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Extant methods of product weakness detection usually depend on time-consuming questionnaire with high artificial involvement, so the efficiency and accuracy are not satisfied. The purpose of this paper is to propose an opinion-aware analytical framework – PRODWeakFinder – to expect to detect product weaknesses through sentiment analysis in an effective way.

PRODWeakFinder detects product weakness by considering both comparative and non-comparative evaluations in online reviews. For comparative evaluation, an aspect-oriented comparison network is built, and the authority is assessed for each node by network analysis. For non-comparative evaluation, sentiment score is calculated through sentiment analysis. The composite score of aspects is calculated by combing the two types of evaluations.

The experiments show that the comparative authority score and the non-comparative sentiment score are not highly correlated. It also shows that PRODWeakFinder outperforms the baseline methods in terms of accuracy.

Semantic-based method such as ontology are expected to be applied to identify the implicit features. Furthermore, besides PageRank, other sophisticated network algorithms such as HITS will be further employed to improve the framework.

The link-based network is more suitable for weakness detection than the weight-based network. PRODWeakFinder shows the potential on reducing overall costs of detecting product weaknesses for companies.

A quicker and more effective way would be possible for weakness detection, enabling to reduce product defects and improve product quality, and thus raising the overall social welfare.

An opinion-aware analytical framework is proposed to sentiment mining of online product reviews, which offer important implications regarding how to detect product weaknesses.

To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this paper aims to explore the distribution regularities of users’ attention and sentiment on product aspects from the temporal perspective of online reviews.

Temporal characteristics of online reviews (purchase time, review time and time intervals between purchase time and review time), similar attributes clustering and attribute-level sentiment computing technologies are used based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of users’ attention and sentiment on product aspects in this paper.

The empirical results show that a power-law distribution can fit users’ attention on product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of users’ sentiment on product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product.

This paper cannot acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms.

This work reveals the distribution regularities of users’ attention and sentiment on product aspects, which is of great significance in assisting decision-making, optimizing review presentation and improving the shopping experience.

Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this paper is to show that the performance of a recommender system can be enhanced by integrating explicit knowledge extracted from product reviews with implicit knowledge extracted from analysis of consumer’s purchase behaviour.

The authors introduce a sentiment and preference-guided strategy for product recommendation by integrating not only explicit, user-generated and sentiment-rich content but also implicit knowledge gleaned from users’ product purchase preferences. Integration of both of these knowledge sources helps to model sentiment over a set of product aspects. The authors show how established dimensionality reduction and feature weighting approaches from text classification can be adopted to weight and select an optimal subset of aspects for recommendation tasks. The authors compare the proposed approach against several baseline methods as well as the state-of-the-art better method, which recommends products that are superior to a query product.

Evaluation results from seven different product categories show that aspect weighting and selection significantly improves state-of-the-art recommendation approaches.

The proposed approach recommends products by analysing user sentiment on product aspects. Therefore, the proposed approach can be used to develop recommender systems that can explain to users why a product is recommended. This is achieved by presenting an analysis of sentiment distribution over individual aspects that describe a given product.

This paper describes a novel approach to integrate consumer purchase behaviour analysis and aspect-level sentiment analysis to enhance recommendation. In particular, the authors introduce the idea of aspect weighting and selection to help users identify better products. Furthermore, the authors demonstrate the practical benefits of this approach on a variety of product categories and compare the approach with the current state-of-the-art approaches.

A focus on product aspects that are most important to target consumers helps communicating product advantage to consumers, both in product design and in marketing. In previous studies, relations between the importance of functional and expressive product aspects and gender, age and social class have been indicated. However, knowledge of the relation of demographic variables to more specific product aspects is more informative for product development and marketing purposes. The purpose of this paper is to provide an insight into the relative importance of product aspects for different consumer groups as important information for product development and marketing. This study seeks to identify relations between gender, age, education and income, and the importance of aesthetic aspects, symbolic aspects, functionalities, ease of use and quality in buying a product.

Consumers (n=247) covering a range in age, gender, education and income, indicated the importance of several product aspects in the purchase decision for ten differentiated product categories. Next to relations between demographic variables and product aspect importances, the influence of social significance of the product category on these relations was assessed.

A number of significant relations between age, gender, education and income and the importance of aesthetic and symbolic aspects, functionalities, ease of use and quality in making a purchase decision have been found.

Relationships of several demographic variables with more specific product aspects – namely aesthetic, symbolic, functionalities, ease of use and quality – have been identified. Knowledge of these relations will help companies to better adjust their products and marketing efforts to fit consumer preferences.

Reports on research based on the results of a survey of design management in the UK mechanical engineering industry. Considers the issue of which aspects of production were considered in the design of products and when. Demonstrates that at the prototype stage production aspects became the most important. This shows that the manufacturability of the product is not considered until after it has been designed. Concludes that the effective and efficient manufacture of the product is not given sufficient attention by mechanical engineering firms. Also investigates the involvement of production personnel in the design process. Finds that production engineering was more extensively involved in the design process the closer it moved towards manufacture. Points to further research which hopes to address this lack by providing practical tools for the application of concurrent engineering.

This study aims to examine the effects of perceived product–brand fit and brand type on consumer evaluations of wearable smart masks’ technological, aesthetic and social attributes and how these affect consumers’ attitudes and intentions to use.

Through an experimental approach, a total of 240 US consumers’ evaluations of smart masks are compared according to perceived product–brand fit (high vs low) and brand type (electronics vs fashion).

The results showed that high perceived product–brand fit increases consumers’ evaluations, while brand type did not significantly affect consumers’ evaluations. Among various attributes, social acceptability had the greatest influence on consumers’ attitude and intention to use. Perceived ease of use, however, positively influenced attitude but negatively influenced intention to use.

As consumers’ interest in smart health-care wearables increases and air pollution is a serious issue across countries, research on wearable smart masks is being facilitated. Smart masks refer to the digitalized, reusable wearable masks that provide protection and health-care functions. However, their market penetration is still limited. To close this gap between smart mask technology and the market, this study examines how perceived fit and brand type can be used to enhance consumer evaluations.

The purpose of this paper is to investigate the influence of technical and management practices of a new product development (NPD) project on the project success in terms of observed value-added time. The authors address the research questions: “How do the product innovation aspects, information process, suppliers integration and project team aspects influence the success of NPD projects in terms of observed value-added time?”

Specific hypotheses are posed based on literature research and semi-structured interviews with project managers used for the pilot study. In total, 40 projects carried out by multinational corporation, concerning NPD, are analysed, comparing product and project features with results obtained in terms of performance indexes usually used in “Lean Production” sector: “waste time”; and “type of wastes”.

This research highlighted that “over-processing” is the most important waste made in the projects when the product concept is Completely New. Great support has been found for the hypothesis that a clear definition of products’ concept can reduce waste time. Unexpected result has been obtained analysing “Information process aspects” category where the more frequent use of support systems (i.e. quality function deployment, computer-integrated manufacturing, computer-aided design, etc.) tended to increase waste time. Important connections have also been found between project team aspects and low waste time.

The identification of sources of waste can help managers to assess their current innovation practices, identify gaps between their current practice and best practice, and define action plans to close those gaps.

In literature, researches exploring the effects that both organizational and managerial factors have on value-added has received only scant attention. This study attempts to bridge this gap, and takes a first step to investigate the role of product innovation aspects, information process, supplier integration and project team aspects in “value-added time” in a NPD project.

The paper aims to present a conceptual framework that helps in incorporating sustainability into software projects, highlights the importance of project sustainability and provides an extensive review of recent relevant contributions across various fields.

The authors carried out a systematic bibliographic search on relevant published materials to analyse links between the two disciplines (sustainability and software projects). Furthermore, content analysis was applied to the final selected publications to identify and classify relevant triple bottom line (TBL) aspects to develop the framework.

The inclusion of TBL-related aspects is the most efficient and effective method used to incorporate sustainability into projects. Most of the relevant contributions in the software literature have focussed on either project product or project process or on one or two dimensions of sustainability rather than the three dimensions of the TBL theory. This study contributes by proposing a conceptual framework that encompasses TBL-related aspects for incorporating sustainability into processes and products of software projects.

Validating the proposed framework empirically could be an interesting research issue. In addition, future works may focus on different types of industries, such as information systems, telecommunications and service sectors, which have seldom been studied in the literature.

Software companies – or other relevant organisations – may use the proposed framework as a measurement tool to evaluate the environmental and social impacts of their current products and project management practices. Consequently, these organisations may pay more attention to incorporating sustainability into their project management practices.

The proposed framework may contribute towards a more sustainable orientation by providing a unique combination of TBL-related aspects that gives academics and practitioners a better understanding of how software projects can be managed sustainably.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

Most of the past studies have considered social and personal factors in relation to counterfeit product purchase intention. However, there is a dearth of research that linked ethical aspects with such kind of product purchase intention. Considering this gap, the purpose of this paper is to investigate the direct as well as indirect effect of ethical aspects on the attitude of consumers’ counterfeit product purchase in the Malaysian market.

A total of 737 questionnaires were distributed in China Town, Low Yat Plaza, as well as a few “pasar malam” (night markets), which yielded 400 completed usable responses. Partial Least Square Smart PLS software and SPSS were utilised in order to analyse the data.

The results revealed that the ethical aspect in term of religiosity, ethical concern, and perception of lawfulness directly and indirectly affect consumers’ behavioural intention to purchase counterfeit products.

It is expected that the study findings will enhance the understanding of marketers as well as policymakers about consumers’ purchase intention of such fake products. Eventually, it will help them to come up with better marketing strategies to purchase counterfeit products and to encourage them to purchase the original product.

This is relatively a pioneer study that examines the effect of ethical aspects of consumers in term of their religiosity, ethical concern, and perception of lawfulness on their attitude towards buying counterfeit products. Additionally, this study examines the mediating role of consumer attitude to purchase counterfeit product between ethical aspects and behavioural intention, which is comparatively new to the existing body of knowledge. Last, but not the least, this research has examined these relationships in a new research context i.e., Malaysian market, which can advance the knowledge about consumer behaviour in the East Asian context.