Female bike riders’ clothing needs with the incorporation of wearable technology: Design and evaluation of wearers’ perception of smart clothing within the cradle-to-cradle design framework

Lee, Kyung
Major Professor
Young-A Lee
Committee Member
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Art and Design

The overall purpose of this study was to increase a general understanding of the mechanisms that determine how female bike riders’ clothing needs are met through the use of smart clothing, by empirically testing new smart clothing designs that incorporate wearable devices developed by the researcher within the cradle-to-cradle design framework. The specific objectives of this study were: (1) to identify important design criteria of bike wear for female bike riders under the frame of consumers’ functional-expressive-aesthetic needs along with their needs and desires for wearable technology; (2) to design and develop smart clothing for female bike riders in accordance with identified female bike riders’ expectations and needs within the cradle-to-cradle design framework; (3) to evaluate female bike riders’ perceived needs satisfaction and social acceptability of the proposed smart clothing by examining the relationships among perceived satisfaction of functional, expressive and aesthetic needs, and perceived social acceptability; and (4) to evaluate the marketability of proposed smart clothing by testing the hypothetical research model incorporating the following variables: perceived satisfaction of functional, expressive and aesthetic needs, perceived social acceptability, attitude, and purchase intention.

The findings of this study were based on data collected from two different online surveys (Study 1 and Study 2) as well as a proposed smart bike jacket that included transformable functions developed by this researcher and used for Study 2 survey. For this study, females in large cities worldwide were considered a key segment of the population for studying female bike riders’ clothing needs incorporating wearable technologies. Using a purposeful and convenience sampling approach, the Study 1 sample was recruited from females aged 18 years and over living in the U.S. with bike riding experience, and were members of the “Transportation Alternative,” a non-profit organization dedicated to bike riders in New York City; 136 usable responses were obtained and used for the data analysis. The Study 2 used a nation-wide convenience sampling approach that involved females aged 18 years and over living in the U.S. with bike riding experiences; 488 usable responses were obtained from the Amazon Mechanical Turk and used for the data analysis.

The instrumentation for the study incorporated two main online questionnaires, including both close-ended and open-ended questions, and proposed smart bike wear developed by the researcher. Based on female bike riders’ identified bike wear needs from Study 1 survey, the researcher incorporated appropriate design components into smart clothing design incorporating a wearable device for addressing survey participants’ special needs. The developed smart bike wear in this stage was used for Study 2 survey.

Two self-administered questionnaires for the Study 1 and Study 2 were developed using multiple-item measurements that were validated and determined as reliable from previous studies and open-ended questions. In Study 2 survey, the participants were expected to respond to each question after watching a short video clip demonstrating the features of the proposed smart clothing, as a tool of stimuli to measure perception of respondents toward the proposed smart clothing.

The Statistical Package for the Social Sciences (SPSS Version 21.0) software and AMOS Version 21.0 were employed to conduct statistical analyses and model testing. Demographic data were analyzed using descriptive statistics for both the 136 respondents for the Study 1 and the 488 respondents for the Study 2. An initial series of confirmatory factor analysis (CFA) was used to test validity and reliability of constructs in the measurement model for both the Study 1 and Study 2. In the Study 2, the structural equation model for theoretical predictors of purchase intention was tested. To analyze the data collected from open-ended questions, the researcher used a word clouds analysis, a popular content analysis method for text-based data.

The findings were:

1. The Study 1 identified 25 valid functional-expressive-aesthetic-price (FEAP) needs measurement items that were applicable for consideration when designing smart clothing from the perspective of female bike riders. Functional design characteristics, especially those contributing to comfort, protection, and ventilation, were the most important needs of respondents. Expressive and aesthetic design characteristics were also shown to be important needs to be considered when designing female bike riders’ clothing, but less important than functional needs.

2. Smart clothing that embedded multiple transformable features was favored by most respondents, and a jacket that could be changed into a bag was the most commonly desired transformable smart clothing feature. The findings from Study 1 survey guided this researcher to design a smart jacket transformable into a bag capable of storing detachable pieces, with essential design characteristics that incorporated identified consumers’ bike wear design needs. The processes of product design and development were guided by the frame of the cradle-to-cradle (C2C) design process which fully considered sustainability practices. A female bike riders’ transformable jacket incorporating a LED signal lighting device was developed in response to participants’ needs and desires that were identified in the Study 1.

3. The Study 2 identified 41 valid functional-expressive-aesthetics (FEA), social acceptability, attitude, and purchase intention related measurement items to examine respondents’ perceived satisfaction level for the proposed smart jacket. The proposed smart bike wear fulfilled the functional needs across all elements for smart bike wear. Especially, the results proved that ventilation features (e.g., mesh trimmed around armpits, detachable sleeves), as well as enhanced visibility (e.g., incorporated reflective trimming and a LED lighting device) of the proposed smart clothing satisfied consumers’ special smart clothing needs when bike riding. The proposed smart jacket also fulfilled the aesthetic and expressive needs of the respondents. Regarding social acceptance of the proposed smart clothing, most of the respondents said that what was presented in smart clothing was socially acceptable. A few responses suggested there were privacy concerns about wearable devices that use smart phone applications to collect data.

4. The hypothesized model consisted of six latent variables (functional design characteristics, expressive design characteristics, aesthetic design characteristics, social acceptability, attitudes, and purchase intention). The results of structural equation modeling (SEM) for the hypothesized model revealed a good model fit. All five structural paths in the model were statistically significant. As expected from hypothesis (H) 1, perceived satisfaction of functional design characteristics significantly and positively affected attitude; for H2, perceived satisfaction of expressive design characteristics significantly and positively affected attitude; for H3, perceived satisfaction of aesthetic design characteristics significantly and positively affected attitude; for H4, perceived social acceptability significantly and positively affected attitude; and for H5, attitude toward purchasing smart clothing significantly and positively affected purchasing intention. The statistical testing results confirm that the level of perceived satisfaction of functional, expressive, and aesthetic design characteristics as well as perceived social acceptability influences the creation of positive attitudes toward the use of smart clothing, leading to positive purchase intentions for smart clothing.

This research significantly contributes to the literature by providing insight into the inadequately researched area of smart clothing for female bike riders. It is the first study conducted that investigated female bike riders’ special needs and social acceptability of smart clothing under the C2C design framework. Its holistic approach to the analysis of data collected through various research stages (needs identification-smart clothing design-design evaluation) uncovered previously unidentified issues surrounding female bike riders’ smart clothing needs, revealing numerous areas where future research is needed, and providing vital information for both the apparel industry and academia.

Limitations of this study were presented, and implications and recommendations for future studies and for practice were also suggested.