sample of Manufacturing Companies. Sustainability
**2020**, 12, 3647. [Google Scholar] [CrossRef] - Khan, I.; Hou, F.; Le, H.P.; Ali, S.A. Do natural resources, urbanization, and value-adding manufacturing affect environmental quality? Evidence from the top ten manufacturing countries. Resour. Policy
**2021**, 72, 102109. [Google Scholar] [CrossRef] - Sarkar, B.; Ullah, M.; Sarkar, M. Environmental and economic sustainability through innovative green products by remanufacturing. J. Clean. Prod.
**2022**, 332, 129813. [Google Scholar] [CrossRef] - Chekima, B.; Wafa, S.; Igau, O.A.; Chekima, S.; Sondoh, S.L. Examining green consumerism motivational drivers: Does premium price and demographics matter to green purchasing? J. Clean. Prod.
**2016**, 112, 3436–3450. [Google Scholar] [CrossRef] - Mont, O.K. Clarifying the concept of product-service system. J. Clean. Prod.
**2002**, 10, 237–245. [Google Scholar] [CrossRef] - Feng, D.; Lu, C.F.; Jiang, S.F. An Iterative Design Method from Products to Product Service Systems-Combining Acceptability and Sustainability for Manufacturing SMEs. Sustainability
**2022**, 14, 722. [Google Scholar] [CrossRef] - Gartzen, T.; Brambring, F.; Basse, F. Target-Oriented Prototyping in Highly Iterative Product Development. In Proceedings of the 3rd International Conference on Ramp-up Management (ICRM), Aachen, Germany, 22–24 June 2016; pp. 19–23. [Google Scholar]
- Zhou, J.Y.; Yu, M.; Zhao, W.; Zhang, K.; Chen, J.; Guo, X. An Iterative Conceptual Design Process for Modular Product Based on Sustainable Analysis and Creative Template Method. Processes
**2022**, 10, 1095. [Google Scholar] [CrossRef] - Liu, Y.; Gan, W.X.; Zhang, Q. Decision-making mechanism of online retailer based on additional online comments of consumers. J. Retail. Consum. Serv.
**2021**, 59, 102389. [Google Scholar] [CrossRef] - Ji, X.; Gao, Q.; Wang, H. A bilevel-optimization approach to determine product specifications during the early phases of product development: Increase customer value and reduce design risks. Expert Syst. Appl.
**2022**, 188, 116012. [Google Scholar] [CrossRef] - Yang, C.; Wu, L.G.; Tan, K.; Yu, C.Y.; Zhou, Y.L.; Tao, Y.; Song, Y. Online User Review Analysis for Product Evaluation and Improvement. J. Theor. Appl. Electron. Commer. Res.
**2021**, 16, 1598–1611. [Google Scholar] [CrossRef] - Yu, Z.Y.; Zhao, W.; Guo, X.; Hu, H.C.; Fu, C.; Liu, Y. Multi-Indicators Decision for Product Design Solutions: A TOPSIS-MOGA Integrated Model. Processes
**2022**, 10, 303. [Google Scholar] [CrossRef] - Liu, P.D.; Chen, S.M.; Wang, Y.M. Multiattribute group decision making based on intuitionistic fuzzy partitioned Maclaurin symmetric mean operators. Inf. Sci.
**2020**, 512, 830–854. [Google Scholar] [CrossRef] - Ramalingam, S. Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition. Fuzzy Sets Syst.
**2018**, 337, 25–51. [Google Scholar] [CrossRef] - Liu, H.C.; Quan, M.Y.; Shi, H.; Guo, C. An integrated MCDM method for robot selection under interval-valued Pythagorean uncertain linguistic environment. Int. J. Intell. Syst.
**2019**, 34, 188–214. [Google Scholar] [CrossRef] - Zhang, Y.J.; Wei, G.W.; Guo, Y.F.; Wei, C. TODIM method based on cumulative prospect theory for multiple attribute group decision-making under 2-tuple linguistic Pythagorean fuzzy environment. Int. J. Intell. Syst.
**2021**, 36, 2548–2571. [Google Scholar] [CrossRef] - Rodriguez, R.M.; Martinez, L.; Herrera, F. Hesitant Fuzzy Linguistic Term Sets for Decision Making. Ieee Trans. Fuzzy Syst.
**2012**, 20, 109–119. [Google Scholar] [CrossRef] - Wang, H. Extended hesitant fuzzy linguistic term sets and their aggregation in group decision making. Int. J. Comput. Intell. Syst.
**2015**, 8, 14–33. [Google Scholar] - Chen, Z.S.; Chin, K.S.; Li, Y.L.; Yang, Y. Proportional hesitant fuzzy linguistic term set for multiple criteria group decision making. Inf. Sci.
**2016**, 357, 61–87. [Google Scholar] [CrossRef] - Pang, Q.; Wang, H.; Xu, Z.S. Probabilistic linguistic linguistic term sets in multi-attribute group decision making. Inf. Sci.
**2016**, 369, 128–143. [Google Scholar] [CrossRef] - Liao, H.C.; Mi, X.M.; Xu, Z.S. A survey of decision-making methods with probabilistic linguistic information: Bibliometrics, preliminaries, methodologies, applications and future directions. Fuzzy Optim. Decis. Mak.
**2020**, 19, 81–134. [Google Scholar] [CrossRef] - Lei, F.; Wei, G.W.; Gao, H.; Wu, J.; Wei, C. TOPSIS Method for Developing Supplier Selection with Probabilistic Linguistic Information. Int. J. Fuzzy Syst.
**2020**, 22, 749–759. [Google Scholar] [CrossRef] - Liu, P.D.; Teng, F. Probabilistic linguistic TODIM method for selecting products through online product reviews. Inf. Sci.
**2019**, 485, 441–455. [Google Scholar] [CrossRef] - Li, G.; Law, R.; Vu, H.Q.; Rong, J. Discovering the hotel selection preferences of Hong Kong inbound travelers using the Choquet Integral. Tour. Manag.
**2013**, 36, 321–330. [Google Scholar] [CrossRef] - Baykasoglu, A.; Golcuk, I. Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Syst. Appl.
**2017**, 70, 37–51. [Google Scholar] [CrossRef] - Yi, Z.H. Decision-making based on probabilistic linguistic term sets without loss of information. Complex Intell. Syst.
**2022**, 8, 2435–2449. [Google Scholar] [CrossRef] - Wan, S.P. 2-Tuple linguistic hybrid arithmetic aggregation operators and application to multi-attribute group decision making. Knowl.-Based Syst.
**2013**, 45, 31–40. [Google Scholar] [CrossRef] - Nilashi, M.; Zakaria, R.; Ibrahim, O.; Abd Majid, M.Z.; Zin, R.M.; Farahmand, M. MCPCM: A DEMATEL-ANP-Based Multi-criteria Decision-Making Approach to Evaluate the Critical Success Factors in Construction Projects. Arab. J. Sci. Eng.
**2015**, 40, 343–361. [Google Scholar] [CrossRef] - Yan, X.; Guo, J.; Lan, Y.; Cheng, X. A biterm topic model for short texts. In Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 13–17 May 2013; Association for Computing Machinery: New York, NY, USA, 2013; pp. 1445–1456. [Google Scholar]
- Cheng, X.Q.; Yan, X.H.; Lan, Y.Y.; Guo, J.F. BTM: Topic Modeling over Short Texts. IEEE Trans. Knowl. Data Eng.
**2014**, 26, 2928–2941. [Google Scholar] [CrossRef] - Zhang, Y.Z.; Ye, C.M.; Geng, X.L. Probabilistic linguistic term set multi-criteria decision-making method considering psychological behavior of decision makers. J-Glob.
**2020**, 37, 3001–3024. [Google Scholar] - Pan, Q.H.; Liu, X.S.; Bao, H.L.; Su, Y.; He, M.F. Evolution of cooperation through adaptive interaction in a spatial prisoner’s dilemma game. Phys. A-Stat. Mech. Its Appl.
**2018**, 492, 571–581. [Google Scholar] [CrossRef] - Balachandran, A.; Voelker, G.M.; Bahl, P.; Venkat Rangan, P. Characterizing user behavior and network performance in a public wireless LAN. Perform. Eval. Rev.
**2002**, 30, 195–205. [Google Scholar] [CrossRef][Green Version] - Stankovic, M.; Mirza, M.M.; Karabiyik, U. UAV Forensics: DJI Mini 2 Case Study. Drones
**2021**, 5, 49. [Google Scholar] [CrossRef]

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | |
---|---|---|---|---|---|---|---|---|

R_{1} | {S_{3}(0.4), S_{4}(0.6)} | {S_{3}(0.3), S_{4}(0.7)} | {S_{3}(0.5), S_{4}(0.5)} | {S_{3}(0.4), S_{4}(0.6)} | {S_{2}(0.25), S_{3}(0.25), S_{4}(0.5)} | {S_{2}(0.3), S_{3}(0.7)} | {S_{3}(0.4), S_{4}(0.6)} | {S_{3}(0.4), S_{4}(0.6)} |

R_{2} | {S_{1}(1)} | {S_{1}(0.3), S_{2}(0.7)} | {S_{1}(0.5), S_{2}(0.5)} | {S_{4}(1)} | {S_{3}(0.4), S_{4}(0.6)} | {S_{2}(0.4), S_{3}(0.6)} | {S_{3}(0.5), S_{4}(0.5)} | {S_{3}(0.3), S_{4}(0.7)} |

R_{3} | {S_{2}(0.4), S_{3}(0.6)} | {S_{2}(0.25), S_{3}(0.5), S_{4}(0.25)} | {S_{2}(0.3), S_{3}(0.7)} | {S_{2}(0.2), S_{3}(0.8)} | {S_{2}(0.3), S_{3}(0.7)} | {S_{4}(1)} | {S_{2}(0.4), S_{3}(0.6)} | {S_{2}(0.5), S_{2}(0.5)} |

R_{4} | {S_{1}(0.4), S_{2}(0.6)} | {S_{1}(0.5), S_{2}(0.5)} | {S_{1}(0.4), S_{2}(0.6)} | {S_{0}(0.4), S_{1}(0.6)} | {S_{1}(0.2), S_{2}(0.8)} | {S_{3}(0.5), S_{4}(0.5)} | {S_{1}(0.2), S_{2}(0.8)} | {S_{0}(0.25), S_{1}(0.25), S_{2}(0.5)} |

R_{5} | {S_{3}(0.3), S_{4}(0.7)} | {S_{3}(1)} | {S_{0}(0.5), S_{1}(0.5)} | {S_{2}(0.4), S_{3}(0.6)} | {S_{1}(0.2), S_{2}(0.8)} | {S_{1}(0.3), S_{2}(0.7)} | {S_{2}(0.3), S_{3}(0.7)} | {S_{0}(0.2), S_{1}(0.8)} |

R_{6} | {S_{3}(1)} | {S_{3}(0.3), S_{4}(0.7)} | {S_{2}(0.25), S_{3}(0.25), S_{4}(0.5)} | {S_{3}(0.5), S_{4}(0.5)} | {S_{3}(0.4), S_{4}(0.6)} | {S_{3}(0.3), S_{4}(0.7)} | {S_{3}(0.4), S_{4}(0.6)} | {S_{3}(0.5), S_{4}(0.5)} |

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | |
---|---|---|---|---|---|---|---|---|

R_{1} | S_{3.6} | S_{3.7} | S_{3.5} | S_{3.6} | S_{3.25} | S_{2.7} | S_{3.6} | S_{3.6} |

R_{2} | S_{1} | S_{1.7} | S_{1.5} | S_{4} | S_{3.6} | S_{2.6} | S_{3.5} | S_{3.7} |

R_{3} | S_{2.6} | S_{3} | S_{2.7} | S_{2.8} | S_{2.7} | S_{4} | S_{2.6} | S_{2} |

R_{4} | S_{1.6} | S_{1.5} | S_{1.6} | S_{0.6} | S_{1.8} | S_{3.5} | S_{1.8} | S_{1.25} |

R_{5} | S_{3.7} | S_{3} | S_{0.5} | S_{2.6} | S_{1.8} | S_{1.7} | S_{2.7} | S_{0.8} |

R_{6} | S_{3} | S_{3.7} | S_{3.25} | S_{3.5} | S_{3.6} | S_{3.7} | S_{3.6} | S_{3.5} |

**Table 3.**Degree of deviation of expert E

_{1}from the correlation between user requirements and technical modules.

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | |
---|---|---|---|---|---|---|---|---|

R_{1} | 0.3394 | 0.297 | 0.3535 | 0.3394 | 0.1822 | 0.2969 | 0.3394 | 0.3394 |

R_{2} | 0 | 0.297 | 0.3535 | 0 | 0.3394 | 0.3394 | 0.3535 | 0.2969 |

R_{3} | 0.3394 | 0.354 | 0.2969 | 0.2262 | 0.2969 | 0 | 0.3394 | 0.3535 |

R_{4} | 0.3394 | 0.354 | 0.3394 | 0.3394 | 0.2262 | 0.3535 | 0.2262 | 0.5901 |

R_{5} | 0.2969 | 0 | 0.3535 | 0.3394 | 0.2262 | 0.2969 | 0.2969 | 0.2262 |

R_{6} | 0 | 0.2969 | 0.1822 | 0.3535 | 0.3394 | 0.2969 | 0.3394 | 0.3535 |

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | |
---|---|---|---|---|---|---|---|---|

R_{1} | 3.3217 | 3.3217 | 3.1340 | 2.8217 | 3.0656 | 2.3943 | 3.2491 | 3.2491 |

R_{2} | 1.0670 | 1.3943 | 1.1340 | 3.3191 | 3.2491 | 1.6915 | 3.1340 | 3.3943 |

R_{3} | 2.2491 | 2.6382 | 2.3943 | 2.4089 | 2.3943 | 3.5670 | 2.2491 | 1.6340 |

R_{4} | 1.2491 | 1.1340 | 1.3217 | 0.2491 | 0.9089 | 3.1340 | 1.5687 | 0.6367 |

R_{5} | 2.5141 | 2.6971 | 0.1340 | 2.2491 | 1.0687 | 1.3943 | 2.3943 | 0.5687 |

R_{6} | 3.1971 | 3.3943 | 3.0656 | 3.1340 | 3.2491 | 3.3943 | 3.2491 | 3.1340 |

**Table 5.**Expert E

_{1}evaluation of the probabilistic semantics terminology for the impact between user requirements.

R_{1} | R_{2} | R_{3} | R_{4} | R_{5} | R_{6} | |
---|---|---|---|---|---|---|

R_{1} | 0 | {S_{2}(0.75), S_{3}(0.25)} | {S_{3}(0.2), S_{4}(0.8)} | {S_{0}(0.1), S_{1}(0.5), S_{2}(0.4)} | {S_{3}(0.4), S_{4}(0.6)} | {S_{3}(0.75), S_{4}(0.25)} |

R_{2} | {S_{2}(0.25), S_{3}(0.5), S_{4}(0.25)} | 0 | {S_{3}(0.5), S_{4}(0.5)} | {S_{3}(0.75), S_{4}(0.25)} | {S_{0}(0.1), S_{1}(0.6), S_{2}(0.3)} | {S_{1}(0.25), S_{2}(0.5), S_{3}(0.25)} |

R_{3} | {S_{1}(0.2), S_{2}(0.8)} | {S_{3}(0.5), S_{4}(0.5)} | 0 | {S_{2}(0.3), S_{3}(0.7)} | {S_{0}(0.3), S_{1}(0.7)} | {S_{3}(0.25), S_{4}(0.75)} |

R_{4} | {S_{3}(0.5), S_{4}(0.5)} | {S_{2}(0.25), S_{3}(0.75)} | {S_{2}(0.2), S_{3}(0.8)} | 0 | {S_{0}(0.2), S_{1}(0.8)} | {S_{1}(0.25), S_{2}(0.75)} |

R_{5} | {S_{3}(0.75), S_{4}(0.25)} | {S_{1}(0.25), S_{2}(0.75)} | {S_{1}(0.25), S_{2}(0.5), S_{3}(0.25)} | {S_{1}(0.25), S_{2}(0.75)} | 0 | {S_{0}(0.5), S_{1}(0.5)} |

R_{6} | {S_{3}(0.5), S_{4}(0.5)} | {S_{1}(0.3), S_{2}(0.7)} | {S_{0}(0.1), S_{1}(0.9)} | {S_{0}(0.2), S_{1}(0.8)} | {S_{2}(0.1), S_{3}(0.9)} | 0 |

R_{1} | R_{2} | R_{3} | R_{4} | R_{5} | R_{6} | |
---|---|---|---|---|---|---|

R_{1} | 0 | S_{2.25} | S_{3.8} | S_{1.4} | S_{3.6} | S_{3.25} |

R_{2} | S_{3} | 0 | S_{3.5} | S_{3.25} | S_{1.2} | S_{2} |

R_{3} | S_{1.8} | S_{3.5} | 0 | S_{2.7} | S_{0.7} | S_{3.75} |

R_{4} | S_{3.5} | S_{2.75} | S_{2.8} | 0 | S_{0.8} | S_{1.75} |

R_{5} | S_{3.25} | S_{1.75} | S_{2} | S_{1.75} | 0 | S_{0.5} |

R_{6} | S_{3.5} | S_{1.7} | S_{0.9} | S_{0.8} | S_{2.9} | 0 |

R_{1} | R_{2} | R_{3} | R_{4} | R_{5} | R_{6} | |
---|---|---|---|---|---|---|

R_{1} | 0 | 0.2651 | 0.2262 | 0.5288 | 0.3394 | 0.2651 |

R_{2} | 0.3535 | 0 | 0.3535 | 0.2651 | 0.2687 | 0.3125 |

R_{3} | 0.2262 | 0.3535 | 0 | 0.2969 | 0.2969 | 0.2651 |

R_{4} | 0.3535 | 0.2651 | 0.2262 | 0 | 0.2262 | 0.2651 |

R_{5} | 0.2651 | 0.2651 | 0.3125 | 0.2651 | 0 | 0.3535 |

R_{6} | 0.3535 | 0.2969 | 0.1272 | 0.2262 | 0.1272 | 0 |

R_{1} | R_{2} | R_{3} | R_{4} | R_{5} | R_{6} | |
---|---|---|---|---|---|---|

R_{1} | 0.9957 | 0.9824 | 1.0991 | 0.8448 | 0.9006 | 1.0155 |

R_{2} | 1.1216 | 0.8130 | 1.0596 | 0.8897 | 0.7452 | 0.9305 |

R_{3} | 1.0181 | 0.9631 | 0.8097 | 0.8258 | 0.6901 | 0.9623 |

R_{4} | 1.0656 | 0.9035 | 0.9542 | 0.6484 | 0.6748 | 0.8518 |

R_{5} | 0.9151 | 0.7394 | 0.7993 | 0.6598 | 0.5155 | 0.6756 |

R_{6} | 0.9319 | 0.7260 | 0.7290 | 0.6009 | 0.6875 | 0.6078 |

D_{j} | F_{j} | D_{j} + F_{j} | D_{j} − F_{j} | ω_{j} | $\overline{{\mathit{\omega}}_{\mathbf{j}}}$ | |
---|---|---|---|---|---|---|

R_{1} | 5.8380 | 6.0479 | 11.8859 | 0.2099 | 11.8878 | 0.1956 |

R_{2} | 5.5596 | 5.1273 | 10.6868 | −0.4323 | 10.6956 | 0.1760 |

R_{3} | 5.2691 | 5.4509 | 10.7200 | 0.1818 | 10.7216 | 0.1764 |

R_{4} | 5.0983 | 4.4694 | 9.5677 | −0.6290 | 9.5884 | 0.1578 |

R_{5} | 4.3046 | 4.2137 | 8.5184 | −0.0909 | 8.5189 | 0.1402 |

R_{6} | 4.2830 | 5.0435 | 9.3265 | 0.7605 | 9.3575 | 0.1540 |

Index | User Requirements | Product Components | Focus Group Improvement Programme | Sustainable Development Requirement |
---|---|---|---|---|

1 | Battery power, flight duration | Battery modules | Improving battery charging efficiency and battery capacity. | It is recommended to use batteries that do not contain harmful substances, such as lithium iron phosphate, lithium polymer battery, lithium polymer battery, etc |

2 | Wind resistance and stability of drones | Fuselage shells, propellers | Lifting of the overall UAV housing counterweight to ensure flight; widening of the propeller to adjust propeller orientation in the event of wind. | The overall materials of the drone body, such as polypropylene, polyamide and polycarbonate, should meet the specific environmental regulations of developing countries |

3 | Picture clarity | Shooting footage | Professional photographers, with the possibility of configuring higher photographic components, offering individual choice of parts. | Photographic components shall meet the technical requirements of environmental labeling products |

4 | photography module | Fixed flight path data | Professional photographers, with the possibility of configuring higher photographic components, offering individual choice of parts. | This demand is not sustainable development requirements |

5 | Smart Follow | Signal Receiver, Recording Lens | Set receiver module, drone sensor receiver module, intelligent following, more suitable for the elderly and children to go out to monitor and professional record life bloggers. | This demand is not sustainable development requirements |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Wang, Q.; Wang, S.; Fu, S. A Sustainable Iterative Product Design Method Based on Considering User Needs from Online Reviews. *Sustainability* **2023**, *15*, 5950.
https://doi.org/10.3390/su15075950

**AMA Style**

Wang Q, Wang S, Fu S. A Sustainable Iterative Product Design Method Based on Considering User Needs from Online Reviews. *Sustainability*. 2023; 15(7):5950.
https://doi.org/10.3390/su15075950

**Chicago/Turabian Style**

Wang, Qi, Shuo Wang, and Si Fu. 2023. "A Sustainable Iterative Product Design Method Based on Considering User Needs from Online Reviews" *Sustainability* 15, no. 7: 5950.
https://doi.org/10.3390/su15075950