Search results

The current work aims to understand the consumers’ adoption of electric vehicles (two-wheelers) from their value perspective by utilizing the value-based adoption model.

The study considered data from 302 potential electric two-wheeler customers and tested the hypotheses using structural equation modeling.

The outcomes showed that perceived economic benefits, social image, enjoyable acceleration and enhanced fun and perceived environment (positively) and perceived physical safety risk, perceived cost of ownership and range and charging risk (negatively) influenced the customers’ perceived value linked with electric two-wheeler (ETW) adoption. Only low engine noise emission and infrastructure issues did not affect perceived value.

Most of the respondents considered in the study were less than 35 years old. Hence, the model can be tested for other age groups.

The study’s outcomes will help ETW marketers, manufacturing companies and governments (state and central) to provide a more convenient environment for electric two-wheelers' adoption and help them curate appropriate strategies.

The current work offers a better understanding of potential customers' ETW adoption by employing a value-based trade-off.

Understanding consumer behavior across various contexts within marketing has long been the focus of studies. Although many models are used in explaining consumers' behavior, one of these is the Model of Goal-Directed Behavior (MGB), which is becoming prominent in explaining consumers' behavior in marketing. Given its popularity, prior research on MGB has shown inconsistent outcomes regarding the casual association of MGB variables. To overcome this, the authors have adopted a meta-analytic review of the marketing studies grounded on MGB theory in examining the consumers' behavior.

The study reviewed and analyzed 611 correlations from 27 studies with 31 samples (combined sample size of 9588) using a meta-analytic structural equation modeling (MASEM) technique.

The outcomes of MASEM confirm the significance of all the proposed relationships in the MGB model. However, the attitude has shown a strong influence on desire formation among all the proposed MGB relationships. Further, past buying experience and positive anticipated emotions strongly affect desire in developed nations compared to developing nations.

The current work has considered the possibility of various recommended moderators (e.g. culture, crisis situation, sample size, method of data collection, etc.); however, the study lacks to consider the dimension of gender dominance in it. Hence, future researchers should keep it in mind while conducting similar studies. Future scholars can also perform a comparative study on MGB across the domains and subdomains to know more insights.

The current work offers a better understanding of MGB application in marketing. As this work is one of the first meta-analyses on MGB application in marketing that also considers the effect of various moderators, it thus adds knowledge to the literature on MGB in marketing. It will also help the future researchers to understand MGB as a framework and its application in marketing.

Grounded on the concept of a value trade-off, the authors of this study seek to address the question of why some people visit an offline store before purchasing online. The authors offer a novel perspective by identifying and describing the perceived value drivers (benefits and sacrifices) associated with showrooming in the context of the branded apparel segment.

Data collected from 318 showrooming customers were analysed in the context of the proposed perceived value framework using the structural equation modelling method.

The results showed that enhanced product evaluation, monetary savings, smart shopper feelings and perceived enjoyment (positively) and search costs and online risk (negatively) influenced consumers' showrooming value perceptions as benefits and sacrifices associated with showrooming. Only perceived consumption delay emerged as insignificant. As expected, perceived showrooming value was identified as an important driver of showrooming intentions.

The application of this paper's findings is limited to the branded apparel segment. The model can be tested in other sectors with a larger sample size to gain deeper insights.

The findings can be utilized by brick-and-mortar retailers to retain showrooming customers.

The authors of the current research work contribute to a better understanding of showrooming by adopting a perceived-value-based perspective, which offers an alternative yet effective route for understanding showrooming.

This article undertakes a literature review on showrooming, offering an exhaustive overview of research publications and future research objectives that will contribute to extending the understanding of the phenomenon.

The showrooming literature has been collected from journals indexed by SCOPUS and ranked by ABDC. This was later analysed with the SPAR-4-SLR framework and the TCCM methodology (theories, contexts, characteristics, and methodologies) proposed by Paul et al. (2021) and Paul and Rosado-Serrano (2019).

The insights of this review include bibliometrics of showrooming research and the number of explored showrooming theories, methodologies, and contexts from which the phenomenon has been studied. It also highlights the various aspects that might be considered while building an optimal approach.

Articles published in SCOPUS-indexed and ABDC-ranked journals between 2012 and August 2022 were considered. Some articles published in conference proceedings and journals, not fulfilling the aforementioned criteria, might have been missed.

SPAR-4-SLR and TCCM methodologies would aid the researchers in further exploration of this phenomenon and suggest options for enhancing customer experience (CX) eventually leading to customer retention. Retail channel managers will find this knowledge handy in “encouraging loyal showrooming” and ensuring business sustainability.

This study uses the novel SPAR-4-SLR framework to structure the review, while TCCM methodology sheds light on the showrooming from the perspective of various theories, contexts, characteristics, and methodologies. The scope for further research identified through the above-mentioned framework and methodology would be of high value to the researchers and practitioners alike.

This research aims to investigate the showrooming phenomenon in the context of the evolving omnichannel shopping landscape, which seamlessly integrates both physical and online retail channels. Showrooming, wherein customers browse products in physical stores but ultimately purchase from online competitors, poses a potential threat to the job security and job satisfaction of sales staff in brick-and-mortar (B&M) stores. To address this issue, this study explores the relationship between showrooming, self-efficacy, sales performance, job insecurity and job satisfaction of sales staff, using the job demands-resources (JDR-R) model as a theoretical framework.

This research employs quantitative research methods and gathers data from 219 sales staff working in Indian retail stores. Structural equation modeling is used to test the proposed hypotheses.

The results indicate that showrooming is associated with a decrease in the self-efficacy, sales performance and job satisfaction of sales staff. Furthermore, the result indicates that showrooming is positively associated with increased job insecurity among the sales staff.

This study offers valuable contributions to existing literature and offers insights for both retailers and salespeople regarding the potential repercussions of showrooming. It also suggests coping strategies to address the challenges posed by showrooming and the behavior of showroomers.

The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images.

We used machine learning techniques with convolutional neural network.

Detecting COVID-19 symptoms from patient CT scan images.

This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images.