Product Usage Context Research Articles

Constructing Product Usage Context Knowledge Graph Using User-Generated Content for User-Driven Customization

Abstract User-driven customization is a particular design paradigm where customers act as co-designers to configure products based on their needs. However, due to insufficient product usage experience, customers may design a product incompatible with their environment and needs. Such incompatibility can negatively affect the performance of some customized features or even cause product failure. As a result, customers may hesitate to customize products because additional complexities and uncertainties are perceived. Product usage context (PUC), as all the environment and application factors that affect customer needs and product performance, can be used to facilitate customer co-design in user-driven customization. Identifying individual customer’s PUC can help customers foresee potential design failures, make more holistic design decisions, and be confident with their designs. Against the background, this paper proposes a PUC knowledge graph (PUCKG) construction method using user-generated content (UGC). The proposed method can convert crowdsourced corner cases into structured PUCKG to support personal PUC prediction, summarization, and reasoning. A case study of robot vacuum cleaners is conducted to validate the efficacy of the proposed method.

An ontology-based product usage context modeling method for smart customization

Product usage context (PUC) identification is an effective approach to approximate the complex driver behind heterogeneous customer preference. With the sweeping trend of data-driven smart customization, a large volume of product usage data has allowed designers to understand contextual customer needs (CNs) and enable them to offer highly customized products and services in time. However, as the PUC ontology is not clearly defined, most of the existing PUC models are incomplete, ambiguous and imprecise. Inappropriate use of those models will result in failing to extract knowledge from data. In this paper, an ontology-based context modeling method is proposed, with the aim to help designers understand PUC in a comprehensive manner. A case study of robot vacuum cleaner (RVC) is used as an illustrative example. It is concluded that the proposed method can enable designers quickly establish a well-defined PUC model to support smart customization.

The Impact of Power Distance Belief on Consumers' Brand Preferences

Five lab studies and a field study explore how power distance belief (PDB) influences consumers' preference for mass-market versus niche brands, the underlying mechanisms, and boundary conditions. We find that high (vs. low) PDB consumers are more likely to prefer mass-market over niche brands because they tend to be risk averse, which in turn increases preference for widely used brands. Consequently, the relationship between PDB and brand preferences is mediated by risk aversion. Further, when product usage contexts are risky, low (but not high) PDB consumers' preference for mass-market brands increases, whereas offering an extended money-back guarantee increases high (but not low) PDB consumers' preference for niche brands. In addition, we find that when choosing service providers (versus choosing physical goods), low (but not high) PDB consumers are significantly more likely to prefer mass-market brands over niche brands. We conclude with the implications of these findings for marketers, such as for their segmentation and targeting endeavors. We also provide specific tools that marketers could use, both inside and outside the store, to influence consumers' preference for mass-market versus niche brands.

Operating data-driven inverse design optimization for product usage personalization with an application to wheel loaders

Traditional design requires designers to envisage a product operating environment in order to identify customer needs. Analyzing product usage context by collecting actual product operating data during the product in use empowers new opportunities for the projection of requirement specifications and understanding of use case scenarios. This paper proposes a data-driven inverse design optimization approach to provide decision support to product personalization design. A closed-loop decision-making framework is formulated by integrating forward design and inverse problem solving within a coherent framework of data-driven analysis. An application to the transmission system personalization design of wheel loaders is presented to demonstrate how personalized product usage contexts are identified through inverse analysis of product operating data under different operating conditions. A particle swarm optimization (PSO) algorithm incorporated with Simulink simulation is developed to solve the multi-objective optimization of power performance and fuel economy for wheel loaders.

What women want? How contextual product displays influence women’s online shopping behavior

A lack of touch and feel options inhibits online purchases; to enhance online product presentations, this study suggests a potential influence of rich contextual product displays, relative to plain white backgrounds. The results of five studies reveal that the product usage context influences purchase intentions among female customers. Women and men differ in their decision-making processes and evaluate different attributes and benefits prior to purchase. Displaying a product in a rich contextual setting appears to enhance women’s perceptions of emotional value, which heightens their purchase intentions. This article also explicates the additive effect of including contextual elements and confirms the robustness of these effects across product categories and price changes. These findings suggest various notable implications for academics and practitioners.

Shopping online? The role of imagination and gender

Purpose This study aims to examine gender differences in the impact of imagining product use on purchase decisions. The authors argue that while imagination can enhance purchase intention for female consumers, it can be detrimental to male consumers. This study explores the conditions under which imagination can be turned into a positive device for male consumers. Design/methodology/approach Three experimental studies were conducted. The first two studies illustrate the differential effects of imagination on males vs females. Given the negative effect found among males, the third study focused exclusively on male consumers to identify conditions under which the negative impact of imagination on these consumers can be alleviated. Findings Studies 1 and 2 show that while an imagination tactic has positive or no effect on female consumers, a generic imagination request lowers male consumers’ purchase intention. Focusing on potential ways of alleviating this negative effect, Study 3 shows that for males without prior brand ownership experience, imagining product use in a less-typical context can increase purchase intention. Research limitations/implications The results provide evidence that gender impacts the effectiveness of imagination in improving product evaluation. Furthermore, the context of imagination and previous brand experience can be used together to determine how male consumers respond to imagination. Practical implications The study’s findings warn against the blind use of imagination tactics. Instead, retailers need to customize imagination tactics based on gender, previous brand experience and product usage context. Originality/value To the best of the authors’ knowledge, this is one of the first papers to examine the impact of gender on the influence of imagination on product evaluation.

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

AbstractThis 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.

Automatic Identification of Product Usage Contexts from Online Customer Reviews

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.

How is the green trend affecting formulations for the cleaning industry?

Traditional design requires designers to envisage a product operating environment in order to identify customer needs. Analyzing product usage context by collecting actual product operating data during the product in use empowers new opportunities for the projection of requirement specifications and understanding of use case scenarios. This paper proposes a data-driven inverse design optimization approach to provide decision support to product personalization design. A closed-loop decision-making framework is formulated by integrating forward design and inverse problem solving within a coherent framework of data-driven analysis. An application to the transmission system personalization design of wheel loaders is presented to demonstrate how personalized product usage contexts are identified through inverse analysis of product operating data under different operating conditions. A particle swarm optimization (PSO) algorithm incorporated with Simulink simulation is developed to solve the multi-objective optimization of power performance and fuel economy for wheel loaders.

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

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.

User Perception on Mixed Attribute of Intelligent Product According to Product Usage Context

User Perception on Mixed Attribute of Intelligent Product According to Product Usage Context

Creating When You Have Less: The Impact of Resource Scarcity on Product Use Creativity

This research examines how a general sense of resource availability influences consumers’ product use creativity. The authors propose and demonstrate that the salience of resource scarcity versus abundance enhances the novelty of product use solutions in independent consumption environments. An investigation of the underlying process finds that scarcity salience activates a constraint mindset that persists and manifests itself through reduced functional fixedness in subsequent product usage contexts (i.e., makes consumers think beyond the traditional functionality of a given product), consequently enhancing product use creativity. This work advances the extant creativity literature, currently limited to examining the effects of context-specific resource constraints, by establishing a context-independent linkage between resource availability and product use creativity. Furthermore, this research contributes to the scarcity literature, which has primarily focused on investigating the quantity and frequency of consumption, by examining the impact of scarcity on the quality of consumption solutions.

Having champagne without celebration?

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.

Probabilistic Graphical Modeling of Use Stage Energy Consumption: A Lightweight Vehicle Example1

Although energy consumption during product use can lead to significant environmental impacts, the relationship between a product's usage context and its environmental performance is rarely considered in design evaluations. Traditional analyses rely on broad, average usage conditions and do not differentiate between contexts for which design decisions are highly beneficial and contexts for which the same decision may offer limited benefits or even penalties in terms of environmental performance. In contrast, probabilistic graphical models (PGMs) provide the capability of modeling usage contexts as variable factors. This research demonstrates a method for representing the usage context as a PGM and illustrates it with a lightweight vehicle design example. Factors such as driver behavior, alternative driving schedules, and residential density are connected by conditional probability distributions derived from publicly available data sources. Unique scenarios are then defined as sets of conditions on these factors to provide insight into sources of variability in lifetime energy use. The vehicle example demonstrates that implementation of realistic usage scenarios via a PGM can provide a much higher fidelity investigation of use stage energy savings than commonly found in the literature and that, even in the case of a universally beneficial design decisions, distinct scenarios can have significantly different implications for the effectiveness of lightweight vehicle designs.

Substitution in Use and the Role of Usage Context in Product Category Structures

The relationship between the substitution in use approach to product-market structure and cognitive accounts of product category structures is investigated in terms of the role of product usage context. Studies 1 and 2 show that distinctive usages predict similarity judgments, whereas usage versatility predicts prototypicality. The results of a third study suggest that individual usage contexts are associated with subcategories that involve restructuring of the overall category and, as such, guide context-specific product recall. Conceptual and managerial implications of the findings are discussed.