scholarly journals Cyber-Empathic Design: A Data-Driven Framework for Product Design

2017 ◽  
Vol 139 (9) ◽  
Author(s):  
Dipanjan Ghosh ◽  
Andrew Olewnik ◽  
Kemper Lewis ◽  
Junghan Kim ◽  
Arun Lakshmanan
Keyword(s):  
Product Design ◽  
Structural Equation ◽  
Design Space ◽  
Equation Modeling ◽  
Design Decision ◽  
Interaction Data ◽  
Fit Indices ◽  
User Perceptions ◽  
Psychological Constructs ◽  
Product Interaction

A critical task in product design is mapping information from consumer to design space. Currently, this process largely depends on designers identifying and mapping psychological and consumer level factors to engineered attributes. In this way, current methodologies lack provision to test a designer's cognitive reasoning and could introduce bias when mapping from consumer to design space. In addition, current dominant frameworks do not include user–product interaction data in design decision making, nor do they assist designers in understanding why a consumer has a particular perception about a product. This paper proposes a framework—cyber-empathic (CE) design—where user–product interaction data are acquired using embedded sensors. To gain insight into consumer perceptions relative to product features, a network of psychological constructs is utilized. Structural equation modeling (SEM) is used as the parameter estimation and hypothesis testing technique, making the framework falsifiable in nature. To demonstrate effectiveness of the framework, a case study of sensor-integrated shoes is presented, where two models are compared—one survey-only and one using the cyber-empathic framework model. Covariance-based SEM (CB-SEM) is used to estimate the parameters and the fit indices. It is shown that the cyber-empathic framework results in improved fit over a survey-only SEM. This work demonstrates how low-level user–product interaction data can be used to understand and model user perceptions in a way that can support falsifiable design inference.

scholarly journals Cyber-Empathic Design: A Data Driven Framework for Product Design

2016 ◽  
Author(s):  
Dipanjan D. Ghosh ◽  
Junghan Kim ◽  
Andrew Olewnik ◽  
Arun Lakshmanan ◽  
Kemper E. Lewis
Keyword(s):  
Product Design ◽  
Structural Equation ◽  
Design Space ◽  
Equation Modeling ◽  
Design Decision ◽  
Interaction Data ◽  
Model Framework ◽  
Empathic Design ◽  
Product Interaction

One of the critical tasks in product design is to map information from the consumer space to the design space. Currently, this process is largely dependent on the designer to identify and map how psychological and consumer level factors relate to engineered product attributes. In this way current methodologies lack provision to test a designer’s cognitive reasoning and could therefore introduce bias while mapping from consumer to design space. Also, current dominant frameworks do not include user-product interaction data in design decision making and neither do they assist designers in understanding why a consumer has a particular perception about a product. This paper proposes a new framework — Cyber-Empathic Design — where user-product interaction data is acquired via embedded sensors in the products. To understand the motivations behind consumer perceptions, a network of latent constructs is used which forms a causal model framework. Structural Equation Modeling is used as the parameter estimation and hypothesis testing technique making the framework falsifiable in nature. To demonstrate the framework and demonstrate its effectiveness a case study of sensor integrated shoes is presented in this work, where two models are compared — one survey based and using the Cyber-Empathic framework model. It is shown that the Cyber-Empathic framework results in improved fit. The case study also demonstrates the technique to test a designers’ cognitive hypothesis.

A Psychometric Study of the Quality Perception Questionnaire

2012 ◽  
Vol 71 (2) ◽  
pp. 101-106 ◽  
Author(s):  
Raffaele Cioffi† ◽  
Anna Coluccia ◽  
Fabio Ferretti ◽  
Francesca Lorini ◽  
Aristide Saggino ◽  
...  
Keyword(s):  
Structural Equation ◽  
Factor Model ◽  
Equation Modeling ◽  
Fit Indices ◽  
Quality Perception ◽  
Italian Survey ◽  
Confirmatory Factor ◽  
Instrument Measuring ◽  
Hospital Structures

The present paper reexamines the psychometric properties of the Quality Perception Questionnaire (QPQ), an Italian survey instrument measuring patients’ perceptions of the quality of a recent hospital admission experience, in a sample of 4400 patients (Mage = 56.42 years; SD = 19.71 years, 48.8% females). The 14-item survey measures four factors: satisfaction with medical doctors, nursing staff, auxiliary staff, and hospital structures. First, we tested two models using a confirmatory factor analysis (structural equation modeling): a four orthogonal factor and a four oblique factor model. The SEM fit indices and the χ² difference suggested the acceptance of the second model. We then did a simulation using a bootstrap with 1000 replications. Results confirmed the four oblique factor solution. Third, we tested whether there were significant differences with respect to age or sex. The multivariate general linear model showed no significant differences in the factors with respect to sex or age.

scholarly journals Women’s self-care behavior and its relationship with social capital in Yazd, Iran

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hamideh Shiri-Mohammadabad ◽  
Seyed Alireza Afshani
Keyword(s):  
Social Capital ◽  
Structural Equation ◽  
Self Care ◽  
Equation Modeling ◽  
Fit Indices ◽  
Factors Affecting ◽  
Positive Effects ◽  
The Social ◽  
The City ◽  
Care Behavior

Abstract Background Research on factors affecting self-care is scarce. The social factors, in particular, have not been yet investigated in Iran. Therefore, the present study aimed to investigate the relationship between self-care and social capital among women. Methods The participants were 737 women who were living in the marginal, middle and upper areas in the city of Yazd, Iran. Data were collected using a researcher-made self-care questionnaire and Harper’s (Off Natl Stat 11:2019, 2019) Social Capital Scale. The data were analyzed using structural equation modeling by SPSS and Amos v24. Results The results showed that the social capital had significant positive effects on the general self-care behavior of the participants (β = 0.56, p < 0.001). It also had significant positive effects on the self-care behavior of women living in the marginal (β = 0.58), middle (β = 0.49) and upper (β = 0.62) parts of the city (p < 0.001). Besides, the women living in the marginal parts had relatively lower levels of self-care compared to those living in the middle and upper parts of the city. The examination of the fit indices indicated that the model has a good fit (CMIN/DF = 2.087, NFI = 0.921, RMSEA = 0.027, CFI = 0.956, TLI = 0.940, GFI = 0.956, IFI = 0.957). Conclusion The findings of this study demonstrated that social capital has significant positive effects on the general self-care behavior of women. Therefore, improving their self-care can be achieved through promoting their social capital.

Does product design matter? Exploring its influences in consumers’ psychological responses and brand loyalty

2018 ◽  
Vol 31 (3) ◽  
pp. 886-907 ◽  
Author(s):  
Chia-Lin Hsu ◽  
Yen-Chun Chen ◽  
Tai-Ning Yang ◽  
Wei-Ko Lin ◽  
Yi-Hsuan Liu
Keyword(s):  
Product Design ◽  
Brand Loyalty ◽  
Structural Equation ◽  
Brand Management ◽  
Affective Response ◽  
Equation Modeling ◽  
Psychological Responses ◽  
Cognitive Response ◽  
Cross Sectional ◽  
Content Type

Purpose Unique product design is a highlight of sustainable branding. The purpose of this paper is to investigate whether product design affects customers’ psychological responses (i.e. cognitive and affective responses) to smartphones, and, in turn, affects their brand loyalty (i.e. attitudinal and behavioral brand loyalty), further advancing the knowledge of product design and brand management. Design/methodology/approach This work used survey data from 456 Taiwanese with experience using smartphone. Structural equation modeling was employed to test the proposed model and hypotheses. Findings The results indicate that the product design significantly affects both cognitive response and affective response, which, in turn, significantly affect both attitudinal brand loyalty and behavioral brand loyalty. The findings also suggest that the moderating effect of product involvement on the relationship between product design and affective response is statistically significant, although it does not positively and significantly moderate the link between product design and cognitive response. Research limitations/implications This study has two main limitations. First, this study was conducted in the context of smartphones, thus potentially constraining the generalization of the results to other industries. Second, the data in this study were obtained from a cross-sectional design. Practical implications These findings can permit companies to generate more brand loyalty in their customers and guide their management of assets and marketing activities. Originality/value This paper presents new insights into the nature and importance of product design in brand value.

Expressive flexibility and resilience among U.S. military college students: Evaluating the enhancing and suppressing of emotions and resilience

2020 ◽  
Vol 4 (2) ◽  
pp. 187-198
Author(s):  
Vasiliki Georgoulas-Sherry
Keyword(s):  
Structural Equation ◽  
Human Performance ◽  
Factor Model ◽  
Well Being ◽  
Equation Modeling ◽  
Confirmatory Factor Analyses ◽  
Student Sample ◽  
Psychological Constructs ◽  
Resilience Scale ◽  
Military Student

Significant research has confirmed the necessity to better comprehend psychological constructs that are essential in predicting and influencing human performance, in particular, assessing expressive flexibility and resilience. However, limited research has investigated the relationships that exist between these two constructs that are critical protective factors in facilitating the mental health and the well-being of individuals. Through a number of structural equation modeling (SEM) techniques, the current endeavor evaluates this gap to assess the relationship between these two constructs. Utilizing a military student sample from a private U.S. military university (N = 107), participants completed the Resilience Scale for Adults (RSA) and the Flexible Regulation of Emotional Expression (FREE) scale. Correlations matrixes reported positive relationships between expressive flexibility and resilience. Confirmatory factor analyses (CFAs) revealed a bi-factor models of expressive flexibility and resilience. Additional CFAs revealed a two-factor model structure between expressive flexibility and resilience. Implications for future work are offered.

scholarly journals The Impact of Intraclass Correlation on the Effectiveness of Level-Specific Fit Indices in Multilevel Structural Equation Modeling

2016 ◽  
Vol 77 (1) ◽  
pp. 5-31 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Jr-Hung Lin ◽  
Oi-Man Kwok ◽  
Sandra Acosta ◽  
Victor Willson
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Intraclass Correlation ◽  
Monte Carlo Study ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Saturated Model ◽  
Fit Indices ◽  
The Impact ◽  
Multilevel Structural Equation

Several researchers have recommended that level-specific fit indices should be applied to detect the lack of model fit at any level in multilevel structural equation models. Although we concur with their view, we note that these studies did not sufficiently consider the impact of intraclass correlation (ICC) on the performance of level-specific fit indices. Our study proposed to fill this gap in the methodological literature. A Monte Carlo study was conducted to investigate the performance of (a) level-specific fit indices derived by a partially saturated model method (e.g., [Formula: see text] and [Formula: see text]) and (b) [Formula: see text] and [Formula: see text] in terms of their performance in multilevel structural equation models across varying ICCs. The design factors included intraclass correlation (ICC: ICC1 = 0.091 to ICC6 = 0.500), numbers of groups in between-level models (NG: 50, 100, 200, and 1,000), group size (GS: 30, 50, and 100), and type of misspecification (no misspecification, between-level misspecification, and within-level misspecification). Our simulation findings raise a concern regarding the performance of between-level-specific partial saturated fit indices in low ICC conditions: the performances of both [Formula: see text] and [Formula: see text] were more influenced by ICC compared with [Formula: see text] and SRMRB. However, when traditional cutoff values ( RMSEA≤ 0.06; CFI, TLI≥ 0.95; SRMR≤ 0.08) were applied, [Formula: see text] and [Formula: see text] were still able to detect misspecified between-level models even when ICC was as low as 0.091 (ICC1). On the other hand, both [Formula: see text] and [Formula: see text] were not recommended under low ICC conditions.

Structural Equation Modeling: An Alternative to Traditional Validation Techniques

1986 ◽  
Vol 14 (4) ◽  
pp. 345-352
Author(s):  
Margaret E. Bell ◽  
Jean A. Massey
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Statistical Significance ◽  
Equation Modeling ◽  
Fit Indices ◽  
T Values ◽  
Conditions Of Learning ◽  
Nursing Course ◽  
Cardiovascular Nursing

Validation of the sequencing of objectives is an important step in structural design. Prior statistical techniques, such as the reproducibility coefficient, have yielded only summary information. In contrast, structural equation modeling provides both goodness-of-fit indices and effect coefficients for links or paths between time-ordered events, i.e., objectives. Discussed here is the application of structural equation modeling to a set of objectives in a senior-level cardiovascular nursing course. Consistent with the theory-based requirement of structural equation modeling, the objectives were developed using Robert Gagné's conditions of learning. Also discussed is the use of “t” values, which indicate statistical significance of the paths, for testing instructional links in the learning model.

scholarly journals Validating the University Student Engagement Model through Structural Equation Modeling

2018 ◽  
Vol 5 (4) ◽  
pp. 318 ◽  
Author(s):  
Hafsa Mzee Mwita
Keyword(s):  
Structural Equation Modeling ◽  
Student Engagement ◽  
Structural Equation ◽  
Factor Model ◽  
University Student ◽  
Equation Modeling ◽  
Psychological Constructs ◽  
Confirmatory Factor ◽  
The University ◽  
Behavior And Cognition

<p><em>The main purpose </em><em>of this study is to investigate whether emotional, cognitive and behavioral engagements, represents three conceptually and empirically distinct psychological constructs when studied within the same domain. This paper reports part of the findings from a major study entitled “Predictors of Self-Handicapping Behavior among Muslim Students”. Testing for factorial equivalence of scores from a measuring instrument was carried-out through structural equation modeling by using AMOS version 16.</em><em> </em><em>Results of Confirmatory Factor Analysis of responses from 790 undergraduates prove that the SEM three factor model of University Student Engagement (USE) is empirically fit and reliable, which also supports the argument that emotion, behavior and cognition are the student engagement manifestations of an interrelated constellation of academic student engagement. </em></p>

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