Name: applying an emass customization program as a research tool to evaluate consumer benefits
Uploaded: 2019-09-27T13:30:00
Description: Watch this Scientific Journal Video about Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits at JoVE.com

The data has been modified from Park and Yoo&#8217;s study29. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of KOREA (NRF = 2016S1A5A2A03927809).

This research was exempted from the IRB Review at Ewha Womans University and was assigned protocol number #143-18.
1. Recruitment of Participants
<ol>
	<li>Prepare to conduct an online survey. 
	NOTE: An online survey was conducted using a survey company in South Korea. The research company has the largest consumer panel with high response rates in Korea. Age and gender distributions in the panel reflect the state of the Korean population. The consumer panel has a high ...

<ol>
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Implications of findings 
The findings of this study reveal that consumers&#8217; extrinsic and intrinsic benefits derived from creating a mass customized product help the growth of emotional attachment to the product, creation of positive attitudes toward the customization program, and increased loyalty intentions. The findings on the moderating effects of fashion innovativeness reveal that when compared to consumers in a low fashion innovativeness group, those in a high fashion innovativeness grou...

Mass customization refers to the ability of an e-retailer to tailor products, services, and the transactional environment to individual customers1. Today&rsquo;s consumers are not satisfied with standard products, and many retailers have recognized this. Offering a mass customization option is one method to obtain customer loyalty and competitive advantages2. Mass customization as a marketing tactic allows consumers to create their own products based on particular needs and thus provides individualized products or services3. For example, consumers can not only purchase a pair of shoes that are mass produced, but they can also create a new and unique pair of shoes that are not available on regular retail websites by choosing the color, fabric, and other design components. As a result, consumers can purchase more favorable products, and their satisfaction with the customized product as well as brand loyalty increase4,5.
With increasing use of the internet, the mass customization process has become more rapid and efficient in terms of lowering production time and providing more design options with the same costs. Furthermore, retailers can obtain information regarding what their target customers prefer and thus build strong relationships with them6,7. As such, many industries (i.e., apparel, shoes, cars, and computers) have adopted customization programs. Although mass customization benefits both consumers and retailers, some retailers face challenges8. Therefore, there is a need to examine how consumers perceive benefits and how these benefits influence other shopping responses for long-term success.
Drawing on the hierarchy of effects (HOE) model from persuasion theories9, this study proposes that consumers process information based on cognition-affect-conation sequence. Specifically, this study examines (after creating a mass-customized product) whether perceived consumer benefits (cognition) influence loyalty intentions (conation) through product attachment and the attitude towards a mass customization program (affect). Based on motivation theory10, perceived benefits are divided into extrinsic and intrinsic benefits11.
Extrinsic benefit pertains to a consumer&rsquo;s perceived value derived from using a product12 (thus, close in value to the product quality11), whereas intrinsic benefit indicates a pleasant experience when using a product11. In a mass customization context, extrinsic benefit is associated with the product a consumer creates, and intrinsic benefit is related to the customization experience that satisfies hedonic and experiential needs13,14. Prior research has found that consumers&rsquo; perceived benefits enhance emotional product attachment15 and positive attitudes toward a mass customization program16. Emotional product attachment refers to an emotional tie that consumers connect to a product17, which positively influences attitudes toward the customization program18 and loyalty intentions19. Furthermore, attitudes toward a customization program positively influence loyalty intentions20.
Lastly, this study examines how an individual characteristic (i.e., fashion innovativeness) influences consumer responses differently. Fashion innovativeness refers to the degree to which an individual&rsquo;s innovative tendency influences adoption of a new fashion item21. Research findings show that consumers who desire to avoid conformity (i.e., highly fashion innovative consumers) are motivated to acquire unique products, indicating that mass customization may be an effective tactic to differentiate themselves from others22. Therefore, this study assumes that a greater number of positive responses will be generated for highly fashion innovative consumers.
Based on previous literature reviews, this study addresses the following research hypotheses. H1: Perceived benefits (a: extrinsic benefit, b: intrinsic benefit) of a mass customized product will positively influence emotional product attachment; H2: Perceived benefits (a: extrinsic benefit, b: intrinsic benefit) of a mass customized product will positively influence attitudes toward a mass customization program; H3: Emotional product attachment will positively influence attitudes toward a mass customization program; H4: Emotional product attachment will positively influence loyalty intentions; H5: Attitude toward a mass customization program will positively influence loyalty intentions; and H6: Compared to low fashion innovativeness, high fashion innovators will have more positive responses to (a) perceived benefits, (b) emotional product attachment, (c) attitudes, and (d) behavioral intentions.
To enhance external validity, this study uses an existing mass customization program. Potential participants in South Korea are recruited for this study and are asked to create their own trench coats using a program as if they had actually purchased the product. To explore the participants&rsquo; responses based on their customizing experiences, this study uses an online survey. Participants can access the questionnaire immediately after using the customization program online. After collecting data, the study uses single-group SEM to investigate the effects of consumer benefits on product attachment, attitude, and loyalty intentions. To examine the moderating roles of fashion innovativeness, the study uses LMAs.

<table>
	<tbody>
		<tr>
			<td>SPSS AMOS 22</td>
			<td>IBM Corporation, Data Solution Inc.</td>
			<td></td>
			<td>used for confirmatory factor analyses, structural equation modeling analyses, and latent means analyses</td>
		</tr>
	</tbody>
</table>

applying an emass customization program as a research tool to evaluate consumer benefits

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization through marketing technology. The purpose of this study is to examine how to conduct consumer research using an online survey and analysis of data. This study examines consumers&#39;&#160;perceived benefits while customizing a product as well as emotional product attachment, attitudes toward a customization program, and loyalty intentions in the context of online retailing. In addition, this study investigates how consumer responses are different based on individual characteristics such as fashion innovativeness. An online survey company in South Korea recruited 290 female apparel shoppers who purchased apparel online. To enhance external validity, this study used an existing retail website with a well-established mass customization program. After completing the customization program, participants complete the online questionnaire. Structural equation modeling (SEM) and latent mean analyses (LMAs) are then performed for analyses. This study stresses the importance of testing measurement invariance for mean comparisons. Before the SEM and LMA, this study follows the hierarchy of invariance tests (configural invariance test, metric invariance test, and scalar invariance test), which are not considered by traditional approaches such as ANOVA. These statistical analyses provide applicability of the invariance test procedures and LMA to consumer behaviors. The conclusions of mean differences have integrity and validity because they are guided by a sophisticated statistical procedure to ensure measurement invariance.

Frequency statistics offered characteristics of the sample. A total of 290 female online consumers completed the shopping process using the e-mass customization program. The demographic characteristics of the sample were evenly distributed. By age group, 23.1% were in their twenties, 28.3% in their thirties, 26.6% in their fourties, and 22.1% in their fifties. By marital status, 58.3% were married, while 40% were single. By occupation, 45.2% were office workers, 22.8% were housewives, 10.3% were professionals, 9.3% were ...

Watch this Scientific Journal Video about Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits at JoVE.com

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Presented here is a protocol to examine consumer responses toward mass customization in the context of online retailing. The protocol details the online survey procedure and how to analyze data using structural equation modeling and group differences using latent mean analyses.

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization ...

Our protocol requires participants to experience mass customization before taking a survey, and uses the survey data to test the relationships between consumer responses. The protocol allows consumer data to be applied to structural equation modeling and latent means analysis to obtain sophisticated statistical data and to increase the validity of the research. Our study will benefit research investigating the effects of retail or marketing technology on consumer responses.Although testing several invariance tests such as configural, matrix and scalar invariance may be difficult, we advise patience and following this procedure very carefully to increase the quality of the data. Demonstrating the procedure will be Hiyun Kim, a grade A student from my laboratory. Use an online survey to recruit female consumers who have an online apparel shopping experience.Send an invitation email to qualifying participants that includes information regarding the purpose of the study and an assurance of the confidentiality of the responses. Send guidelines to those survey participants who agree to take part in a survey showing how to create trench coats using the customization program. Participants who agree to take part in the survey start the survey procedure.They receive a link that is connected to the eMass Customization program in an existing shopping website, and are asked to imagine you are well off enough to purchase likable clothing and have to purchase a trench coat to attend an important meeting. You will want to create a unique trench coat. While browsing the internet, you come across the perfect apparel website that has a mass customization program.24 hours after receiving the scenario, activate the survey link so that participants who have finished their trench coat and are ready to take the survey can click on the survey link. In the survey, have the participants upload the screenshot and price of the trench coat they created to the first page of the survey. Then, have the participants complete the online questionnaire regarding the perceived benefits and emotional attachment to the customized product, and attitude toward the customization program, loyalty intentions, and demographic questions.Provide a monetary reward to those participants who complete the survey. When all of the surveys have been completed, save the survey data in an SPSS file containing of all the responses of the survey participants and use the cleaned data to conduct a structural equation modeling analysis. Using a median split, sum and average the scores of six items of fashion innovativeness and calculate the median score of fashion innovativeness.Under the transform menu, click recode into different variables and code one for a low fashion innovative group if the mean score is lower than the median. Double click the fashion innovative group variable to move it to the split cases by field. Then, assign the output file directory location to save the files as Data_low fashion innovativeness.sav and Data_high fashion innovativeness. sav in the assigned directory. To confirm the convergent validity, click select data files, Data_TOTAL.sav.Develop the measurement model based on the research questions to include five latent variables and 17 observed variables. Set each of the variances of the latent variables as one and click calculate estimates. Then, check the fit indices of the measurement model from the results of the single group confirmatory factor analysis, goodness of fit index, adjusted goodness of fit index, normed fit index, Tucker Lewis index, comparative fit index and root mean square error of approximation.In this representative analysis, a single group confirmatory factor analysis was conducted with five latent variables and 17 observed variables. All critical ratios of the factor coefficients were significant, implying that the convergent validity was achieved. The fit indices of a single group structural equation modeling revealed an acceptable fit.Moving from the single group confirmatory factor analysis to multi-group confirmatory factor analysis to cross-validate the five factor measurement model for both groups reveals that configural invariance was achieved and that all of the critical ratios of the factor coefficients were significant. To test the metric invariance, the factor coefficients were constrained to be the same across two groups, and another multi-group confirmatory factor analysis was performed, indicating that a chi-square difference of 14.728 was not significant and that the metric invariance was satisfied. Since the metric invariance model was accepted, scalar invariance was tested.Since the full metric/full scalar invariance model was nested in the full metric invariance model, a chi-square difference test was conducted, demonstrating that a chi-square difference of 11.18 was not significant, and that scalar invariance was satisfied. Given that the configural invariance, metric invariance and scalar invariance were achieved, a latent mean analysis was performed and the means of five latent variables for the high innovative groups were determined to be positive values that were significantly higher than those for the low fashion innovative groups. The mass customization program may seem difficult depending on an individual's perception of the task complexity.Remember to provide enough time for the participants to acclimate to the customization program. To address group differences in the relationships among latent variables, multiple group structural equation modeling can be performed to compare past coefficients across the groups. Most researchers have used the multiple group structural equation modeling for group comparisons.Our study provides another way for multiple group comparisons to be conducted in a social science area.

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