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1 – 10 of over 98000One of the critical reasons for the nonacceptance of additive manufacturing (AM) processes is the lack of understanding and structured knowledge of design for additive…
Abstract
Purpose
One of the critical reasons for the nonacceptance of additive manufacturing (AM) processes is the lack of understanding and structured knowledge of design for additive manufacturing (DfAM). This paper aims to assist designers to select the appropriate AM technology for product development or redesign. Using the suggestion provided by the design assist tool, the user’s design alterations depend on their ability to interpret the suggestion into the design without affecting the design’s primary objective.
Design/methodology/approach
This research reports the development of a tool that evaluates the efficacy values for all seven major standard AM processes by considering design parameters, benchmark standards within the processes and their material efficacies. In this research, the tool provides analytical and visual approaches to suggestion and assistance. Seventeen design parameters and seven benchmarking standards are used to evaluate the proposed product and design quality value. The full factorial design approach has been used to evaluate the DfAM aspects, design quality and design complexity.
Findings
The outcome is evaluated by the product and design quality value, material suit and material-product-design (MPD) value proposed in this work for a comparative assessment of the AM processes for a design. The higher the MPD value, the better the process. The visual aspect of the evaluation uses spider diagrams, which are evaluated analytically to confirm the results’ appropriateness with the proposed methodology.
Originality/value
The data used in the database is assumed to make the study comprehensive. The output aims to help opt for the best process out of the seven AM techniques for better and optimized manufacturing. This, as per the authors’ knowledge, is not available yet.
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Shimiao Jiang, Shuqin Cai, Georges Olle Olle and Zhiyong Qin
More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only once for…
Abstract
Purpose
More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only once for a long time, as while the product review score may highly affected by service factors or be “gently” evaluated. Existing regression or machine learning-based methods suffer from low accuracy when applied to the OCRs of durable products on e-commerce web sites. The purpose of this paper is to propose a new approach for customer segment analysis base on OCRs of durable products.
Design/methodology/approach
The research proposes a two-stage approach that employs latent class analysis (LCA): the feature-mention matrix construction stage and the LCA-based customer segmentation stage. The approach considers reviewers’ mention on product features, and the probability-based LCA method is adopted upon the characteristics of online reviews, to effectively cluster reviewers into specified segmentations.
Findings
The research finding is that, using feature-mention instead of feature-opinion records makes segment analysis more effective. The research also finds that, LCA method can better explain the characteristics of the OCR data of durable products for customer segmentation.
Practical implications
The research proposes a new approach to durable product review mining for customer segmentation analysis. The segment analysis result can provide supports for new product design and development, repositioning of existing products, marketing strategy development and product differentiation.
Originality/value
A new approach for customer segmentation analysis base on OCRs of durable products is proposed.
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Jinze Chai, Liya Wang, Quanlong Shi and Mingxing Wu
Feature fatigue (FF) will lead to negative Word-Of-Mouth (WOM), which damages the brand’s long-term profit and ultimately decreases the manufacturer’s customer equity (CE). It…
Abstract
Purpose
Feature fatigue (FF) will lead to negative Word-Of-Mouth (WOM), which damages the brand’s long-term profit and ultimately decreases the manufacturer’s customer equity (CE). It becomes severer in multi-generation products because of the significant impacts of earlier generation products on the CE of later ones. The purpose of this paper is to alleviate FF, it is imperative for designers to decide what features should be integrated to balance initial revenue and long-term profit so as to maximize CE.
Design/methodology/approach
In this paper, a novel method based on the Norton-Bass model is proposed to alleviate FF of multi-generation products to help designers find optimal feature combination that maximizes CE. The authors take the effects of adding features on product capability and usability into account, and integrate product capability, usability, WOM and earlier-generation product’s effects into the Norton-Bass model to predict the impacts of FF on CE in current product development. A case study of a virtual product is presented to illustrate and validate the proposed method.
Findings
The advantage of the proposed method is highlighted in the cases of large feature number, high-product complexity (low-product usability) and multi-generation products. The experiments show that the earlier generations do affect the later ones from the perspective of maximizing CE. The superiority of the proposed method compared with the traditional way to put all potential features into a product during the product development is demonstrated. And the more features, the larger CE obtained using the proposed model than the one obtained by traditional way.
Originality/value
Although, there are reports attempting to analyze and alleviate FF, most of these studies still suffer the limitations that cannot point out what features should be added to the product with the objective of maximizing CE. In addition, few studies have been carried out to alleviate FF of multi-generation products. A novel method based on the Norton-Bass model and a genetic algorithm is proposed to alleviate FF of multi-generation products to help designers find optimal feature combination that maximizes CE.
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Valentin Gattol, Maria Sääksjärvi, Tripat Gill and Jan Schoormans
Previous research in the context of feature fit has examined the effects of congruence (i.e. more specifically, the extent to which a new feature and the product are similar in…
Abstract
Purpose
Previous research in the context of feature fit has examined the effects of congruence (i.e. more specifically, the extent to which a new feature and the product are similar in the hedonic-utilitarian benefits they provide to consumers). The purpose of this paper is to examine a second dimension of feature fit: complementarity (i.e. the extent to which a new feature is related and contributing to the main functionality of the product).
Design/methodology/approach
The role of feature fit is examined in two experimental studies (n=593) in the context of feature additions, and also for feature deletions.
Findings
The results showed that complementarity adds value to a product as an additional dimension of feature fit beyond congruence, complementarity matters more for a hedonic than for a utilitarian product, and complementarity can compensate for lack of congruence.
Originality/value
For a product developer, adding new features to a product offers an array of choices in terms of what feature(s) to include. Although having a large pool of potential features to choose from is attractive it can also prove problematic, as products may become overly complex and features do not fit well together. The results demonstrate the importance of both congruence and complementarity as predictors of feature fit when features are added to or deleted from products.
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Mingxing Wu, Liya Wang, Ming Li and Huijun Long
This paper aims to propose a novel method to predict and alleviate feature fatigue. Many products now are loaded with an extensive number of features. Adding more features to one…
Abstract
Purpose
This paper aims to propose a novel method to predict and alleviate feature fatigue. Many products now are loaded with an extensive number of features. Adding more features to one product generally makes the product more attractive on the one hand but, on the other hand, may result in increasing difficulty to use the product. This phenomenon is called “feature fatigue”, which will lead to dissatisfaction and negative word-of-mouth (WOM). Feature fatigue will damage the brand’s long-term profit, and ultimately decrease the manufacturer’s customer equity. Thus, a problem of balancing the benefit of increasing “attractiveness” with the cost of decreasing “usability” exists.
Design/methodology/approach
A novel method based on the Bass model is proposed to predict and alleviate feature fatigue. Product capability, usability and WOM effects are integrated into the Bass model to predict the impacts of adding features on customer equity in product development, thus helping designers alleviate feature fatigue. A case study of mobile phone development based on survey data is presented to illustrate and validate the proposed method.
Findings
The results of the case study demonstrate that adding more features indeed increases initial sales; however, adding too many features ultimately decreases customer equity due to usability problems. There is an optimal feature combination a product should include to balance product capability with usability. The proposed method makes a trade-off between initial sales and long-term profits to maximize customer equity.
Originality/value
The proposed method can help designers predict the impacts of adding features on customer equity in the early stages of product development. It can provide decision supports for designers to decide what features should be added to maximize customer equity, thus alleviating feature fatigue.
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Edwin Love and Erica Mina Okada
– The purpose of this study is to propose differential marketing tactics for high-quality products versus low-price products by building on construal level theory.
Abstract
Purpose
The purpose of this study is to propose differential marketing tactics for high-quality products versus low-price products by building on construal level theory.
Design/methodology/approach
Two studies were conducted, one using students and another using data collected from more than 7,000 online auctions.
Findings
When consumers consider high-quality products, they use more abstract mental models, and when they consider low-price products, they use more concrete mental models. Differentiation based on primary features product is more effective for products that are positioned on quality, while differentiation based on the secondary features is more effective for products that are positioned on price. Also, marketing efforts to attract attention are more effective for products positioned on quality than those positioned on price.
Research limitations/implications
This research focused on how consumers use different mental models for considering high-quality versus low-price product offerings but did not examine whether a given segment/consumer uses different models in considering high-quality versus low-price alternatives.
Practical implications
Managers wishing to reinforce a high-quality position should focus on marketing efforts compatible with consumers’ high level construal by enhancing and highlighting the primary features, and drawing consumers’ attention to their product offerings. Managers wishing to reinforce a low-price positioning should focus on marketing efforts that are compatible with consumers’ low level construal by enhancing and highlighting secondary features.
Originality/value
This research makes an important theoretical link between construal theory and brand positioning.
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Raaid Batarfi, Aziz Guergachi and M.I.M. Wahab
Studies have suggested that attributes are dynamic and a life cycle of product and service attributes exists. When an innovative feature is introduced, the feature might attract…
Abstract
Purpose
Studies have suggested that attributes are dynamic and a life cycle of product and service attributes exists. When an innovative feature is introduced, the feature might attract and delight customers. However, with the passage of time the state of the attractiveness of this feature may change, for better or for worse. The purpose of this paper is to provide a detailed model that shows the factors and related sub-factors that affect the life cycle of a feature and thereby explain the changes that may happen to a feature over time.
Design/methodology/approach
This model provide detailed explanations of the direct and indirect factors that affect the states of a feature, the ones that affect the rate of adoption, and the ones that trigger the changes between states. The model uses a current-market product’s feature to discuss the effects of these factors on the life cycle of this feature in detail.
Findings
This paper extends the theory of attractive quality attributes by identified seven states of the feature in its life cycle. These states are as follows: unknown/unimportant state, honey pot state, racing state, required state, standard state, core state, and dead state. This paper also identified eight major factors that affect the transition of the feature from one state to another. These factors include demographic, socioeconomic, behavioural, psychological, geographical, environmental, organisational, and technological factors.
Originality/value
The findings of this paper provide additional evidence that product and service attributes are dynamic. This paper also increases the validity of the attractive quality attributes theory and the factors that affect the state of the feature in its life cycle. The understanding of the state of the feature in its life cycle, and the factors that influence this change, helps not only in the introduction of completely new features but also in knowing when to remove obsolescent ones.
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Sooyong Park, Minseong Kim and Vijayan Sugumaran
A software product line (SPL) captures commonalities and variations (C&V) within a family of systems. Although, feature‐oriented approaches have been proposed for building product…
Abstract
A software product line (SPL) captures commonalities and variations (C&V) within a family of systems. Although, feature‐oriented approaches have been proposed for building product lines, none of them provide a systematic approach for identifying features. This paper proposes a domain analysis method for creating SPL based on scenarios, goals and features. In particular, the paper presents a domain requirements model (DRM) that integrates features with goals and scenarios, and a domain requirements modeling method that uses the DRM. This approach has been applied to the home integration system (HIS) domain to demonstrate its feasibility. This approach makes it possible to systematically identify features and provide the rationale for both features and C&V.
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Mohammad Rahman and M. Manzur Rahman
The purpose of this paper is to examine feature fatigue with products and how to deal with it.
Abstract
Purpose
The purpose of this paper is to examine feature fatigue with products and how to deal with it.
Design/methodology/approach
The paper looks at feature fatigue and how products perform on the market. It then suggests the right and wrong ways of defeating fatigue.
Findings
The paper reveals the problem of too many features in a product leading to fatigue. However, it suggests that, with increasingly demanding consumers and ever‐shortening product life cycles, firms should attempt to defeat feature fatigue not by reducing features, but by improving product designs that reduce the fatigue. Otherwise their products can disappoint consumers and perform poorly on the market, opening an opportunity for competitors.
Originality/value
This paper presents useful information on ways to deal with customer fatigue with product features.
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Chong Wu and Dong Zhang
The purpose of this paper is to rank products by combining sentiment analysis (SA) and multiple attribute decision-making (MADM).
Abstract
Purpose
The purpose of this paper is to rank products by combining sentiment analysis (SA) and multiple attribute decision-making (MADM).
Design/methodology/approach
This research constructs intuitionistic fuzzy (IF)-based sentiment word framework and corresponding computation rules, which aim to calculate the sentiment score of each sentiment phase. Based on intuitionistic fuzzy weighted averaging operator, the authors aggregate the overall performance of each feature for different products. Then, the MADM method can be used, TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) in this paper, to rank product through online reviews.
Findings
The results of the research show the superiority and applicability of proposed method in ranking products with online reviews.
Originality/value
This paper proposes IF-based sentiment word framework and corresponding computation rules, which is a novel idea to express both the sentiment orientations (positive, negative and neutral) and emotional intensities. In addition, this research makes full use of knowledge from both experts and online reviewers. Further, attention degree of each feature is considered in the process of calculating weight of different features, which is rarely seen in current studies. This paper makes full use of SA, fuzzy control theory and MADM theory to handle vague information (online comments) and rank alternative products, which can promote future perspectives and developments.
Details