Engaging consumers in multichannel online retail environment

Young Consumers’ Usability Preferences for Mobile and Computer Screens for Online Shopping Activities

The current research endeavors to investigate and contrast consumer usability preferences in the context of electronic commerce (e-commerce) and mobile commerce (m-commerce) using the theoretical framework of the theory of planned behavior (TPB). In order to distinguish the variations in usability preferences between e-commerce and m-commerce, a survey consisting of 37 items was administered to a sample of 213 young Chinese respondents. The survey is aimed at gathering information pertaining to the usability aspects of these two online shopping platforms. The findings of this research indicate that consumer preferences for m-commerce and e-commerce are contingent on the specific context of online shopping. The results suggest that individuals tend to prefer one medium over the other based on the specific situation or task at hand. This information is valuable for professionals in the fields of marketing, branding, and distribution as it can inform the development of effective strategies for optimizing online presence and revenue. Specifically, knowledge of consumer preferences can assist in the creation of appropriate communication, branding, and distribution strategies tailored to the specific contexts in which consumers are likely to engage in e-commerce and m-commerce.

A study of Indian Gen X and Millennials consumers’ intention to use FinTech payment services during COVID-19 pandemic

Purpose

Amid the COVID-19 contamination, people are bound to use contactless FinTech payment services. Because of restrictions on physical movement and avoidance of touching physical money, people willingly choose mobile payment, resulting in enormous growth in FinTech payment service industries. Because of this, this study aims to examine the effect of factors affecting Gen X and Millennials users to use FinTech payment services.

Design/methodology/approach

The authors used 328 responses collected through convenience sampling of Indian users aged between 26 and 57 years in the Delhi-NCR region who are users of FinTech payment services.

Findings

The authors’ findings verified that in India, perceived COVID-19 risk, perceived severity for COVID, individual mobility, subjective norms, perceived ease of use and perceived usefulness have statistically significant impacts on FinTech payment services during the COVID-19 pandemic. Structural equation modelling was used to study the proposed research model. Overall, the model predicted 76.9 % of the variation in intention to use FinTech payment services by the abovesaid variables by Indian users during a pandemic.

Practical implications

This study will provide valuable insight to all FinTech service providers and stakeholders in planning and designing the concerned policy. It will be able to draw the attention of users more.

Originality/value

This research added a valuable theory to the existing technology adoption model (TAM) theory. It demonstrated the utility of the above variables in adopting and using FinTech payment services, which will help service providers to develop future strategies because of the COVID-19 pandemic.

“Wow! It’s Cool”: How Brand Coolness Affects the Customer Psychological Well-Being Through Brand Love and Brand Engagement

In this era of razor-edge competition, marketers strive to outperform their rivals by improving their brands. Increasing brand coolness may be the best way to do it. This study used a stimulus organism response (SOR) model by integration with brand attribution theory to conduct a cross sectional study using purposive sampling technique and surveying young consumers of smart gadgets in Pakistan. A total of 1,178 responses were received and analyzed by structural equation modeling. The results found a positive impact of brand coolness (stimulus) on brand love and brand engagement (both modeled as organism). Brand experience moderated these links. Brand love and brand engagement also mediated the relationship between brand coolness and consumer well-being and delight (both modeled as response). The findings suggest a very important contribution to theory and practice by testing unexploited outcomes of brand coolness. Especially, this study contributes to the consumer well-being literature, again an unexploited aspect of marketing literature. Despite the uniqueness of the findings, the cross sectional design of this study remains a major limitation. Future research may supplement the findings with the help of longitudinal studies. Marketers and practitioners may benefit from this study by improving the coolness of their brands so they may not only increase consumer engagement with the brand but they will also make consumers happy with their brands.

E-Commerce Marketing Optimization of Agricultural Products Based on Deep Learning and Data Mining

China Internet plus agriculture was first put forward in 2015 by the Chinese government’s work report, laying the foundation for the development of Internet plus agriculture and promoting the rapid growth of e-commerce marketing of agricultural products. The combination of agricultural product marketing and e-commerce effectively reduces the intermediate links of agricultural product sales. Many e-commerce professional villages have sprung up in some rural areas across the country, and the number of rural e-commerce stores has continued to grow. At this stage, rural e-commerce has become a new way of agricultural trade, and rural e-commerce has formed a unique rural e-store. At present, the e-commerce market share of agricultural products in rural stores is very large, and its advantages are favored by the government, scientific research institutions, and agricultural products processing enterprises. However, with the gradual development of rural e-commerce, it has also encountered many difficulties. Based on this point, this study applies deep learning and data mining to optimize e-commerce marketing. First, with the growth of the online scale of agricultural product transaction data, the creation of traditional shallow model cannot meet the needs of online data processing. Therefore, this study decides to use the deep learning theory for optimization. It has excellent performance in the technical fields of big data processing and image and voice processing and has strong construction ability, which can effectively represent the characteristics of the model. Combined with the characteristics of e-commerce agricultural products processing and consumer practice, this study designs and develops a new customer value evaluation model based on data mining and e-commerce agricultural products value characteristics in the field of e-commerce. By combining deep learning and data mining technology, this study applies it to the field of e-commerce, so as to promote the transformation of marketing optimization.

Mining numerical measure of consumers’ product evaluation expressed in words based on latent Dirichlet allocation

Purpose

It has always been a hot topic for online retailers to obtain consumers’ product evaluations from massive online reviews. In the process of online shopping, there is no face-to-face interaction between online retailers and customers. After collecting online reviews left by customers, online retailers are eager to acquire answers to some questions. For example, which product attributes will attract consumers? Or which step brings a better experience to consumers during the process of shopping? This paper aims to associate the latent Dirichlet allocation (LDA) model with the consumers’ attitude and provides a method to calculate the numerical measure of consumers’ product evaluation expressed in each word.

Design/methodology/approach

First, all possible pairs of reviews are organized as a document to build the corpus. After that, latent topics of the traditional LDA model noted as the standard LDA model, are separated into shared and differential topics. Then, the authors associate the model with consumers’ attitudes toward each review which is distinguished as positive review and non-positive review. The product evaluation reflected in consumers’ binary attitude is expanded to each word that appeared in the corpus. Finally, a variational optimization is introduced to calculate parameters mentioned in the expanded LDA model.

Findings

The experiment’s result illustrates that the LDA model in the research noted as an expanded LDA model, can successfully assign sufficient probability with words related to products attributes or consumers’ product evaluation. Compared with the standard LDA model, the expanded model intended to assign higher probability with words, which have a higher ranking within each topic. Besides, the expanded model also has higher precision on the prediction set, which shows that breaking down the topics into two categories fits better on the data set than the standard LDA model. The product evaluation of each word is calculated by the expanded model and depicted at the end of the experiment.

Originality/value

This research provides a new method to calculate consumers’ product evaluation from reviews in the level of words. Words may be used to describe product attributes or consumers’ experiences in reviews. Assigning words with numerical measures can analyze consumers’ products evaluation quantitatively. Besides, words are labeled themselves, they can also be ranked if a numerical measure is given. Online retailers can benefit from the result for label choosing, advertising or product recommendation.

Modeling Mobile Commerce Applications’ Antecedents of Customer Satisfaction among Millennials: An Extended TAM Perspective

The continued growth for both smartphone usage and mobile applications (apps) innovations has resulted in businesses realizing the potential of this growth in usage. Hence, the study investigates the antecedents of customer satisfaction due the usage of mobile commerce (m-commerce) applications (MCA) by Millennial consumers in South Africa. The conceptual model antecedents were derived from the extended Technology Acceptance Model (TAM). The research made use of self-administered questionnaires to take a cross section of Millennial MCA users in South Africa. The sample comprised of nearly 5500 respondents and the data was analyzed via structural equation and generalized linear modeling. The results revealed that trust, social influence, and innovativeness positively influenced perceived usefulness; perceived enjoyment, mobility, and involvement positively influenced perceived ease of use; and perceived usefulness and perceived ease of use were positive antecedents of customer satisfaction. Several usage and demographic characteristics were also found to have a positive effect on customer satisfaction. It is important for businesses to improve customer experience and satisfaction via MCA to facilitate a positive satisfaction and social influence among young technologically savvy consumers.

Developing and validating e-retailing satisfaction scales with text-mining

Purpose

The purpose of this study includes two parts. First, it introduces a machine-based method for model and instrument development and updating that integrates large sample qualitative data. Second, a new model and instrument for e-commerce customer satisfaction are developed.

Design/methodology/approach

The research occurs in two phases. In Phase 1, data collection occurs with a literature-based quantitative model and instrument that includes at least one qualitative scale item per construct. Data analysis of the resulting data includes factor analysis (FA) and latent semantic analysis text mining to generate an updated model and instrument. In Phase 2, data collection uses the new model and instrument. Data analysis in Phase 2 includes exploratory data analysis with FA, exploratory structural equation modeling and partial least square modeling.

Findings

As a result of the information gained by the integration of qualitative scales in the literature-based survey, the final model departs substantially from the initial research-based research model. It integrates many of the constructs known to impact a website and software usability from information systems research into a new e-retail satisfaction model.

Originality/value

The research method, as presented here, offers a strategy for integrating large scale qualitative data for refinement of models and the development of instruments. It is essentially a method of gaining the wisdom of crowds economically while simultaneously reducing the biases and laborious effort commonly associated with qualitative research.

The role of consumer-brand engagement towards driving brand loyalty

Purpose

The present research attempts to empirically examine the influence of consumer-brand engagement, its potent antecedents, and relationship quality towards creating and enhancing brand loyalty of automobile brands in Indian context.

Design/methodology/approach

Primary data through survey questionnaires were used to gather empirical data from 443 automobile consumers, out of which 417 samples were included in the final analysis. Structural equation modelling technique was used for assessing the hypothesized direct and indirect relationships among the constructs.

Findings

The empirical findings exhibit consumer involvement and brand interactivity contributes positively and significantly towards consumer-brand engagement, while self-brand image congruity does not significantly influence consumer-brand engagement. Further, mediation analysis results show that relationship quality partially mediates the linkage between consumer-brand engagement and brand loyalty.

Research limitations/implications

The outcomes of this research may provide novel insights and contribute to the limited body of knowledge regarding consumer-brand engagement. In addition, the findings may assist the automobile brand managers and market strategists to design strategies aimed at developing long-term consumer relationships.

Originality/value

This empirical research assesses the mediating effect of relationship quality in the linkage between consumer-brand engagement and brand loyalty for automobile brands, and to the best of our knowledge, has not been attempted by prior researchers in this domain.