Developing a Research Instrument to Study the Impact of Consumer Brand Perception, Consumer Brand Relationship and Consumer Buying Behaviour on Online Apparel Shopping

Sharma, Rishabh; Ahuja, Vandana; Alavi, Shirin

doi:10.1007/978-3-030-24374-6_5

Cited by 5 publications

(7 citation statements)

References 21 publications

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“…It originated in social psychology, and this relationship has been considered an interpersonal relationship. It includes reciprocal interactions between relationship partners through several repetitive actions (Sharma et al, 2020). A sustained CBR generates several significant outcomes for both participants (Aaker & Equity, 1991;Keller, 2001;Nobre et al, 2019;P.…”

Section: Consumer-brand Relationship (Cbr)mentioning

confidence: 99%

“…Most of the scholars in their various researches have conceptualized the CBR as an outcome of one or more relational constructs such as satisfaction, trust, commitment (loyalty) (Khamitov et al, 2019), and love (Morgan & Hunt, 1994;Roberts et al, 2003). Moreover, the most modern school of thought proposed that the development of CBR at a global level, as generated by an amalgamation of such relational constructs of trust, loyalty, and relationship satisfaction, presents the significant measurement of relationship potency and behavioral loyalty (Sharma et al, 2020). Therefore, brand loyalty, brand trust, and customer satisfaction are the core three interrelated variables treated as critical components of such a relationship.…”

Section: Consumer-brand Relationship (Cbr)mentioning

confidence: 99%

“…The notion of consumer-brand relationships (CBR) has received significant and escalating attention from researchers and practitioners (Tsai, 2011). Scholars deemed this relationship is hugely contributing to capitalizing the performance of brands, and they have realized the vital role of the CBR for the success of brands (Sharma et al, 2020). Previous researches have demonstrated that a productive CBR improves the willingness to pay premium prices (Thomson et al, 2005), brand durability (Ahluwalia et al, 2000), and brand loyalty among consumers (Hwang & Kandampully, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Role of Brand Love and Consumers’ Demographics in Building Consumer–Brand Relationship

et al. 2020

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The conception of the consumer–brand relationship has been receiving immense and escalating attention, and it is considered essential for capitalizing the performance of brands. The purpose of this study is twofold: Foremost part of the study comprehends valuable consumer–brand relationships and antecedents of brand equity. The second part is measuring the significant variations in hypothetical model under consumers’ gender and age perspective. The structural equation model is used for analyzing 425 questionnaires through SmartPLS3. Analysis has been conducted several times for combine data and data sets of gender and age groups to identify variations in model relationships. The findings of this study revealed the significant relationship among constructs of the hypothetical model. Brand love emerged as not only one of the significant antecedents of brand loyalty but also the strongest predictor of brand equity. Significant variations have been identified as male, and older consumers are transforming their love into loyalty and equity more than female and young consumers, respectively. The study has revealed guidelines for designing consumer–brand relationship and identified some significant variations that can be helpful in designing such relationship and marketing offers for multiple market segments.

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Section: Consumer-brand Relationship (Cbr)mentioning

confidence: 99%

Section: Consumer-brand Relationship (Cbr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Role of Brand Love and Consumers’ Demographics in Building Consumer–Brand Relationship

et al. 2020

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“…Therefore, the right marketing construction will affect satisfaction and customer loyalty. Furthermore, E-Enjoyment (Sharma et al, 2020), navigation and ease of use (Tandon et al, 2016;Ladhari and Leclerc, 2013), visual involvement (Tandon et al, 2016;Ladhari and Leclerc, 2013;Bressolles et al , 2014;Karimov et al, 2011), web interactivity (Ladhari and Leclerc, 2013;Gehrt et al, 2012), are essential factors that influence the quality of surfing experiences on e-commerce websites that have the potential to impact customer satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Antecedents to Websıte E-Commerce Satısfactıon and Loyalty

Darta

Daulay

Abdillah

2020

APMBA

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This study explores the major determinants of feedback on shopping satisfaction. This research is essential to provide empirical evidence about the factors that can ensure customer satisfaction and loyalty. By surveying respondents that are users of e-commerce website services, this study uses the construct validity test using the Partial Least Square (PLS) method with SmartPLS software application version 2.0. The results explain the visual involvement variable does not correlate with customer satisfaction and loyalty. The interactivity website variable has a strong relationship with customer satisfaction and loyalty. Therefore, customers need service communication interactivity for service providers and merchants. This research contributes to the development of consumer experience theory and online consumer experience.

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“…The growing quantification of disposable income has created tremendous opportunities for the parallel growth of e-commerce online retail infrastructure (Bronnenberg & Ellickson, 2015). Sharma et al (2020) felt that the authors established various parameters affecting these three constructs and developed a research tool to study the effect on online clothing shopping of consumer buying perception (CBP), consumer buying relationship (CBR) and consumer buying behaviour (CBB). Raza et al (2020) recommended their research focussing on factors such as personality congruence, promotion and visual communication should be relevant.…”

Section: Demand Forecastingmentioning

confidence: 99%

Forecasting Buying Intention Through Artificial Neural Network: An Algorithmic Solution on Direct-to-Consumer Brands

Prasad¹,

Ghosal

2021

FIIB Business Review

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The direct-to-consumer (DTC) brands are emerging to reach more number of consumers with more potential to meet their expectations. They are characterized through their metamorphosis as the vertical brands sell their products from the manufacturer to consumers directly without any interruptions from distribution channels as in traditional mode of doing business. They are annihilating themselves in the virtual platforms and later disrupting their existing linear sales models. This empirical investigation is targeted to construct an algorithmic model through a deep learning process which has been instrumental to predict the purchase decision. This investigation has churned a predictive model that is based on the attributes of the buying behaviour of the consumers. The attributes of online buying behaviour like safety of transaction, availability of innovative products and quality of products have been considered to build a predictive model through artificial neural network (ANN). The accuracy of training and testing data are closer, which infers about the consistency and validity of the predictive model. There are several consequences arising from the predictive model obtained that can be seeded from customer-centred marketing and further stemmed from the framing of business strategy, gaining insights into market architecture and choice of customer

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Developing a Research Instrument to Study the Impact of Consumer Brand Perception, Consumer Brand Relationship and Consumer Buying Behaviour on Online Apparel Shopping

Cited by 5 publications

References 21 publications

Role of Brand Love and Consumers’ Demographics in Building Consumer–Brand Relationship

Role of Brand Love and Consumers’ Demographics in Building Consumer–Brand Relationship

Antecedents to Websıte E-Commerce Satısfactıon and Loyalty

Forecasting Buying Intention Through Artificial Neural Network: An Algorithmic Solution on Direct-to-Consumer Brands

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