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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.

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.

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.

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.

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

Although brands have developed mobile applications (apps) to offer consumers new experiences, low app usage numbers indicate the need to develop a systematic, practical evaluation framework for branded app design that specifies concrete design features.

An expert review provides an overview of the design of current branded apps. On the basis of an extensive literature review, this article classifies state-of-the-art design features for branded apps according to a proposed evaluation framework that includes human–computer interaction (HCI)–related and marketing-related evaluation criteria. In an application of these evaluation criteria, the authors evaluate 73 branded apps issued by 11 top fast-moving consumer goods (FMCG) brands.

The expert review identifies strengths and weaknesses that are common to the design of current branded apps. These findings inform the set of design recommendations that this article offers, which includes 14 features common to all types of apps and 9 features specific to particular types of apps.

This research offers practical implications for app designers, who need to address design dimensions contained in the proposed framework including the HCI-related (mobile, social and user experience design features) and marketing-related (branding and customer relationship management design features) to create effective branded apps.

Design elements identified in prior literature remain abstract and do not prescribe a systematic or pragmatic approach to using them in practice. This study takes a multidisciplinary perspective (HCI, marketing and design science) to establish a practical evaluation framework for branded app designs.

Capturing consumers’ notice by differentiating a product from competing brands in attaching an affixed label featuring product claims, as an alternative front-of-package (FOP) cue, has been widely used in fast-moving consumer goods retailing. This paper aims to apply perceived product newness as the basis for examining how affixed labeling, manipulated in terms of design features and message claims, can impact consumer evaluation.

Four between-subjects experiments examined the persuasive impact of the use of affixed labels. In particular, how product evaluation, in response to affixed labeling, varied as a function of its shape (Study 1a), location (Study 1b), the combination of shape and location cues (Study 1c) and the strength of message claims conveyed by such labels (Study 2). Perceived product newness is assessed as a mediator for all studies.

The results show the power of affixed labels in persuasion. Specifically, consumers tend to perceive the item as newer, achieving persuasion, when the affixed label has a distinctive shape or location. Yet, incorporating several unusual design components fails to trigger an elevated result if a singular visual stimulus serves as a cue for an item’s newness. Further, the strength of claims highlighted in an affixed label correlates to positive impact on evaluations.

This study offers an empirically based examination of consumers’ responses to affixed labeling and identifies perceived product newness as a mediator of the observed effect.

A salient, affixed label enables a credible cue for product newness, therefore, driving evaluation.

This paper contributes to understanding the influence on the persuasion of FOP labeling, with salience to retail promotional and sales messaging tactics.

With the continuous development of online shopping, analyzing the competitiveness of products in the fierce market competition is becoming increasingly crucial to position their own product development. However, the information overload brought by the network development makes it getting difficult to obtain the accurate competitiveness information. Therefore, competitiveness analysis research to combine with the perceived helpfulness study needs urgent solution. Furthermore, deviations exist in the three common methods of perceived helpfulness research. Finally, the traditional information fusion analysis only analyzes the advantages and disadvantages of products in competitiveness analysis without taking account of the competitive environment.

This study puts forward a novel prediction model of perceived helpfulness in conjunction of unsupervised learning and sentiment analysis techniques, to conduct the comparison with pros and cons of congeneric products.

This paper adopts Wilcoxon test to demonstrate the significant rectification of our competitiveness analysis to the traditional methods. It is noted that the positive reviews of the products in this study impact more on product word of mouth and competitiveness than negative ones.

To sum up, the results of this study benefit businesses in locating their dynamic market position with competitors in practice and exploring new method for long-term development strategic planning.

The purpose of this study was to find the key face mask features using Kano model in combination with a hierarchical cluster analysis based on customer satisfaction (CS) and preference.

This study used 171 responses collected from a self-administrated online survey with convenience sampling where respondents were asked about 16 different features of face masks.

The study revealed that, among 6 Kano categories, 15 features were categorized as “one dimensional” and only the high price fell under the “reverse” category but all features were not equally weighted by customers. The result also showed viral protection and comfortability were the most desired features by customers regardless of its price and the “color matching” feature can act both as “one dimension” and as “attractive” feature.

This study will help face mask producers to drive their resources towards those features which customers value more by showing how to prioritize features even if they fall under the same category.

This study used customer satisfaction and dissatisfaction index along with an unsupervised machine learning tool to improve features classification based on Kano model. The findings of this study can be used to formulate future research studies.

The purpose of this study is to present a methodology for enhancing the quality and usefulness of online reviews for prospective customers to investigate how this contemporary form of instrumental support can be facilitated to strengthen customer-to-customer support.

This study develops an analytics framework with applications of machine learning models using customer review data from Amazon.com. Linear regression is commonly used for review helpfulness and sales prediction. In this study, Random Forest model is applied because of its strong performance and reliability. To advance the methodology, a custom script in Python is created to generate Partial Dependence Plots for intensive exploration of the dependency interpretations of review helpfulness and sales. The authors also apply K-Means to cluster reviewers and use the results to generate reviewer qualification scores and collective reviewer scores, which are incorporated into the review facilitation process.

The authors find the average helpfulness ratio of the reviewer as the most important determinant of reviewer qualification. The collective reviewer qualification for a product created based on reviewers’ characteristics is found important to customers’ purchase intentions and can be used as a metric for product comparison.

The findings of this study suggest that service improvement efforts can be performed by developing software applications to monitor reviewer qualifications dynamically, bestowing a badge to top quality reviewers, redesigning review sorting interfaces and displaying the consumer rating distribution on the product page, resulting in improved information reliability and consumer trust.

This study adds to the research on customer-to-customer support in the service literature. As customer reviews perform as a contemporary form of instrumental support, the authors validate the determinants of review helpfulness and perform an intensive exploration of its dependency interpretation. Reviewer qualification and the collective reviewer qualification scores are generated as new predictors and incorporated into the helpfulness-based review facilitation services.

Both governments and the food industry are interested in plant-based products. New products are advertised as climate-friendly, with plant-based materials increasingly replacing animal-based content. In Finland, oat milk dominates the plant-based milk market. The authors studied what features young and urban users of plant-based and cow's milk value in oat milk for coffee and how the preferences of the users relate to ethical food-choice motives.

In total, 308 students filled in an e-questionnaire. The survey used best-worst scaling (BWS), a discrete choice approach, to measure the perceived values related to oat milk characteristics. The ethical motives were measured by a version of the Lindeman and Väänänen scale. Also the respondents' diets were asked. Preference clusters were identified and viewed with the ethical food-choice motives and diets.

The respondent group that exclusively used cow's milk attached more value to taste, added nutritional elements, discounts and recommendations by friends. The rest of the respondents attached more value to origin and sustainability-related features of oat milk. In the six-cluster solution, one extreme cluster was valuing taste and the other was valuing sustainability-related issues. All the ethical food-choice motives: ecological welfare, political values and religion were (roughly) the higher the cluster valued sustainability-related items. The respondents eating meat were more likely to belong to the clusters valuing taste than non-meat eaters that belong more likely to clusters valuing sustainability-related features.

Very few earlier studies have explored the heterogeneity of valuations of plant-based products and the products' relationship with ethical food-choice motives.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

Effectiveness of message tactics in social marketing projects often varies across groups of individuals, which suggests the importance of tailoring communication approaches to maximize the success of promotional strategies. This study aims to contribute in this direction by using an innovative approach to promote targeted human papillomavirus vaccination, applying conjoint analysis to understand parental preferences for social media content features.

An online purpose-built quantitative survey was administered to a group of parents meeting eligibility criteria. The survey questions were designed based on inputs from formative qualitative research conducted in a previous phase of the study.

In the overall sample of 285 parents, responses show that image was the most important feature of social media posts overall, followed by source and text. Cluster analysis identified eight segments in the sample based on parental preferences for content features. Significant differences across segments were identified in terms of need for cognition, vaccine hesitancy, parental gender, concerns around side effects, trust in medical providers, information sharing behaviors on social media and information seeking online.

The application of conjoint analysis to promotional content allows to assess which features of the content are most important in persuading different individuals and provide insights on how people process the information, ultimately to inform targeted promotion based on preferences. Conjoint analysis has been widely used in consumer research to explore audience preferences for products or services, but only a few applications of conjoint analysis to the design and testing of promotional content are found in the literature.