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PurposeConsumers interacting with smart wearable devices is on the rise in the current health-AI market, which offers a great opportunity for companies to execute interactive marketing. However, this opportunity is mainly reliant on consumers' use of smart wearable devices. This paper aims to develop a model considering health and privacy factors to elucidate consumers' use of smart wearable devices for unleashing their full potential in interactive marketing.Design/methodology/approachThe authors collected 250 samples via an online survey to validate the smart wearable devices usage model that elucidates factors that stimulate consumer usage, including privacy concerns, health consciousness and consumer innovativeness. The authors used structural equation modeling and multi-group analysis to test the hypotheses.FindingsPrivacy concerns of consumers have a negative effect on smart wearable devices usage, while health consciousness positively impacts consumers' usage of smart wearable devices. Consumer innovativeness indirectly affects smart wearable devices usage via effort expectancy. Experienced consumers are less sensitive to the performance expectancy but more affected by effort expectancy regarding smart wearable devices.Originality/valueThe present study contributes to the literature stream of health-AI usage by unraveling the impacts of privacy concerns and health consciousness and examining the moderating role of prior experience. The findings suggest marketers in the health-AI industry should endeavor to build transparent and sound privacy protection mechanisms and promote smart wearable devices by fostering health awareness of potential consumers.

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PurposeThe purpose of the research is to explore how small and medium enterprises (SMEs) in central China achieve intelligent transformation through the use of artificial intelligence (AI). Because of unequal resource allocation, constraints on the intelligent transformation of SMEs in central China are different from those in economically and technologically well-developed coastal provinces. Hence, the authors focus on SMEs in central China to identify drivers of and barriers to intelligent transformation.Design/methodology/approachThe interview data were collected from 66 SMEs across 20 industries in central China. To verify the validity of the data collection method, the authors used two methods to control for retrospective bias: multi-level informants and enterprises' AI project application materials (Wei and Clegg, 2020). The final data were validated without conflicts. Next, the authors cautiously followed a two-step approach recommended by Venkatesh et al. (2010) and used NVivo 11.0 to analyze the collected text data.FindingsSMEs in central China are enthusiastic about intelligent transformation while facing both internal and external pressures. SMEs need to pay attention to both internal (enterprise development needs, implementation cost, human resources and top management involvement) and external factors (external market pressure, convenience of AI technology and policy support) and their different impacts on intelligent transformation. However, constrained by limited resources, SMEs in central China have been forced to take a step-by-step intelligent transformation strategy based on their actual needs with the technological flexibility method in the short term.Originality/valueConsidering the large number of SMEs and their importance in promoting China's economic development and job creation (SME Bureau of MIIT, 2020), more research on SMEs with limited resources is needed. In the study, the authors confirmed that enterprises should handle “social responsibility” carefully because over-emphasizing it will hinder intelligent transformation. However, firms should pay attention to the role of executives in promoting intelligent transformation and make full use of policy support to access more resources.