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This paper aims to answer questions pertinent to whether or not services provided by smart hotels are really what customers are looking for, as well as to ascertain what are some unintended experiences guests may encounter. In essence, to the best of the authors’ knowledge, this research is the first in the field to acknowledge the paradox of smart service.

This inquiry adopts a qualitative approach with data-driven from online customer reviews and semistructured interviews. Thematic analysis was undertaken to interpret review comments.

Results point to a new phenomenon, which is coined as the smartness paradox. In particular, customers on one hand enjoy an array of smart-infused experiences that jointly offer patrons a sense of a futuristic lifestyle. On the other hand, smart devices superimpose a number of hindrances that bring guests dismay and annoyance.

This investigation brings smart service failure to the fore to highlight several key failure themes that could jeopardize the entire operation with debased customers’ satisfaction and loyalty inclination.

The smartness-paradox framework used in the present inquiry entails both approach and avoidance consequences customers enact depending on their smart experiences.

Fixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.

Through an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.

The comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).

In this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

Despite the increasing relevance of seamless shopping experience in an omnichannel context, research on how seamless shopping experience affects customers’ word of mouth on social media (sWOM) remains scant. Based on the attribution theory, this study aims to investigate the effects of seamless shopping experience types on customers’ sWOM intentions from the perspective of smart-shopping feelings and validated the moderation role of shopping orientation.

Using a data set of 301 omnichannel customers, three scenario-based experiments were conducted to address the research questions.

An efficient and interconnected experience is more likely to positively affect sWOM intentions than an inefficient but interconnected experience. Furthermore, smart-shopping feelings were found to have a significant mediating effect. For experiential-oriented shoppers, the positive relationship between an efficient and interconnected experience, smart-shopping feelings and sWOM intentions was significantly strengthened.

This research contributes to the sWOM and omnichannel service experience literature by investigating the influences of seamless shopping experience types on customers’ sWOM intentions. This research also provides recommendations for designing and delivering a superior, seamless shopping experience for omnichannel shoppers.