The primary purpose of this study was to investigate and present a theoretical model that identifies the most influential factors affecting the adoption of artificial intelligence (AI) by SMEs to achieve sustainable business performance in Saudi Arabia by integrating the Technology–Organization–Environment (TOE) framework. The authors utilized a quantitative method, using a survey instrument for this research. Data for this research were collected from managers working in six different sectors. Subsequently, based on company size, firms were divided into two groups, allowing multi-group analysis of small and medium-sized businesses to explore group differences. Hence, firm size played a moderating role in the conceptualized model. Data analysis was performed on SmartPLS 3, and the results suggest that dimensions of the TOE framework, such as relative advantage, compatibility, sustainable human capital, market and customer demand, and government support, play a significant role in the adoption of AI. Moreover, this study found a significant influence of AI on SMEs’ operational and economic performance. The multi-group analysis (MGA) results reveal significant group differences, with a medium-sized firm strengthening the relationship between relative advantage and AI adoption compared to small-size firms. The findings lead to practical implications for companies on how to increase the adoption of AI to help SMEs embrace their technological challenges in KSA and obtain sustainable business performance to contribute to the economy.

Purpose: Unlike previous studies that examine the role of different entrepreneurial ecosystem factors in predicting entrepreneurial intentions. The purpose of this study is to explain the direct effects of entrepreneurial ecosystem factors effecting entrepreneurial intentions and configurational combinations of entrepreneurial ecosystem factors that cause high or low entrepreneurial intentions among female university students in Saudi Arabia. Design/methodology/approach: The study used structured survey-questionnaire based data collected from 310 female students enrolled in different universities in Saudi Arabia. The study used symmetric analysis using structural equation modeling technique, whereas asymmetric analysis is performed using the fuzzy-set qualitative comparative analysis, necessary condition analysis is also used to identify the role of different entrepreneurial ecosystem factors in increasing and/or decreasing entrepreneurial intentions among young Saudi women. Findings: The results of symmetrical analysis show that access to finance, access to physical infrastructure, and cultural factors are not significantly associated with entrepreneurial intentions, whereas government policies and regulations, government programs and support, social factors and entrepreneurship education and training are significantly associated with the development of entrepreneurial intentions among female Saudi university students. While the result of asymmetrical analysis provides 15 configurational models that explains the high levels of certain factors to predict entrepreneurial intentions among female university students in Saudi Arabia. Specifically, social support is found as necessary condition in majority of models to predict high levels of entrepreneurial intentions among female Saudi university students. Practical implications: The results of the study provide empirical evidence to policymakers in Saudi Arabia. The study proposes that it is not mandatory that the high levels of all entrepreneurial ecosystem factors are important to predict high entrepreneurial intentions, rather in some conditions the low levels of certain factors are obligatory to predict high levels of entrepreneurial intentions. Originality/value: Two-step mix-method approach is used in this study containing analysis of symmetric within entrepreneurial ecosystem increase or decrease entrepreneurial intentions among female university students in Saudi Arabia. There has been plenty of research that examines the role of entrepreneurial ecosystem factors in development of university students’ entrepreneurial intentions, however there is less research evident in the entrepreneurship literature that examine the configurational effects of factors within entrepreneurial ecosystem in increasing and/or decreasing entrepreneurial intentions among female university students.

Purpose: With technological progress, retail companies must understand how to make virtual channels and environments more interactive to enhance user engagement. In this study, we apply the stimulus-organism-response (SOR) paradigm and the Customer Engagement Theory to investigate the impact of various types of customer engagement on the connection amid interactivity and continuous metaverse-empowered marketing channel usage. Design/methodology/approach: The analysis was conducted using PLS-SEM on data collected via a representative sampling approach from 300 respondents on prolific online (the UK was fed as the country with the sample representing people with AR/VR gear). Findings: It has been found that cognitive, emotional and social engagement positively and significantly mediates the relation between the variables studied. Personalization had a significantly negative moderating influence on the association between cognitive engagement and continuous use. Originality/value: Besides extending existing research in the area of metaverse-empowered channels, the study’s findings provide important guidelines for retail companies to enhance customer engagement in virtual environments effectively.