What drives customer satisfaction and word of mouth in mobile commerce services? A UTAUT2-based analytical approach

Antecedents of trust and customer loyalty in online shopping: The moderating effects of online shopping experience and e-shopping spending

This paper determines the antecedents of trust (i.e., perceived ease of use [PEoU], privacy concerns [PC], perceived security [PS], product variety [PV], and on-time delivery [OD]) and customer loyalty (CL) in online retailing. A questionnaire was developed using scales validated in prior e-commerce studies to measure these factors in the conceptual model. Data were collected in an online survey from a non-probability judgement sample of online shoppers between the ages of 18 and 65 years old, who provided informed consent for participation in the survey. Data were analysed via structural equation modeling (SEM) on AMOS version 28. The ethical approval certificate was issued by the College of Business and Economics Research Ethics Committee (CBEREC). The results indicate that customer trust (CT) in online shopping relies on OD, PS, PV, and PEoU, but not PC. CT, followed by OD and PV, significantly impacts CL. The results show that trust mediates the relationship between OD, PS, and PV, and CL. Online shopping experience and e-shopping spending significantly moderate the impact of PV on trust. The impact of OD on CL is significantly moderated by the online shopping experience. This paper validates a scientific approach to coexisting effects of these key forces that e-retailer practitioners can use to gain trust and build CL. Research that validates this valuable knowledge is absent in the literature, as the factors were measured disjointedly in prior studies. This study offers originality by validating these forces in online retailing in South Africa.

Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants

Virtual Assistants, also known as conversational artificial intelligence, are transforming the reality around us. These virtual assistants have challenged our daily lives by assisting us in the different dimensions of our lives, such as health, entertainment, home, and education, among others. The main purpose of this study is to develop and empirically test a model to predict factors that affect users' behavioral intentions when they use intelligent virtual assistants. As a theoretical basis for investigating behavioral intention of using virtual assistants from the consumers' perspective, researchers employed the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). For this research paper, seven variables were analyzed: performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value. In order to improve consumer behavior prediction, three additional factors were included in the study: perceived privacy risk, trust, and personal innovativeness. Researchers carried out an online survey with 304 responses. The obtained sample was analyzed with Structural Equation Modeling (SEM) through IBM SPSS V. 27.0 and AMOS V 27.0. The main study results reveal that factors, such as habit, trust, and personal innovation, have a significant impact on the adoption of virtual assistants. However, on the other side, performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, price/value, and perceived privacy risk were not significant factors in the users' intention to adopt this service. This research paper examines the effect of personal innovation, security, and trust variables in relation to the use of virtual assistants. It contributes to a more holistic understanding of the adoption of these intelligent devices and tries to fill the knowledge gap on this topic, as it is an emerging technology. This investigation also provides relevant information on how to successfully implement these technologies.

How do mini games affect female users of mobile commerce? Improving platform satisfaction through game use intention

Combining games with mobile commerce applications has been a trend in recent years. Mobile commerce is attracting a large number of people, especially females, to play mini games on the platform. The gamification of mobile commerce may affect users’ platform satisfaction. This study aims to explore the intention of female users to play mobile commerce games and its impact on platform satisfaction. We collected data from females in China who played mobile commerce games and then used a structural equation model to test the various hypotheses we made. The results showed that game use intention fully mediated the impact of performance expectation, hedonic motivation, and social influence on platform satisfaction. Effort expectation had no significant impact on game use intention but had a positive impact on platform satisfaction. Game use intention had a positive impact on platform satisfaction. The frequency of playing games and the amount of money spent on the platform were positively correlated with game use intention. Females over 25 years old preferred to play mobile commerce games. This study provides a theoretical reference and practical enlightenment and makes a specific contribution to the development of mobile commerce platform and the application of gamification. However, this study has limitations in terms of test samples, research methods and research content, and further research on mobile commerce is required in the future.

Emerging technologies adoption in healthcare: A SOHI model

The internet and emerging technologies have increased the utilisation of digital platforms. This study aims to draw on performance expectancy, social influence, and satisfaction to investigate a proposed model which is geared toward social media healthcare information (SOHI) adoption. The proposed model uses a structured online questionnaire, and 300 responses were evaluated using partial least squares and structural equation modelling [PLS-SEM]. From the findings, performance expectancy of social media (PESM) and satisfaction with social media (SATSM) were revealed to be significant predictors of behavioural intention towards social media (BISM). Satisfaction with social media (SATSM) had the greatest impact on BISM, accounting for 63.8 per cent of the variance in users' intentions to utilise SOHI. Similarly, PESM and social influence on social media (SISM) had the most predictive influence on SATSM, accounting for 50.5 per cent of the variance in users' social media satisfaction, which led to SOHI adoption. Unlike others, the social influence on social media (SISM) did not have a significant effect on BISM. BISM and satisfaction with social media (SATSM) were significant predictors of SOHI adoption, accounting for 54.5 per cent of the variance in SOHI adoption. The recommendations in this study would help healthcare professionals change their approach to digital healthcare engagement.

Enhancing PLS-SEM-Enabled Research with ANN and IPMA: Research Study of Enterprise Resource Planning (ERP) Systems’ Acceptance Based on the Technology Acceptance Model (TAM)

PLS-SEM has been used recently more and more often in studies researching critical factors influencing the acceptance and use of information systems, especially when the technology acceptance model (TAM) is implemented. TAM has proved to be the most promising model for researching different viewpoints regarding information technologies, tools/applications, and the acceptance and use of information systems by the employees who act as the end-users in companies. However, the use of advanced PLS-SEM techniques for testing the extended TAM research models for the acceptance of enterprise resource planning (ERP) systems is scarce. The present research aims to fill this gap and aims to show how PLS-SEM results can be enhanced by advanced techniques: artificial neural network analysis (ANN) and Importance–Performance Matrix Analysis (IPMA). ANN was used in this research study to overcome the limitations of PLS-SEM regarding the linear relationships in the model. IPMA was used in evaluating the importance and performance of factors/drivers in the SEM. From the methodological point of view, results show that the research approach with ANN artificial intelligence complements the results of PLS-SEM while allowing the capture of nonlinear relationships between the variables of the model and the determination of the relative importance of each factor studied. On other hand, IPMA enables the identification of factors with relatively low performance but relatively high importance in shaping dependent variables.

How Web Content Types Improve Consumer Engagement through Scarcity and Interactivity of Mobile Commerce?

The emergence of various web contents gives customers influence according to characteristics. The characteristics of the content can distinguish the three types of commerce: branded content-type commerce, review content-type commerce, and home shopping content-type commerce. The purpose of this study is to identify the difference between customer engagement and reuse intention according to the three types of content characteristics. To identify the research questions, we conducted experiment and survey for three different commerce types. Randomized participants were exposed to different three web contents. Analysis of variance (ANOVA) was applied to compare three groups on average to analyze the differences between those groups. After testing the manipulation of experiment, structural equation modeling for various antecedents was performed. Interaction had a positive effect on engagement, as we prove within the paper. Content information had a positive impact on engagement, as you can see within the research. The effect of attention on engagement was confirmed as positive. Results of analysis proved our hypotheses; thus, scarcity of time had a positive effect on engagement and scarcity of quantity had a positive effect on engagement, as we prove within the paper. Ubiquity has a positive effect on engagement. System quality positively affects engagement. Ease of use has a positive effect on engagement. Consumer engagement had a positive effect on reuse intention. Finally, there are differences among the three kinds of mobile commerce as a conclusion within the findings.

Measuring SERVQUAL dimensions and their importance for customer-satisfaction using online reviews: a text mining approach

Purpose

The extant literature has utilized the SERVQUAL scale to measure service quality dimensions and their importance towards customer-satisfaction using close-ended survey-based questions and not open-ended questions and/or user-generated qualitative responses. On the other hand, while measuring customer-satisfaction drivers from user-generated content (UGC), extant studies have majorly used overall or aspect-wise evaluations and not evaluations specific to SERVQUAL dimensions. In this study, the authors try to bridge the gap.

Design/methodology/approach

The authors suggest a methodology consisting of text mining, machine learning and econometric techniques that can measure consumer evaluations of SERVQUAL dimensions. The authors used qualitative and quantitative UGC obtained from 27,052 online reviews on 362 airlines by reviewers of 158 nationalities for our analysis.

Findings

The authors established a unique method which combines qualitative and qualitative UGC to measure service quality. The authors have also uncovered the comparative importance of such dimensions in creating customer-satisfaction and recommendation in the context of the airline industry.

Originality/value

The paper is one of the pioneering studies that try to find measures of SERVQUAL dimensions from online consumer reviews and their influence on customer satisfaction.

Understanding mobile users' information adoption behaviour: an extension of the information adoption model

Purpose

Mobile messaging applications (MMAs) have surpassed top social media platforms. Recent and rapid use of MMAs has made it extremely difficult to ignore the existence of customer-to-customer (C2C) mobile information. This study, therefore, aims to expand the knowledge of customers' adoption behaviour of such information.

Design/methodology/approach

Through applying and utilizing social support theory (SST) and the information adoption model (IAM), this study introduces a holistic theoretical model, explaining customers' adoption of information derived from MMAs and exploring the antecedents of IAM. Based on the data collected from 305 UK MMA users, this study empirically tests the research model using structural equation modelling estimation.

Findings

The results of this study reveal that social support is a key antecedent of information quality and credibility and support IAM in terms of its ability to explain MMAs' information adoption.

Practical implications

The insights are valuable for businesses and marketers to understand customers' mobile communications and be socially support-oriented while developing marketing communication strategies.

Originality/value

The study integrates SST and IAM to improve the understanding of customers' information adoption behaviour. It is the first attempt that establishes that social support is a key antecedent of IAM.

How does satisfaction of solar PV users enhance their trust in the power grid? - Evidence from PPAPs in rural China

BACKGROUND

Photovoltaic Poverty Alleviation Projects (PPAPs) have been implemented in Chinese rural areas since 2014. As a new energy policy, PPAPs have played an important role in alleviating rural poverty. However, the adoption of solar PV faces multiple barriers from the perspective of beneficiaries. Therefore, this study aims to discuss and analyze factors affecting beneficiaries' satisfaction and their trust in State Grid, promoting the adoption of solar PV.

METHODS

Based on the integrated American Customer Satisfaction Index (ACSI) and Unified Theory of Acception and Use of Technology (UTAUT) model, this study used the Structural Equation Model (SEM) to reveal how the beneficiaries' satisfaction enhance their trust in State Grid. The data were obtained from a survey of 928 PPAPs' beneficiaries by stratified and random sampling in Chinese rural areas.

RESULTS

The results confirm that environmental perception in this study has positive impact on beneficiaries' satisfaction. In addition, perceived quality also has a positive effect on beneficiaries' satisfaction and trust in State Grid; however, social influence has a negative impact on beneficiaries' satisfaction; behavior expectation can directly promote beneficiaries' satisfaction while indirectly propel their trust in State Grid.

CONCLUSIONS

This study constructs an integrated customer satisfaction model from the perspective of beneficiaries and proposes relevant measures to promote the adoption of solar PV that can be applied to poverty reduction in other developing countries worldwide.

Assessing Antecedents of Behavioral Intention to Use Mobile Technologies in E-Commerce

The last decades have seen continuous increases in electronic commerce (e-commerce) and particularly mobile commerce (m-commerce). These sharp increases, especially during the COVID-19 pandemic, have led companies to become aware of the potential of this trade channel. This paper investigates the effects of antecedents on behavioral intention in m-commerce and the role of consumer satisfaction in the buyer decision process. In our investigation, we used the modified technology acceptance model (TAM). The research was conducted through a survey based on a self-administered questionnaire, with the data being analyzed by structural equation modeling and cross-tabulation. Following the investigation, we found that perceived usefulness (PU) and perceived ease of use (PEU) positively influence behavioral intention. Consumer satisfaction also plays a significantly positive role in m-commerce actual use, influencing future behavioral intention. The results also showed that generational and gender differences strongly affect behavioral intention, with younger generations and male respondents being more inclined to use m-commerce.

A Two-Stage SEM–Artificial Neural Network Analysis of Mobile Commerce and Its Drivers

The COVID-19 health and economic crisis has affected all areas of social life globally, including the economy. The world economy has declined due to purchasing power for individuals who have been forced to stay at home and cannot perform work. These restrictions to prevent the spread of SARS-Cov-2 have led to an increase in electronic commerce and mobile commerce as tools for procuring goods and services. In this paper, we conducted a longitudinal analysis of mobile commerce as an essential electronic commerce component, establishing the main drivers of mobile commerce and the intensity of their influences. The research focuses on mobile commerce in the United States (U.S.). It covers the period 2010–2020, the last year of this period capturing the context of the COVID-19 pandemic and its impact on electronic commerce (e-commerce) and mobile commerce (m-commerce). In the macroeconomic analysis of competitiveness, we established the main drivers of m-commerce, using artificial neural networks and the mediation effects found between the variables that describe m-commerce, e-commerce, and total sales, using structural equation modeling. The research results indicate an increase in the share of e-commerce in total sales and a predominance of the m-commerce share in e-commerce on the background of traffic restrictions and social distance rules imposed due to the COVID-19 pandemic. Stakeholders in the m-commerce area should consider the following enhancing drivers: increasing internet speed, expanding 5G and Wi-Fi networks, and increasing accessibility and trust in mobile devices and applications.

Segmentation of Older Adults in the Acceptance of Social Networking Sites Using Machine Learning

This study analyzes the most important predictors of acceptance of social network sites in a sample of Chilean elder people (over 60). We employ a novelty procedure to explore this phenomenon. This procedure performs apriori segmentation based on gender and generation. It then applies the deep learning technique to identify the predictors (performance expectancy, effort expectancy, altruism, telepresence, social identity, facilitating conditions, hedonic motivation, perceived physical condition, social norms, habit, and trust) by segments. The predictor variables were taken from the literature on the use of social network sites, and an empirical study was carried out by quota sampling with a sample size of 395 older people. The results show different predictors of social network sites considering all the samples, baby boomer (born between 1947 and 1966) males and females, silent (born between 1927 and 1946) males and females. The high heterogeneity among older people is confirmed; this means that dealing with older adults as a uniform set of users of social network sites is a mistake. This study demonstrates that the four segments behave differently, and many diverse variables influence the acceptance of social network sites.

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.

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications.

An empirical examination of the moderating role of age and gender in consumer mobile banking use: a cross-national, quantitative study

Purpose

Despite the benefits of mobile banking services in an increasingly digitised world, adoption rates remain unsatisfactory. The present cross-national study examines age- and gender-dependent variations of consumer intentions and use of mobile banking services.

Design/methodology/approach

The study analyses consumer mobile banking use by integrating factors such as with trust, security and privacy and it examines the effects of these factors among two demographic factors including age and gender. 897 Lebanese and British mobile banking users completed a survey. Data was analysed by partial least squares-structural equations modelling.

Findings

Consumer behavioural intention was significantly moderated by age through its relationship with facilitating conditions and trust among Lebanese respondents, and performance expectancy, effort expectancy, hedonic motivation, price value and habit among their British counterparts. As for gender, a significant moderating effect was evidenced in the Lebanese, but not the British sample, on the level of performance expectancy, effort expectancy, facilitating conditions, price value and perceived security.

Originality/value

The findings provide evidence of the applicability of the new factors proposed in this research. The reflection of the influence of these demographic factors in a cross-national context provides insights into mobile banking adoption variation between different countries.