Cyber risk assessment in small and medium-sized enterprises: A multilevel decision-making approach for small e-tailors

The role played by information and communication technologies in today's businesses cannot be underestimated. While such technological advancements provide numerous advantages and opportunities, they are known to thread organizations with new challenges such as cyberattacks. This is particularly important for small and medium-sized enterprises (SMEs) that are deemed to be the least mature and highly vulnerable to cybersecurity risks. Thus, this research is set to assess the cyber risks in online retailing SMEs (e-tailing SMEs). Therefore, this article employs a sample of 124 small e-tailers in the United Kingdom and takes advantage of a multi-criteria decision analysis (MCDA) method. Indeed, we identified a total number of 28 identified cyber-oriented risks in five exhaustive themes of "security," "dependency," "employee," "strategic," and "legal" risks. Subsequently, an integrated approach using step-wise weight assessment ratio analysis (SWARA) and best-worst method (BWM) has been employed to develop a pathway of risk assessment. As such, the current study outlines a novel approach toward cybersecurity risk management for e-tailing SMEs and discusses its effectiveness and contributions to the cyber risk management literature.

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.

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.

Sustaining Consumer E-Commerce Adoption in Sub-Saharan Africa: Do Trust and Payment Method Matter?

The long-term sustainability of e-commerce usage for improving quality of life is dependent on several factors. Presently, trust and payment methods have emerged as critical factors influencing e-commerce adoption. Consequently, this study investigates the UTAUT determinants of consumer e-commerce adoption in Ghana by focusing on the mediating and moderating roles of trust and payment method. The study employs the Partial Least Square Structural Equation Model (PLS-SEM) to estimate the survey response of 535 purposively selected respondents from six regions in Ghana. The results corroborate a direct relationship between the UTAUT variables and e-commerce adoption. However, the payment method suggests no moderating effect on trust and e-commerce adoption connection. Nevertheless, trust significantly mediates the UTAUT variables and e-commerce adoption. Finally, the mediation was highest between social influence and trust but lowest between effort expectancy and trust. This challenges practitioners and managers of e-commerce platforms to consider the mediating role of trust to improve adoption and the sustainable usage of e-commerce.

Effects of Risk Perception of Pests and Diseases on Tea Famers’ Green Control Techniques Adoption

Green control techniques support the concept of green plant protection, advocate for the safe and reasonable use of pesticides, and finally achieve the goal of controlling pests and diseases and protecting the environment. The purpose of this study is to explore the effect of risk perception of pests and diseases on farmers’ usage intention of green control techniques. Based on 747 samples of tea farmers in Sichuan province, China, introducing the Unified Theory of Acceptance and Use of Technology (UTAUT) framework and using the Partial Least Square–Structural Equation Modeling (PLS-SEM) approach, this paper found that risk perception has a negative impact on behavioral intention. Performance expectancy, effort expectancy, and social influence can positively affect behavioral intention, and facilitating conditions can also positively influence usage behavior. Moreover, the mediating analysis indicated that the higher the risk perception is, the less performance expectancy of green control techniques and the weaker the behavioral intention. Meanwhile, risk perception also plays a mediating effect on the relationship between effort expectancy and behavioral intention. This study could help to provide references for policymaking to improve the adoption of green control techniques.

Does e-shopping service quality enhance customers’ e-shopping adoption? An extended perspective of unified theory of acceptance and use of technology

This research advances the knowledge in customer behavior literature by adding new exogenous and moderating variables to the UTAUT framework. It explores the relationships among e-shopping service quality (an exogenous variable), e-shopping drivers (performance expectancy, effort expectation, social influence, and facilitating conditions), e-shopping intention, and e-shopping adoption with the moderating role of offline brand trust in an e-shopping context. Structure equation modeling was performed to confirm the distinctiveness of variables and path analysis based on a sample size of 356 e-shoppers in Pakistan. The outcomes demonstrate that e-shopping drivers are influenced by e-shopping service quality. Moreover, e-shopping intention and e-shopping adoption are led by e-shopping drivers. Furthermore, the relationship between e-shopping drivers and e-shopping intention is moderated by offline brand trust. The discussion of theoretical and practical implications and study limitations are also presented.

Modeling the Factors that Influence Ghanaian Entrepreneurs to Adopt e-Commerce

This study investigated the factors that influence Ghanaian entrepreneurs to adopt e-commerce. Cross-sectional data was gathered from 520 entrepreneurs in the most populous and industrious regions in Ghana. The unified theory of acceptance and use of technology (UTAUT) was employed to effectively understand the unexplored phenomenon of e-commerce adoption among Ghanaian entrepreneurs. Partial Least Square-Structural Equation Modeling (PLS-SEM) was used to test the hypothesized relationships. The findings indicate that performance expectancy, effort expectancy, and social influence (SI) positively and significantly influenced the behavioral intention (BI) to adopt e-commerce. Facilitating conditions (FC) and BI had a significant positive relationship with the adoption of e-commerce.

The effect of positive TRI traits on centennials adoption of try-on technology in the context of E-fashion retailing

To provide a more realistic experience, e-retailers have implemented virtual try-on systems. It is, therefore, important to examine the variable that influences customers' intention to use try-on technologies when online shopping for apparel. The main aim of the current study is to identify and examine the design and individual characteristics that influence centennials to adopt virtual try-on systems. Factors extracted from the UTAUT2 model and technology readiness were proposed in the current study model, which was empirically validated based on data collected from 315 participants. The main results of structural equation modeling largely supported the significant role of "optimism" and "innovativeness" in performance expectancy and price value. Behavioral intention was also predicted by all the factors of UTAUT2 apart from effort expectancy. These results provide a guideline for online retailers on how to communicate with their centennial customers to influence them to adopt try-on technology.