Understanding Customer Preference: Outline of a New Approach to Prioritise Sustainability Product Information

Kwok, Sze Yin; Hallstedt, Sophie I.; Boeva, Veselka

doi:10.1007/978-981-15-8131-1_3

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…Actually, a common understanding between developers and customers on sustainability values and features is critical for sustainable production and consumption. In this direction, Kwok et al (2020) noticed a gap between how product sustainability information is perceived by customers and how this impacts the purchasing behavior of customers [23]. Through a specific case study, the authors started discussing the potential of (i) identifying prioritized sustainability attributes using sustainability design space, and (ii) applying machine learning to model customer preferences.…”

Section: Discussionmentioning

confidence: 99%

Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products

Saidani

Kim

Ayadhi

et al. 2021

Volume 6: 33rd International Conference on Design Theory and Methodology (DTM)

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Online product reviews are a valuable resource for product developers to improve the design of their products. Yet, the potential value of customer feedback to improve the sustainability performance of products is still to be exploited. The present paper investigates and analyzes Amazon product reviews to bring new light on the following question: “What sustainable design insights can be identified or interpreted from online product reviews?”. To do so, the top 100 reviews, evenly distributed by star ratings, for three product categories (laptop, printer, cable) are collected, manually annotated, analyzed and interpreted. For each product category, the reviews of two similar products (one with environmental certification and one standard version) are compared and combined to come up with sustainable design solutions. In all, for the six products considered, between 12% and 20% of the reviews mentioned directly or indirectly aspects or attributes that could be exploited to improve the design of these products from a sustainability perspective. Concrete examples of sustainable design leads that could be elicited from product reviews are given and discussed. As such, this contribution provides a baseline for future work willing to automate this process to gain further insights from online product reviews. Notably, the deployment of machine learning tools and the use of natural language processing techniques to do so are discussed as promising lines for future research.

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Section: Discussionmentioning

confidence: 99%

Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products

Saidani

Kim

Ayadhi

et al. 2021

Volume 6: 33rd International Conference on Design Theory and Methodology (DTM)

View full text Add to dashboard Cite

show abstract

“…Then, the starting point in an SC is the search for low prices on raw materials from suppliers, without complex production processes, high energy consumption, or generating high amounts of waste that can result in penalties due to non-compliance with environmental regulations [30]. But additionally, information regarding customers is necessary for understanding quality requirements in products to increase sales and share market [31], [32]. Based on those mentioned above, the following hypothesis is proposed: H 1 .…”

Section: B Business Performance (Bp)mentioning

confidence: 99%

Effect of the Sustainable Supply Chain on Business Performance— The Maquiladora Experience

et al. 2022

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Mexican maquiladoras have a complex and intensive supply chain because they import raw materials from other countries and export their final products. This paper reports a structural equation model with three independent variables associated with supply chain sustainability (Inbound sustainability, Internal sustainability, and Outbound sustainability) and two variables related to external entities integration (Supplier integration and Customer integration). Those variables are related to three dependent variables as sustainability indicators (Business performance, Social performance, and Environmental performance). Variables are related by ten hypotheses that are validated using the partial least squares technique integrated into WarpPLS v.7 software, and information comes from 187 responses to a questionnaire applied to managers and engineers laboring at the Mexican maquiladora industry. The research objective is to know the relationship among variables to increase performance based on sustainable activities. Findings indicate that, given the nature of maquiladora industry, Supplier integration does not moderate the relationship between Inbound sustainability and Internal sustainability. In addition, Customer integration does not moderate the relationship between Internal sustainability and Outbound sustainability. However, the Business performance has a high effect from Social performance, Environmental performance, Internal sustainability and Outbound sustainability.

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“…As of now, few research papers have tried to linking product sustainability features and sentiment analysis from online reviews, and existing works present several shortcomings, notably in terms of automation and sustainable design implication [2]. Moreover, Kwok et al (2020) noticed a gap between how product sustainability information is perceived by customers and how this impacts the purchasing behavior of customers [7]. Also, a recent study performing sentiment analysis on sustainability reporting [8] has highlighted a key issue: different sentiment analysis tools, models, or algorithms (which are described in sub-section 3.2) can produce different outcomes -i.e., polarities (positive, negative, or neutral) -while using the same dataset.…”

Section: Current Gaps and Research Objectivesmentioning

confidence: 99%

“…− Positioning and importance of sustainability-oriented features or properties among critical product features or functions (e.g., mapped along the Kano model [29]); − Customers' preferences to prioritize sustainability product/feature information [7]; − Positive/negative perceptions of sustainable features (e.g., use of plastic, unsafe to a user, like "glass cracking") [2]; − Non-sustainable use cases or patterns of a product supposed to be sustainable (resulting in a wasted potential) [9]; − Automated detection of defects on consumer products [30]; − Wear and tear, or an early failure, of specific components or parts, altering the proper functioning and lifespan of products.…”

Section: Model Deployment To Identify Sustainable Design Leadsmentioning

confidence: 99%

“…Eventually, here are some further examples of how machine learning models can be deployed to better explore and exploit the (sustainable) design space. Kwok et al (2020) started discussing, through a specific case study, the potential of (i) identifying prioritized sustainability attributes using sustainability design space, and (ii) applying machine learning to model customer preferences [7]. In this direction, applying machine learning in the aerospace industry, Bertoni et al (2020) analyzed the potential for automating the value and sustainability assessment in design space exploration [31].…”

Section: Model Deployment To Identify Sustainable Design Leadsmentioning

confidence: 99%

See 1 more Smart Citation

Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges

Saidani

Kim

Yannou

2021

Volume 3A: 47th Design Automation Conference (DAC)

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The increasing number of product reviews posted online is a gold mine for designers to know better about the products they develop, by capturing the voice of customers, and to improve these products accordingly. In the meantime, product design and development have an essential role in creating a more sustainable future. With the recent advance of artificial intelligence techniques in the field of natural language processing, this research aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. In this paper, the opportunities and challenges offered by existing frameworks — including Python libraries, packages, as well as state-of-the-art algorithms like BERT — are discussed, illustrated, and positioned along an ad hoc machine learning process. This contribution discusses the opportunities to reach and the challenges to address for building a machine learning pipeline, in order to get insights from product reviews to design more sustainable products, including the five following stages, from the identification of sustainability-related reviews to the interpretation of sustainable design leads: data collection, data formatting, model training, model evaluation, and model deployment. Examples of sustainable design insights that can be produced out of product review mining and processing are given. Finally, promising lines for future research in the field are provided, including case studies putting in parallel standard products with their sustainable alternatives, to compare the features valued by customers and to generate in fine relevant sustainable design leads.

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Understanding Customer Preference: Outline of a New Approach to Prioritise Sustainability Product Information

Cited by 5 publications

References 10 publications

Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products

Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products

Effect of the Sustainable Supply Chain on Business Performance— The Maquiladora Experience

Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges

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