Hyper-personalization – fashion sustainability through digital clienteling

Geetika Jain (Faculty of Management, Uttar Pradesh Technical University, Lucknow, India)
Sapna Rakesh (IMS, Ghaziabad, Noida, India)
Mohd Kamalun Nabi (Department of Commerce and Business Studies, Jamia Millia Islamia, New Delhi, India)
K.R. Chaturvedi (School of Management, Krishna Institute of Engineering and Technology, Ghaziabad, India)

Research Journal of Textile and Apparel

ISSN: 1560-6074

Publication date: 3 December 2018

Abstract

Purpose

This study aims to find the model fit to understand the consumer behavior in context to the hyper-personalization through digital clienteling by using structural equation modeling. The traditional method of customer passive observance has been transformed to dominance, where, the fundamental challenge for companies is to understand consumer behavior, work on cost-efficiency and implement sustainable innovation.

Design/methodology/approach

To investigate this emerging issue, this study aims to find the model fit via applying “Technology Acceptance Model” (TAM) and “Theory of Reasoned Action” (TRA) in context to the hyper-personalization through digital clienteling with special reference to women ethnic fashion wear.

Findings

The study findings depict the perceived ease of use (PEOU) and perceived usefulness (PU) of technology, attitude toward clienteling and subjective norm toward customization impact on customer intensions. The findings posited that perceived usefulness is having the strong relationship with purchase intention as compare to other variables. So, the analysis postulated that customer considered hyper-personalization is having perceived usefulness for customer and it also helps customer in getting the information about the product on the Web page.

Research limitations/implications

Because of lack of availability of resources, a specified sampling method has been used for this study. A new research, which will cover the fashion apparel from all the categories with a detailed study from the branded and non-branded point of view, will provide better description on this topic.

Practical implications

By having personalized Web page through big data analytics, customer will have positive experience and positive association with the company. The other parameters also play an important role toward the customer behavioral intention. The current study approaches new way of understanding the participative management of the personalization and tool to guide the work of strategy professionals and management of fashion e-commerce sector internationally and even in the other sectors also.

Social implications

Because of advancement of technology, the usage of online media is increasing day by day and this change is having high impact on the society, though we can innovate in any field or industry. Hyper-personalization has an impact on the online consumer buying behavior, which will affect the methods of searching information for consumers.

Originality/value

This new area of research is having large scope of future research from the fashion industry point of view. This paper is working as one of the element in the area of hyper-personalization through digital clienteling to gain sustainable results in the fashion industry.

Keywords

Citation

Jain, G., Rakesh, S., Kamalun Nabi, M. and Chaturvedi, K. (2018), "Hyper-personalization – fashion sustainability through digital clienteling", Research Journal of Textile and Apparel, Vol. 22 No. 4, pp. 320-334. https://doi.org/10.1108/RJTA-02-2018-0017

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


Introduction

In anticipation of creating customer positive association, online retail companies are trying their hands on digital transformation, where touching every line of customer shopping story is the need of the hour. Achieving fashion sustainability through digital clienteling is the new buzz world in the fashion industry. Understanding the requirement of customers in a traditional clienteling approach is feasible and entirely confined. But, in the digital age, where the customer is exposed to new platform, online websites and social media are working as the mode of communication. As per Deloitte perspectives (Lay, 2018), companies are trying their level to bridge the digital divide where brand heritage and identity is at risk and new digital path is very delicate in nature. Providing products and services to the customers are no longer work as an online shopping experience where traditional method is misaligned in the current environment and can be a threat to brand reputation. So, it’s very important for companies to have sustainable business strategy in new platform which will create a balance between growth aspirations and future operational plans with existing strategic risk. Retailers need to understand all aspects of this journey where customer is looking for “personal factor” or “personal interaction”. They need to understand and study the past record of product shopping, searched, added to cart and shopping wish list, which can enhance, resemble and complement their past purchase by using the big data. It’s the need of the hour to understand the customer from all the spheres by foraying into digital transformation. To make online shopping as “feel good” experience, companies need to conquer clinteling and provide unique experience, retailer need to communicate to customer by using real-time through all the channels and provide personalized communication using lifestyle-relevant data.

According to Retail Info Systems (Inniss and Ryan, 2015), customers do not think about the channel and platform while searching for information about the product. So, companies need to understand the next step and way beyond to the omnichannel presence. Having a presence online would not fulfill the purpose of customer engagement; hence, companies need to understand the complex shopping journey of customers. To understand the customer experience and to provide unified customer experience, companies should wear the customer shoe and find the pinching area. Companies need to curate products which can work as a catalyst in the story of a customer’s journey based on their lifestyle, choices and past shopping history. It makes more sense to customer where the products will be offered on the basis of data given by customer inputs, search engine records, virtual communication data and behavioral data. Those were days, where companies used to have push strategy either by providing offers and discounts. But, these days, companies need to address the complex behavior of customers by understanding the pain area of customers and alleviating their pain by using technology provided solutions. An online shopper or a website visitor would never ever provide the personal information by filing long survey questionnaires, feedback forms, feedback mails and return forms. According to Fujitsu America (2011) report, customers are having access to information through various social networking websites like Facebook, Twitter and blogs. Customer’s data which have been collected by companies is having no relevance in the current scenario because of fast-moving conversations. Digital clienteling provides better customer information, proactive customer responses and cross-sell opportunities. The companies need to take an initiative to foray into a digital clienteling, where building relationship with customers in an automated nature will exist and flourish it by providing consistent solutions across various touch points.

Digital clienteling – one size does not fit all

Going one step ahead is the requirement where customer expects the high level of transparency from the retailer. According to Retail Info Systems (Inniss and Ryan, 2015), companies need to deliver dynamic digital clienteling solutions which will provide the consistent solutions to customer by all means through omnichannel like providing access to the product assortment data, return policy, tracking record of product delivery, premium shipping services and detail on the availability of product based on price. Engagement with the customers should be through all the channels even before, during and after the shopping experience. To perform better on the digital platform, companies need to close all the gaps between the customer service expectation and delivered customer service. So, when a fashion brand is going digital then it does not mean that merely they need to provide services online. They need to re-structure, re-imagine and re-design the whole world of digital shopping through hyper-personalized way. Digital transformation is the new buzz world in the fashion industry. Though fashion industry specifically to women ethnic wear has major customer base from the offline customer through physical channels, but online customer base is also growing at a fast pace through omnichannel. By using omnichannel customer experience, companies try to touch every step of emotional and virtual aspects of customer experience and shopping journey. Clienteling is defined as an approach which primarily focuses on one-to-one marketing to have positive association and healthy customer loyalty by having personalized customer communication. The main feature of clienteling is that it will be operated through sales-person only and the interaction will be one-on-one basis. As per Forbes article (Published in 2017), Apple store and Nordstrom are applying best practices of clienteling with digital presence. Both the companies have worked on the three major aspects of clienteling, i.e. information, personalized information and seamless checkout.

Hyper-personalization

According to Subramanyan (2014), Hyper-personalization is defined as the use of big data to provide more specialized and personalized products, services and information to the targeted segment. With the help of hyper-personalization, companies can create an authentic customer experience online based on customer requirement. With the advent of technology, customers are trying to curate their surrounding according to their liking, interests and beliefs. Customers want to control the way of accessing the information. Companies can use this concept and provide the information as per the customer requirements. Hyper-personalization works as a tool to marketer to provide the personalized information about the customers. Hyper-personalization has three major focus areas like social listening, data analysis and content. According to Simon (2014), Netflix has 60 per cent got of rental customer business with the help of hyper-personalization data application only. With the help of online data collection, companies can track their customer previous purchase records, demographic information, advertisement clicks and subscriptions of emails.

Information

Information plays a crucial role in the implementation of clienteling, where information plays a role of a catalyst in providing detailed information of customer to sales associates. With the help of big data, sales associates will be having access to the information related to customer clothing size, color preferences, their likes and dislikes, purchase history records and purchase amount. As per Forbes article (Published in 2017), detailed information helps sales associate in driving customer engagement and also helps in providing personalized recommendation and assistance through 360-degree view. Because of information age, the physical clienteling has transformed into digital clienteling where the personalized services provided to customers will be purely based on their big data analytics. The big data analytics will be based on customer past search record through various search engines, search on mobile applications and website visitor information. The big data will play a crucial role in this big picture by having detailed information on customer wants, needs buying history, purchase pattern, purchase amount, online search pattern, behavior. Online retailer can use this information for providing the hyper-personalized products and services to customer in fashion industry with special reference to women ethnic wear.

Communication

Communication has its own relevance in the digital clienteling scenario. However, sending communication mails to customer on frequent basis and informing customers about the ongoing offers and discounts will not solve the purpose. Companies need to understand the requirement of the customer and personalized such communication mails. The personalized mail opens the message which will contain picture or an image of recommended products to customer based on customer’s previous purchase and search history. The message will have the feature to shop directly with personalized product assortment to increase customer experience and positive customer engagement.

Seamless checkout

Retailers can think of right technology to provide safe and seamless checkout process to customers. By using the data of previous mode of payment, shipping and billing address and delivery preference, companies can dramatically improve the customer shopping experience. Providing transparency in various features like tracking status of the product and return processing of the product can create loyalty and positive association between retailer and shopper. Amazon Go is an existing example of seamless checkout of physical retail store where customer can have the grab-and-go shopping experience in place of any sales associate assistance. Online retailers need to provide the same shopping experience online and have seamless checkout by using big data.

Women ethnic wear

Urbanization in India is growing in a fast manner where it is estimated that within another 20 years, the Indian population living in cities and towns and municipalities would increase by 300 million. With the rapid increase in urbanization, the rise of selecting and looking forward to different styles would be noticeable. To maintain the new social status earned, the population shifting the base to urban location would search for fresh fashion trends and styles so as to live up to the new lifestyles according to Pani and Sharma (2012) study. In the Indian subcontinent, wearing any particular styled dresses of various brands, acts as the ethnic identity which depends on the variant across several religion, caste, region or class. Though there are variant options, saree is common ethnic attire including little variation in respect to various cultures or traditions or community. In the Indian market, though there is availability for foreign made or foreign branded apparels, it is a common observation that local manufacturer are more in trend and in similar way it can be observed that in specialized apparel market such as ethnic wears, unbranded local products are more in use according to Basil and Ramalakshmi (2013) study. Chattaraman and Lenon (2008) had stated that as sari is being treated as a formal dress in the Indian society, along with stronger cultural set up, the doors of ethnic apparels would make an advantage in the market; as in present time, ethic wear can be calculated to pose one fourth of section in the retail apparel market according to RNCOS (2016) report. As per a report by Trivedi et al. (2017), a large number of women and older buyers will start buying online. There would be twice as many online women shoppers in 2020 and thrice as many older online shoppers. A dramatic shift is in the demand drivers for women from discount-driven to variety-driven. Older online buyers care more about convenience and shorter delivery times. Shopping on smart phones has gained prominence in fashion e-commerce as 85 per cent of online shoppers prefer to shop on their smart phones.

Theoretical background

Theory of reasoned action

According to Ajzen and Fishbein (1980), theory of reasoned action (TRA) depicts an individual’s intentions toward a specific behavior. There are two basic determinants that predict an individual’s behavior, i.e. attitude and subjective norm. The first determinant, attitude, reflects an individual’s beliefs based on his/her likes and dislikes which lead to an outcome in terms of behavioral intentions. It has inferred in the model that there are two major types of beliefs, i.e. attitude beliefs and normative beliefs. Various theorists have analyzed the attitude of an individual on an object and then tried to predict the behavioral intention of that individual. But, Fishbein posited an individual’s intention which has been demonstrated using attitude of certain behavioral factors as compared to analyzing the attitude of an individual’s certain object. Theory of reason action asserted the two most important determinants, i.e. behavioral intention and subjective norms which have the direct effect on performing a certain behavior.

Technology acceptance model

Technology acceptance model (TAM) (Davis, 1993; Davis et al., 1989) is a well-known theory of information technology and it asserts the intention of an individual to use a system. The intention of an individual is having two major determinants, i.e. perceived usefulness (PU) and perceived ease of use (PEOU). These two beliefs have been used as external variables in the model to assert the effect of PU and PEOU on the attitude of an individual, which leads to change in the behavioral intentions. These two beliefs have been used by various studies to determine and predict the purchasing intentions of an individual while using system and technology (Koufaris, 2002; Gefen et al., 2003; Pavlou, 2003). This theory have been accepted and applied to diverse situations to understand the attitude of potential consumers from an e-vendor website (Venkatesh and Davis, 1996; Agarwal and Karahanna, 2000). Pavlou (2003) has proposed the integration of existing TAM model with the trust and perceived risk.

Attitude

According to TRA model (Ajzen and Fishbein, 1980), an individual’s intentions have a large impact on its buying behavior. Attitude plays a role of catalyst in the construct of TRA, where attitude posits the psychological condition of an individual toward online shopping (Jahng et al., 2002). Attitude has shown as the behavioral beliefs in which an individual shows focal behaviroal to get an information about the product and service. After getting and searching for information, an individual may show some desire toward purchase of that product:

H1.

Attitude toward the behavior of hyper-personalization by using digital clienteling is postively related to intention of purchasing a women ethnic wear online.

H5.

Behavioral intentions of hyper-personalization by using digital clienteling is postively related to purchase behavior of women ethnic wear online.

Subjective norms

Subjective norms refer as an individual’s independent variable which has a lot of influence of others on the person’s buying behavior pattern. According to Ajzen and Fishbein (1980), subjective norms considered as important determinant to understand the social influence and perceived pressure on person’s behavior intention toward purchasing a product. So, subjective norms show the behavior of an individual which may get affect based on the perception of immediate society (for e.g. friends, family, relatives, colleagues and reference groups). The previous studies based on the literature prove the positive relationship between the subjective norms and planned behavior, while empirical work shows a different picture of the relationship where behavioral intentions have the influence of subjective norms. The influence of subjective norms plays a big role because human behavior is led by its intentions (Karahanna et al., 1999):

H2.

Subjective norm related to hyper-personalization by using digital clienteling is postively related to intention of purchasing a women ethnic wear online.

Perceived ease of use

Perceived usefulness and perceived ease of use has been viewed as the cognitive component as the part of the TAM. It has been studied that perceived usefulness and perceived ease of use have been seen as the antecedents of TAM, though they are not directly predicting the attitude and behavioral intentions (Gefen et al., 2003). The contribution of TAM and other model is phenomenon in explaining the online transaction and its contribution from the technology point of view. These models posit the hedonic features related to the technology which boosts the consumer’s intentions to shop online and it also demonstrates the importance of these features for an e-retailer (van der Heijden et al., 2001). Perceived ease of use is one of determinant of TAM where an individual believes that the technology will be effortless (Davis et al., 1989). In case of online buying behavior, perceived usefulness is an extent to which an individual perceives that his effectiveness of getting information will increase from the website; similarly, perceived ease of use is defined as the degree an individual’s belief toward technology to make his job effortless. Davis (1989) theorized that all the external factors such as technology-specific factors and system-specific factors influenced by perceived usefulness and perceived ease of use:

H3.

Perceived ease of use related to hyper-personalization by using digital clienteling is postively related to intention of purchasing a women ethnic wear online.

Perceived usefulness

Researchers considered perceived usefulness (PU) as an important ascendant of an individual’s believe of using a system and technology to enhance his performance (Davis, 1989). It is an extent to which an individual perceives that his effectiveness of getting information will increase from the website. TAM posits the PU and PEOU (perceived ease of use) as the important determinants of predicting the behavioral intentions toward online shopping. Because of these perceptions, consumers’ attitude will get influenced and will affect their intentions toward online shopping. There is a very strong relationship between perceived usefulness and intention of an individual toward online shopping, whereas, the relationship between perceived usefulness and attitude is comparatively weak (Davis et al., 1989; Jackson et al., 1997; Lucas and Spitler, 1999):

H4.

Perceived usefulness related to hyper-personalization by using digital clienteling is postively related to intention of purchasing a women ethnic wear online.

Intention predicting purchase behavior toward hyper-personalization through digital clienteling

This study is an effort to understand the extended model or conceptual model by combining two main models, i.e. TRA and TAM. An integrated approach has been taken to understand the intentions to predict purchase behavior while doing hyper-personalization by using digital clienteling. TRA and TAM propose that attitude, subjective norm, PEOU and PU effect on behavioral intentions and resulting in purchase behavior. The current study has reviewed various articles where the integrated approach has been considered to study included from MIS Quarterly, Journal of Management Information Systems, Information and Management (Figure 1).

Model testing

The study aims to understand the effect of hyper-personalization performed through digital clienteling impact on behavioral intention and purchase behavior also. After looking at current scenario, the requirement of such type of studies arises where customers have an access to different sources which impact the customer’s attitude toward online shopping. Various factors, which have an impact on the customer’s attitude, are also impacting their purchase intention and purchase behavior. To understand the customer behavior toward hyper-personalization done through digital clienteling, structural equation modeling (SEM) has been used in the study. According to a study performed by Bollen and Long (1992), various relationship networks has been derived based on theory by using statistical methodologies like SEM. In the current study, SEM has been performed in two steps. Primary, confirmatory factor analysis (CFA) has been performed to find the measurement model acceptability, and SEM has been performed to check the model fit of the structural model based on theoretical approach.

Method

Sample design

This paper is describing the consumer purchase intention toward hyper-personalization through digital clienteling with special reference to women ethnic wear. So, in the current study, the target segment taken is from 18 – 45 years women respondents, who have done online shopping within 4-5 months previously for ethnic wear. Total 300 respondents have taken for this study, out of which 270 have been used for the analysis.

The demographic analysis of the study is as follows (Table I, II and III):

Research instrument

A questionnaire has been designed to measure the variables based on the two theories which have been used in the studies. There are 20 items have been considered for the this and all the items have been analyzed on Likert scale for four major categories of factors, i.e. attitude, subjective norms, perceived ease of use and perceived usefulness. To find the model between the theoretical model and current study analysis, structural equation modeling (SEM) has been used (Hooper et al., 2008). The research instrument has considered using in this study based on previous studies relevance (Table IV).

The Cronbach’s alpha value for the 20 items proves the reliability test (Hair, 2007).

Confirmatory factor analysis

Confirmatory factor analysis has been performed on the 20 items using the conceptual model which is based on an integrated approach of two different model, i.e. TRA and TAM. It consists of four exogenous variables (attitude, subjective norm, perceived usefulness and perceived ease of use) and two endogenous variables (purchase intention and purchase behavior). Purchase intention has been theorized and hypothesized as one of endogenous variable and works as a mediator while proving the relationship between all the exogenous and endogenous relationships. The regression estimated of the entire observed variable should be above 0.3 considered as normal and above 0.5 considered as healthy (Hair, 2007). It proves that all the construct items have been confirmed to the validity test (Table V).

So, finally, the remaining 16 items for five constructs have been considered for the final analysis. As from the above table, it is clear that all the 16 items have the strong effect on the major six factors which has been defined by the TRA and TAM. The result of factor loading of various items proves their relationship with the subsequent factor and the relationship effect is proportional to the value of factor loading.

Measurement model

To measure the theory with various construct, the measurement model has been used. By using measurement model, various constructs have been measured to check the model fit. CFA analyzes the relationship between all the constructs, which have been taken based on the postulated theory on the study. Further, the results of CFA will be compared to the base theory and then analyze the model fit (Figure 2).

Construct validity

It is used to check the validity of various construct which has been used to measure the model fit. To confirm, that the items which has been used to analyze, are actually able to measure the dimension. CFA has been used to analyze the reliability of items which has been used to perform the analysis on Likert scale. AMOS 19 has been used to perform the statistical analysis. After performing the analysis, the result values confirm the model fit (Table VI).

After performing the analysis, all the values of various statistic indexes are falling under the acceptable range and thus, approving the good model fit.

Structural model

To analyze the model fit, structural model testing is the further step in performing the SEM analysis while analyzing the measurement model validity. The conceptual mode of the current study has been structured on the basis of given model which has been described under literature review (Figure 3).

Estimated standardized path coefficients

To perform SEM analysis and primary requirement of model fit, the estimated coefficients of all the standardized paths should be significant in nature. As per the analysis, the relationship among all the constructs is significant in nature (Table VII).

In the current analysis, it has been posited that the relationship between various construct is at significance level (0.01*, 0.05**).

Hypothesized relationship between various construct is found to be significant and support the theorized model fit indices. The values of the model fit indices fall in the permissible and acceptable range (Table VIII).

Conclusion and implications

The current study has performed to test the model fit and hypothesis relationship which has been formed on the basis of literature review and simultaneously developing the new model based on the present scenario. In the current study the role of various dimensions like attitude, subjective norms, perceived ease of use and perceived usefulness have been tested in the hyper-personalization through digital clienteling. In the conceptual mode, the integrated approach of two major theories TRA and TAM (Ajzen and Fishbein, 1980; Davis et al., 1989) has been used for this study to understand the online consumer behavior toward hyper-personalization through digital clienteling. Two models have been used together to understand the online consumer behavioral intention and its effect on the purchase behavior. To test the conceptual mode, the data have been collected through random sampling and the target respondents were of women who have done online apparel shopping previously. The data have been collected from the age group of 18-45 years majorly from metro cities and this can be considered as the major limitation of the study.

As per the TRA model (Ajzen and Fishbein, 1980), attitude and subjective norm has the direct impact on online consumer behavioral intentions and the behavioral intentions have direct effect on the purchase behavior. All the relationship among attitude, subjective norm, perceived ease of use and perceived usefulness are significant in nature and having values in the permissible range. The results and findings proved the relationship of various determinants as per the theoretical model, although the current study aims to prove the relationship of all these determinants for the hyper-personalization through digital clienteling. The respondents have given their responses on the basis of hyper-personalization feature provided by the company by using big data and their purchase behavior on the basis of that. The findings posited that perceived usefulness is having the strong relationship with purchase intention as compare to other variables. So, the analysis postulated that customer considered hyper-personalization is having perceived usefulness for customer and it also helps customer in getting the information about the product on the Web page.

Because of advancement of technology, the usage of online media is increasing day by day and this change is having very high impact on the though we can innovate in any field or industry and thus facilitating a personalization never known consumer buying behavior and attitude toward online shopping. This study provides the results of new horizon. The current study approaches new way of understanding the participative management of the personalization and tool to guide the work of strategy professionals and management of fashion e-commerce sector internationally and even in the other sectors also. Another, important finding of the study is that personalization has its own importance and customer considers personalized products information is having more usability also. By having personalized Web page through big data analytics, customer will have positive experience and positive association with the company. The other parameters also play an important role toward the customer behavioral intention.

Limitations and future research

In the current study, very less number of variables has been considered for the study. But from the future study perspective, other factors such as social, personal and cultural factors can be considered for the study to get better insights about the research. As per Deloitte digital market report (Published, 2015), the number of internet users are increasing exponentially and the maximum number of users’ base ranged from 16-35 age group. The main limitation of this study is to consider the respondents from metro cities only, although rural population and tier 2-3 cities are having huge potential in terms of prospect customer base. Because of lack of availability of resources, a specified sampling method has been used for this study. A new research, which will cover the fashion apparel from all the categories with a detailed study from the branded and non-branded point of view, will provide better description on this topic. So, this new area of research is having large scope of future research from the fashion industry point of view. In future, a more structured and specified research can be doable in the area of hyper-personalization through digital clienteling. The current paper is working as one of element in the area of hyper-personalization through digital clienteling to gain sustainable results in the fashion industry.

Figures

Conceptual model

Figure 1.

Conceptual model

Measurement model

Figure 2.

Measurement model

Structural model

Figure 3.

Structural model

Sample demographics

Variable Category Count (%)
Gender Female 270 100
Internet access hardware Mobile phone 82 30.3
Desktop/laptop 188 69.6
Marital status Married 158 58.5
Single 112 41.4
Education Undergraduate 93 34.4
Postgraduate 117 43.3
Others 60 22.2
Monthly personal income Below 10K 64 23.7
10K-25K 22 8.15
25K-50K 42 15.5
50K-100K 123 45.5
Above 100K 19 7.03

Cross-tabulation

Cross-tabulation
Monthly personal income
Below 10K 10K-25K 25K-50K 50K-100K Above 100K Total
How many times purchased women ethnic apparel online
1-2 times 38 10 14 49 3 114
2-3 times 17 8 9 0 5 39
3-5 times 9 4 17 11 0 41
5-8 times 0 0 0 63 0 63
>8 times 0 0 2 0 11 13
Total 64 22 42 123 19 270

Cross-tabulation

Cross-tabulation
Marital status
Married Single Total
How many times purchased women ethnic apparel online
1-2 times 67 30 97
2-3 times 8 47 55
3-5 times 8 35 43
5-8 times 52 0 52
>8 times 23 0 23
Total 158 112 270

Reliability coefficient

Reliability statistics (Cronbach α)
Cronbach’s alpha Cronbach’s alpha based on standardized items No. of Items
0.936 0.937 20

Measurement model indices

Variable Code Attribute Factor loadings
Factor 1 (Attitude) 4 items ATT1

ATT2
ATT3
ATT4
Online shopping by having personalized feature is time saving
Online shopping is 24*7
Buying personalized things over the internet is a good idea
I like to do online shopping when I am having personalized product offerings
0.95
0.50
0.59
0.62
Factor 2 (Subjective norm) 2 items SN1
SN2
People who influence my behavior would encourage me to use online shopping for personalized products
People who are important to me would encourage me to use online shopping for personalized products
0.73
0.75
Factor 3 (Perceived usefulness) 3 items PU1
PU2
PU3
Personalized Web page would be useful for getting information about the product
For me, valuable information about the product is important to me
Personalized information would enhance my effectiveness in getting useful product information
0.49
0.40
0.85
Factor 4 (Perceived ease of use) 2 items PEOU1
PEOU2
Getting information specific to the product from the personalized Web page would be easy
For me, getting product information based on my requirement easily available from website
0.76
0.39
Factor 5 (Behavior intension) 3 items PI2
PI3
PI1
I intend to use the internet purchasing for personalized products as much as possible
I intend to use the internet purchasing in the future also if personalized services will be provided
Given that I had access to the personalized internet purchasing, I predict that I would use it
0.81
0.75
0.32
Factor 6 (Purchase behavior) 2 items PB1
PB2
I would feel comfortable buying personalized things over the internet on my own
The internet is a reliable way for me to take care of my personal affair
0.63
0.59

Model fit indices for measurement model

Statistic Recommended value Obtained value
Chi-square value 364.878
df 230
CMIN/DF < 5.00 3.142
GFI > 0.90 0.959
AGFI > 0.80 0.923
TLI 0.867
CFI > 0.90 0.911
RMSEA < 0.10 0.073

Significance (p) values

Estimate SE P
PI <— Attitude 0.16 0.017 **
PI <— SN 0.15 0.022 **
PB <— PI 0.25 0.032 **
PI <— PU 0.33 0.015 **
PI <— PEOU 0.08 0.010 **

Note: Significant at *p < 0.01 and

**

p <0.05

Model fit indices for structural model

Statistic Recommended value Obtained value
Chi-square 391.545
df 215
CMIN/DF < 5.00 3.100
GFI > 0.90 0.945
AGFI > 0.80 0.910
TLI 0.870
CFI > 0.90 0.905
RMSEA < 0.10 0.069

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Further reading

Mangtani, N. (2017), Clienteling in 2017 – Defining the Future in-Store Experience, Forbes.

Corresponding author

Geetika Jain can be contacted at: geetikajain02@gmail.com