# 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

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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 |

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© 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