Assessing the Effects of the COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach

A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines

The emergence of e-commerce platforms, especially online grocery shopping, is heightened by the COVID-19 pandemic. Filipino consumers started to adapt online due to the strict quarantine implementations in the country. This study intended to predict and evaluate factors influencing the intention and usage behavior towards online groceries incorporating the integrated Protection Motivation Theory and an extended Unified Theory of Acceptance and Use of Technology applying machine learning ensemble. A total of 373 Filipino consumers of online groceries responded to the survey and evaluated factors under the integrated framework. Artificial Neural Network that is 96.63 % accurate with aligned with the result of the Random Forest Classifier (96 % accuracy with 0.00 standard deviation) having Perceived Benefits as the most significant factor followed by Perceived Vulnerability, Behavioral Intention, Performance Expectancy, and Perceived. These factors will lead to very high usage of online grocery applications. It was established that machine learning algorithms can be used in predicting consumer behavior. These findings may be applied and extended to serve as a framework for government agencies and grocers to market convenient and safe grocery shopping globally.

What motivates customers to repurchase online under social distancing?

Global e-commerce is growing rapidly during the COVID pandemic. Previous research on customers' online shopping decisions rarely considered social distancing. To investigate customers' continued intention toward online purchases while socially isolated, we propose a framework based on the UTAUT model. A survey of 330 valid samples was collected through an online survey among internet users during a period of social distancing in Indonesia. Hypotheses were validated using a structural equation modeling approach. The results showed that social contingency is the most influential factor on customers' intention to repurchase online under social restriction conditions, followed by customer perceived value and other significant factors. The findings contribute to providing a new understanding of customers' online repurchase intentions when they are in a contingency situation.

Factors influencing the adoption of cashless transactions during COVID-19: an extension of enhanced UTAUT with pandemic precautionary measures

This paper aims to extend the enhanced “unified theory of acceptance and use of technology (UTAUT)” model with pandemic precautionary measures (PPM) to examine the variables that influence the adoption of “cashless transactions (CLT)” during pandemic periods such as COVID-19. For this purpose, this research polled 363 people from 15 zones of Chennai City, India. Utilizing the responses, the relationship between components was explored using a PLS-SEM approach in two distinct research models to examine the influence of PPM in the enhanced UTAUT. The results indicate that the model with PPM (model 2) exhibited an improvement over the model without PPM (model 1) in the variance explained of behavioural intention from 77.3 to 82%. Further, in model 1, performance expectation is the most potent predictor of individuals’ intentions to use CLT, and in model 2, performance expectations deteriorated slightly in its ability and PPM became the most potent predictor. Furthermore, these findings reveal that the PPM-included enhanced UTAUT has significantly strengthened its ability to explain behavioural intent to adopt CLT. Thus, this research model has the potential to be of great use in investigating the adoption of CLT in any epidemic period.

Factors affecting adoption and use of M-commerce services among the customers in Saudi Arabia

M-commerce has the potential to change consumers' shopping habits and establish itself as a significant commerce channel. People rely on digital devices more than ever before, and the growth in M-commerce predicts that mobile will become the preferred channel for online shopping soon. This study is aimed at examining the effect of personal factors, economic factors, ease of doing factors, and safety-related factors due to Covid-19 on the adoption and use of M-commerce services among customers in Saudi Arabia. The study is empirical and is based on survey responses from 340, M-commerce customers in Saudi Arabia. The questionnaire method was used to collect the data. ANOVA and bivariate regression analysis were used to evaluate the collected data. The results showed that four independent variables, namely, personal, economic, ease of doing, and safety factors during the Covid-19 pandemic, are significant predictors of the dependent variable, adoption and use of M-commerce services by the customers. These factors influence customers' purchasing decisions when they use M-commerce services. The study also concluded that the frequency of using M-commerce has increased during the Covid-19 pandemic because of health, safety, and social distancing guidelines. One of the main limitation of the study is the few selective constructs for the research. The finding of the study will be beneficial to the customers to understand the significance of M-commerce services, especially during pandemic situations.

Evaluating the influence of service quality, hedonic, and utilitarian value on shopper's behavioral intentions in urban shopping malls during the COVID-19 pandemic

Shopping malls are subjected to a lot of changes during the COVID-19 pandemic, especially in the Philippines. The objective of the study was to evaluate the influence of service quality, hedonic, and utilitarian value of malls on shopping mall goers' behavioral intentions during the COVID-19 pandemic. SERVQUAL five dimensions were utilized to represent the service quality aspect, while utilitarian and hedonic values are used to embody the shopping mall values among Filipino citizens. Additionally, we included convenience and social experience as part of our model, to which relationships were assessed toward the utilitarian and hedonic shopping mall values, respectively. An online survey was distributed and collected 519 valid responses among Filipino shopping mall goers. Using Structural Equation Modeling (SEM), results showed that for service quality aspect, tangibles, empathy, and assurance had significant effects on shopper's shopping mall satisfaction during the COVID-19 pandemic. On the other hand, both shopping mall values present positive effects on shopper's satisfaction on which utilitarian value gave the strongest influence in the overall model, followed by the hedonic value. Convenience and social experience also showed positive effects on utilitarian and hedonic values, respectively. The utilized extended framework from SERVQUAL dimensions and hedonic and utilitarian values to measure satisfaction and behavioral intentions was seen to be applicable to measure human behavior and lifestyle studies. Overall, the satisfaction from both service quality and shopping mall values aspect strongly influences shoppers' behavioral intention in going to the mall.

An intention to use mobile applications for medical supplies and equipment ordering in clinics

This research developed a mobile medical supplies and equipment ordering app (MMSEOA) model and attempted to validate it empirically. When customers (clinic doctors) make purchases on the app, two types of reasons can be identified: "reasons for" include enduring involvement (emotions), product description, and awareness (familiarity) while the "reasons against", were demonstrated as perceived risk and resistance to change (fear). This study aimed to strengthen and illuminate the most significant dimensions that enhance a doctor's understanding of MMSEOA and the intention to use it. Furthermore, this research investigated the model's applicability among clinic doctors in Jordan. The model was empirically examined using a sample of 342 Jordanian clinic doctors and their secretaries who use mobile services in general. The survey method, a quantitative approach, was utilized; the partial least squares structural equation modeling system was used to investigate the proposed framework. The results demonstrate that these "reasons for" positively influenced the intention to use the MMSEOA except product description. Similarly, reasons against negatively influence the customers' intention to use the MMSEOA app, while perceived risk had no effect on the intention to use. These findings suggested that researchers should focus more on the services, products, and the main function of the MMSEOA to determine their influences on customers' intention to use. This will improve the buying habits related to purchasing medical supplies using MMSEOA and other online platforms, specifically in Jordan and the Middle East at large.

Enhancing technology innovation performance through alliance capability: The role of standard alliance network and political skill of TMTs

Given the increasing competition in standards, standard alliances have become a vital choice for enterprises to enhance their competitive advantage. In standard alliances, what decisions must top management teams make to help their enterprises improve their innovation performance? To answer this question, we draw on dynamic capability theory, social network theory, and high-level echelon theory to understand how alliance capabilities and standard alliance networks affect technology innovation performance. We collected questionnaire data from 465 manufacturing enterprises in China, and the empirical findings show that (1) enterprise alliance capabilities and standard alliance networks have a positive impact on technology innovation performance; (2) enterprise alliance capabilities and technology innovation performance are mediated by standard alliance networks; and (3) the political skills of top management teams strengthen this moderating model. The results of this study enrich the literature on standard alliances and provide a reference for enterprises in developing standard alliance strategies, cultivating alliance capabilities, and exercising the requisite political skills of top management teams.

Research on commodity business value and customer value of e-commerce platforms: Based on consumer psychology and cognition

Under the background of economic globalization and COVID-19, online shopping has gradually replaced offline shopping as the main shopping mode. In this paper, consumers' perceptions are introduced into the traditional BCG matrix to form a new BCG matrix, and according to it, the small gifts of a gift e-commerce platform are classified. We then performed a robustness test comparing the BCG matrix with K-means clustering. We found that new BCG matrix can objectively reflect the value of small gifts and provide suggestions for the e-commerce platform to make subsequent product decisions. Then we judge the customer value of the platform based on the improved RFM model and K-means++ clustering, and provide a reasonable customer value classification method for the e-commerce platform. Finally, we comprehensively consider the relationship between the commodity value and customer value, and analyze the preferences of different types of customer groups for different types of small gifts. Our research result shows that small gifts can be divided into 4 categories according to commodity value, namely "stars," "cash cows," "questions marks," and "dogs." These four categories of small gifts can be converted into each other through marketing ploys. Customers can be divided into important retention customers, key loyal customers and general development customers according to their values. Faced with different types of customers, managers can adopt different strategies to extract customer value. However, consumer psychology will affect consumer cognition, and different types of consumers prefer different types of small gifts, so the precise implementation of marketing strategies will effectively improve the profitability of the gift e-commerce platform. Compared with the traditional classification method, the commodity business value classification method proposed in this paper uses management analysis and planning methods, and introduces consumer psychological factors into the commodity and customer classification, so that the classification results are more credible. In addition, we jointly analyze the results of commodity value classification and customer value classification, and analyze in detail the preferences of different valued customer groups for different types of commodities, so as to provide directions for subsequent research on customer preference.

The impact of consumer positive personality on the purchase behavior of smart products

While the consumption of smart products is continuously increasing, it is essential to explore the trigger mechanism of consumer behavior in respect of smart product purchase. In this scenario, we aim to investigate the impact of consumers’ positive personality on the purchase behavior. We constructed a structural equation model based on the partial least square method and tested our hypotheses on the basis of data analysis. The data were collected by conducting a survey of 326 Chinese consumers. We found two affecting paths from consumers’ positive personality to smart product purchase. First, consumer knowledge promoted by positive personality raises purchase intention and, in turn, stimulates purchase behavior. Second, consumers’ positive personality improves perceived income, which determines actual purchase behavior. This study deepens our understanding of the trigger mechanism of smart product purchase behavior and enriches the consumer behavior theory.

Effect of e-service quality on customer engagement behavior in community e-commerce

Customer engagement behavior is a critical success factor for community e-commerce. While many community e-commerce websites are currently improving service quality to enhance customer engagement behavior, little is known about how such e-services affect customer engagement behavior. Building upon the stimulus-organism-response (SOR) model, this study developed a research model to explain how e-service quality of community e-commerce platform affects customer engagement behavior through customer trust and perceived risk. The research model was empirically evaluated by surveying 326 customers who have shopped through the community e-commerce platforms. The results indicate that e-service quality (system design, intelligent fulfillment, security assurance, and interactive service) positively affects customer engagement behavior. Besides, customer trust and perceived risk play a mediating role between e-service quality and customer engagement behavior. This study offers recommendations to managers on how to build an attractive community e-commerce platform to stimulate customer engagement behavior.

Customer retention through service quality and satisfaction: using hybrid SEM-neural network analysis approach

Mobile commerce is a developing phenomenon, youth worldwide utilising the platform that provides flexibility, ease, and convenience of online shopping through mobile devices. The study investigates the influence of mobile commerce service quality dimensions on the perception of service quality and customer satisfaction. Satisfaction in Mobile commerce promotes the revisit intention among the customer. The study model was designed on the service quality model SERVQUAL, and the factors were adopted from the information and system quality dimensions. The hypotheses were tested with Jordanian adults, and data was collected through a survey from January 2020 to April 2020. The data was analysed using the variance-based statistical analysis tool with Smart Partial Least Squares 3.2. The non-compensatory analytical technique of artificial neural network analysis was employed to assess the study models. Resultantly, responsiveness and cognitive control factors were significantly related to the Mobile commerce overall service quality. The information quality dimension of content usefulness and adequacy was significantly related to the mobile commerce overall service quality. Mobile commerce system quality dimensions were significantly related to the overall service quality. Furthermore, overall service quality perception significantly influenced customer satisfaction, promoting the revisit intention towards mobile commerce. Multilayer artificial neural network analysis was applied. The result conclusively showed that website innovativeness, content usefulness, and ease of use were the three substantial mobile commerce platforms that impacted overall service quality. The e-business management should use state-of-the-art technology features to enhance the quality perception and develop payment security features to nurture trust among users. Future research opportunities and limitations were reported at the end.

Repurchase intentions of new e-commerce users in the COVID-19 context: The mediation role of brand love

The use of e-commerce has exploded due to the impact of COVID-19. People with no experience in e-commerce prior to the COVID-19 pandemic began online shopping for their safety following the pandemic outbreak. As such, these newly joined customers have played a vital role in the rapid development of e-commerce. Maintaining these customers and increasing their repurchase intention is a core issue for e-commerce platform companies. Thus, using new e-commerce users as the participants, this study investigated the structural relationship between brand experience, brand emotional factors (brand attachment and brand love), brand loyalty, and repurchase intention with brand love as the mediator. Research on the multidimensional brand experience (i.e., sensory, emotional, behavioral, and cognitive) from Chinese customers' perspective is still lacking, and our study attempts to fill this gap. A structured questionnaire and hypotheses were designed based on studies and survey of 310 respondents from China in this study. The study results show that, first, the four dimensions of brand experience have a significant positive correlation with brand emotion, with brand cognitive experience having the greatest impact on consumer brand emotion. Second, the influence of brand emotion on brand loyalty is positive and significant, and brand attachment has a stronger influence than brand love on brand loyalty. In addition, brand loyalty has a positive effect on repurchase intention. Finally, brand love plays a mediating role on the relationship between brand attachment and brand loyalty. To enhance customers' brand attachment and love for e-commerce platforms, companies must enhance customers' interest and curiosity in their products. And companies will improve their services to customers by introducing artificial intelligence algorithms to increase customers' repurchase intention, which will ultimately increasing their profitability. This study contributes to the development of e-commerce platform companies.

Industry 4.0 Wireless Networks and Cyber-Physical Smart Manufacturing Systems as Accelerators of Value-Added Growth in Slovak Exports

Industry 4.0 integrates smart and connected production systems that are pivotal in predicting and supporting production in real-time, leading to sustainable organizational performance. In manufacturing, it may increase productivity, sustainability, and energy efficiency, while optimizing competitiveness. The main purpose of this paper is to determine the impact of Industry 4.0 on the Slovak economy through a secondary data analysis in the automotive industry, which is the leading sector in the country. The paper aims to provide a comprehensive analysis of the various opportunities that are available in the value-added growth of car exports in Slovakia. It also explores the case study of PSA Group Slovakia, which highlights the importance of the Industry 4.0 concept in boosting the country’s export growth. The paper proposes a series of recommendations and steps to improve Slovakia’s innovation environment.

The Impact of Consumers' Loneliness and Boredom on Purchase Intention in Live Commerce During COVID-19: Telepresence as a Mediator

This paper examines the relationship between consumer loneliness, boredom, telepresence, influencer-brand image congruence and purchase intention by investigating consumers of live commerce during the COVID-19 period. With the help of an online survey website, survey data was gathered on 550 Chinese customers who experienced live commerce shopping in China. Although previous studies have shown that consumer boredom and loneliness have an impact on purchase intention, the mechanism of influence remains unclear. As a result, additional research is needed to study the link between boredom and loneliness and customer purchase intention. Consumers' purchase intention was influenced by their feelings of loneliness and boredom. Telepresence played a mediating role in the impact of loneliness and boredom on purchase intention. Influencer-brand image congruence played a moderating role in the impact of consumers' boredom on purchase intention. The study results contribute to the research of factors impacting consumers' purchase intention. In addition, this study can help live commerce merchants better understand the impact factors of consumers' purchase intention and contribute to the development of live commerce.

Does User Preference Matter? A Comparative Study on Influencing Factors of User Activity Between Government-Provided and Business-Provided Apps

The competition between government-provided apps and business-provided apps for active users in China is becoming increasingly fierce. Apps with higher user activity will win this competition. To maintain active users, finding the user activity influencing factors is crucial. In this study, we selected a government-provided app—“Beijing One Card”- and a business-provided app—“Bus Code” (the Beijing area) -in the field of public transportation as the comparative research objects. Based on multiple regression analysis, we explored the factors influencing user activity. We found user preference plays a critical role in distinguishing the influencing factors of user activity between two kinds of apps. Distinct from the existing research, the service quality does not affect the user activity of public transportation apps. This paper argues that, whether it is a government-provided app or a business-provided app, to enhance the user activity of the app, in the short term, it is necessary to improve the app function according to the user’s needs, try to provide services which user like, cater to user’s preference, strengthen user experience; In the long run, it is essential to pay attention to mining the user’s data accumulated while operating the app to understand user behavior. So as to affect the user’s preference, enhance user’s trust, and improve user participation through public policy.

Deep Learning-Assisted Smart Process Planning, Robotic Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in the Internet of Manufacturing Things

The purpose of our systematic review is to examine the recently published literature on the Internet of Manufacturing Things (IoMT), and integrate the insights it configures on deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Throughout October 2021 and January 2022, a quantitative literature review of aggregators such as ProQuest, Scopus, and the Web of Science was carried out, with search terms including “deep learning-assisted smart process planning + IoMT”, “robotic wireless sensor networks + IoMT”, and “geospatial big data management algorithms + IoMT”. As the analyzed research was published between 2018 and 2022, only 346 sources satisfied the eligibility criteria. A Shiny app was leveraged for the PRISMA flow diagram to comprise evidence-based collected and handled data. Major difficulties and challenges comprised identification of robust correlations among the inspected topics, but focusing on the most recent and relevant sources and deploying screening and quality assessment tools such as the Appraisal Tool for Cross-Sectional Studies, Dedoose, Distiller SR, the Mixed Method Appraisal Tool, and the Systematic Review Data Repository we integrated the core outcomes related to the IoMT. Future research should investigate dynamic scheduling and production execution systems advanced by deep learning-assisted smart process planning, data-driven decision making, and robotic wireless sensor networks.