Frontier Design: A Product Usage Context Method

2006 ◽  
Author(s):  
Matthew G. Green ◽  
Julie S. Linsey ◽  
Carolyn C. Seepersad ◽  
Kristin L. Wood ◽  
Dan J. Jensen
Keyword(s):  
Product Usage ◽  
Usage Context

A Data-Driven Approach to Product Usage Context Identification From Online Customer Reviews

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Dedy Suryadi ◽  
Harrison M. Kim
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Consumer Satisfaction ◽  
Data Driven ◽  
Data Sets ◽  
Product Usage ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Data Driven Approach ◽  
Usage Context

Abstract This paper proposes a data-driven methodology to automatically identify product usage contexts from online customer reviews. Product usage context is one of the factors that affect product design, consumer behavior, and consumer satisfaction. The previous works identify the usage contexts using the survey-based method or subjectively determine them. The proposed methodology, on the other hand, uses machine learning and Natural Language Processing tools to identify and cluster usage contexts from a large volume of customer reviews. Furthermore, aspect sentiment analysis is applied to capture the sentiment toward a particular usage context in a sentence. The methodology is implemented to two data sets of products, i.e., laptop and tablet. The result shows that the methodology is able to capture relevant product usage contexts and cluster bigrams that refer to similar usage context. The aspect sentiment analysis enables the observation of a product’s position with respect to its competitors for a particular usage context. For a product designer, the observation may indicate a requirement to improve the product. It may also indicate a possible market opportunity in a usage context in which most of the current products are perceived negatively by customers. Finally, it is shown that overall rating might not be a strong indicator for representing customer sentiment toward a particular usage context, due to the moderate linear correlation for most of the usage contexts in the case study.

Product “In-Use” Context Identification Using Feature Learning Methods

2016 ◽  
Author(s):  
Dipanjan D. Ghosh ◽  
Andrew Olewnik ◽  
Kemper Lewis
Keyword(s):  
Machine Learning ◽  
Product Design ◽  
Learning Algorithms ◽  
Feature Learning ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Product Usage ◽  
Feature Based ◽  
Product Interaction ◽  
Usage Context

Usage context is considered a critical driving factor for customers’ product choices. In addition, the physical use of a product (i.e., user-product interaction) dictates a number of customer perceptions (e.g. level of comfort, ease-of-use or users’ physical fatigue). In the emerging Internet-of-Things (IoT), this work hypothesizes that it is possible to understand product usage while it is ‘in-use’ by capturing the user-product interaction data. Mining the data and understanding the comfort of the user adds a new dimension to the product design field. There has been tremendous progress in the field of data analytics, but the application in product design is still nascent. In this work, application of ‘feature learning’ methods for the identification of product usage context is demonstrated, where usage context is limited to the activity of the user. Two feature learning methods are applied for a walking activity classification using smartphone accelerometer data. Results are compared with feature-based machine learning algorithms (neural networks and support vector machines), and demonstrate the benefits of using the ‘feature learning’ methods over the feature based machine-learning algorithms.

Effects of Product Usage Context on Consumer Product Preferences

2005 ◽  
Author(s):  
Matthew G. Green ◽  
Junjay Tan ◽  
Julie S. Linsey ◽  
Carolyn C. Seepersad ◽  
Kristin L. Wood
Keyword(s):  
Product Design ◽  
Contextual Information ◽  
Test Methods ◽  
Valuable Insight ◽  
Product Usage ◽  
Functional Families ◽  
Product Preferences ◽  
Future Work ◽  
Usage Context

We present a framework for understanding product usage context and its impact upon customer needs and product preferences. We conduct customer interviews with two sets of representative products from the functional families of “mobile lighting” and “food boiling” products. Customer interviews lead to identification and characterization of distinct product usage contexts. Interactive surveys measuring customer product choice support the hypothesis that customer product preferences differ for each usage context identified. Further analysis shows that attributes of these chosen products are related to factors of the usage context (e.g. mass is related to transportation mode). These results demonstrate that valuable insight for product design is available through an understanding of usage context, and future work will refine and test methods to formally bring contextual information to bear on product design. These capabilities will be especially important for contexts in which needs assessment has traditionally been difficult, such as with latent needs and frontier design environments.

Having champagne without celebration?

2014 ◽  
Vol 48 (11/12) ◽  
pp. 1939-1961 ◽  
Author(s):  
Shin-Shin Chang ◽  
Chung-Chau Chang ◽  
Ya-Lan Chien ◽  
Jung-Hua Chang
Keyword(s):  
Goal Orientation ◽  
Regulatory Focus ◽  
New Products ◽  
Product Evaluations ◽  
Prevention Focus ◽  
Promotion Focus ◽  
Content Type ◽  
Product Usage ◽  
Usage Context ◽  
The Impact

Purpose – This research aims to analyze whether the self-regulatory focus, a consumer variable, moderates the impact of incongruity on consumer evaluations. A congruity or typicality arises when a product (e.g. champagne) is consistently consumed in certain occasions or is used in conjunction with other specific products. This typicality may remind people of the product with regard to specific contexts but may limit the product’s overall versatility. In line with the moderate incongruity effect, there may be an opportunity to extend a product usage to situations associated with moderate incongruity or atypicality. Design/methodology/approach – Study 1 is a 2 (self-regulatory focus: promotion/prevention) × 3 (atypicality of product usage context: typical/moderately atypical/highly atypical) between-subject experimental design. Study 2 replicated Study 1 with a sample of different age, three different champagne usage contexts and a manipulation of self-regulatory focus. Study 3 is a 2 (self-regulatory focus: promotion/prevention) × 3 (atypicality of product usage context: typical/moderately atypical/highly atypical) × 2 (product replicates: red wine/pearl jewelry) mixed design with self-regulatory focus and atypicality as between-subjects factors and product replicates as a within-subject variable. Findings – Promotion-focus consumers’ product evaluations for the moderate incongruity or atypicality are higher than those for congruity and extreme incongruity. The relationship takes an inverted-U shape. Prevention-focus consumers’ product evaluations decrease monotonically as congruity decreases. Moreover, compared with prevention-focus individuals, promotion-focus ones evaluate moderate incongruity more favorably. Research limitations/implications – There are some limitations to this research. First, it only investigates the moderate incongruity effect with regard to product use occasions and complementary products. To increase the external validity of self-regulatory focus as a moderator of incongruity-evaluation relationships, it remains to future research to extend the research setting to products which have been tightly bonded to specific users, locations, seasons or times. Second, although the experimental designs are similar to previous ones, the scenarios are nevertheless imaginary. Therefore, participants’ involvement levels in all manipulated situations, as well as the quality of their answers, remain unknown. Practical implications – First, brand managers should target only promotion-focus customers to obtain the moderate incongruity effect, but should maintain a consistent marketing strategy for prevention-focus customers. Second, because both promotion- and prevention-focus individuals have unfavorable evaluations of extreme incongruity, drastic changes in marketing strategies should be avoided. Third, people from a Western (Eastern) culture exhibit more promotion (prevention) focus orientation. Therefore, the type of culture can serve as an indicator of regulatory orientation. Fourth, a gain-framed appeal is recommended for realizing the moderate incongruity effect from promotion-focus consumers. Finally, promotion-focus (vs prevention-focus) consumers will welcome a moderately nonalignable than alignable product upgrade. Originality/value – Most prior research on goal orientation has found that promotion-focus (vs. prevention-focus) individuals are more inclined to adopt new products, but both types of people are unlikely to purchase new products when the associated risks become salient, while the research related to schema incongruity has suggested that the moderate incongruity effect may not exist when consumers perceive high risks. By combining both schema congruity and self-regulatory focus theories, this research provides a more precise picture of how and why a person’s goal orientation influences the relative salience of risks and benefits with an increase in incongruity.

scholarly journals Automatic Identification of Product Usage Contexts from Online Customer Reviews

2019 ◽  
Vol 1 (1) ◽  
pp. 2507-2516
Author(s):  
Dedy Suryadi ◽  
Harrison Kim
Keyword(s):  
Product Design ◽  
Automatic Identification ◽  
Computing Power ◽  
Product Usage ◽  
Customer Reviews ◽  
Customer Preferences ◽  
Context Factors ◽  
Online Customer Reviews ◽  
Product Description ◽  
Usage Context

AbstractThere are three product design contexts that may significantly affect the design of a product and customer preferences towards product attributes, i.e. customer context, market context, and usage context factors. The conventional methods to gather product usage contexts may be costly and time consuming to conduct. As an alternative, this paper aims to automatically identify product usage contexts from publicly available online customer reviews. The proposed methodology consists of Preprocessing, Word Embedding, and Usage Context Clustering stages. The methodology is applied to identify usage contexts from laptop customer reviews, which results in 16 clusters of usage contexts. Furthermore, analyzing the review sentences explains the separation of “playing games” –which is more related to casual gaming, and “gaming rig” –which implies high computing power requirements. Finally, comparing customer review with manufacturer's product description may reveal a discrepancy to be investigated further by product designer, e.g. a customer suggests a laptop for basic use, although the manufacturer's description describes it for heavy use.

scholarly journals Application of Feature-Learning Methods Toward Product Usage Context Identification and Comfort Prediction

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Dipanjan Ghosh ◽  
Andrew Olewnik ◽  
Kemper Lewis
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Product Design ◽  
Feature Learning ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Product Usage ◽  
Feature Based ◽  
Product Interaction ◽  
Usage Context

Usage context is considered a critical driving factor for customers' product choices. In addition, physical use of a product (i.e., user-product interaction) dictates a number of customer perceptions (e.g., level of comfort). In the emerging internet of things (IoT), this work hypothesizes that it is possible to understand product usage and level of comfort while it is “in-use” by capturing the user-product interaction data. Mining this data to understand both the usage context and the comfort of the user adds new capabilities to product design. There has been tremendous progress in the field of data analytics, but the application in product design is still nascent. In this work, application of feature-learning methods for the identification of product usage context and level of comfort is demonstrated, where usage context is limited to the activity of the user. A novel generic architecture using foundations in convolutional neural network (CNN) is developed and applied to a walking activity classification using smartphone accelerometer data. Results are compared with feature-based machine learning algorithms (neural network and support vector machines (SVM)) and demonstrate the benefits of using the feature-learning methods over the feature-based machine-learning algorithms. To demonstrate the generic nature of the architecture, an application toward comfort level prediction is presented using force sensor data from a sensor-integrated shoe.

Individual Differences in the Name Similarity Effect

2014 ◽  
Vol 35 (2) ◽  
pp. 111-118
Author(s):  
Daniel J. Howard ◽  
Roger A. Kerin
Keyword(s):  
Individual Differences ◽  
Impression Management ◽  
Interactive Effect ◽  
Purchase Intentions ◽  
Brand Attitude ◽  
Self Monitoring ◽  
Similarity Effect ◽  
And Behavior ◽  
Usage Context

The name similarity effect is the tendency to like people, places, and things with names similar to our own. Although many researchers have examined name similarity effects on preferences and behavior, no research to date has examined whether individual differences exist in susceptibility to those effects. This research reports the results of two experiments that examine the role of self-monitoring in moderating name similarity effects. In the first experiment, name similarity effects on brand attitude and purchase intentions were found to be stronger for respondents high, rather than low, in self-monitoring. In the second experiment, the interactive effect observed in the first study was found to be especially true in a public (vs. private) usage context. These findings are consistent with theoretical expectations of name similarity effects as an expression of egotism manifested in the image and impression management concerns of high self-monitors.

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