Purpose The purpose of this paper is to answer the question of whether smartwatches will survive and gain their own niche within the consumer electronics market. Based on the stimulus–organism–response (S–O–R) framework, this study identifies and validates the impacts of both technological and fashion-related factors (interactivity, autonomy, visual aesthetics and self-expression) on product attachment towards smartwatches through user satisfaction and pleasure derived from their smartwatches. Design/methodology/approach The authors collected the survey data via online surveys from 198 respondents and tested measurement and structural models with the partial least square technique. Findings The authors found that both technological characteristics (interactivity and autonomy) and fashion-related characteristics (visual aesthetics and self-expression) have an impact on product attachment through pleasure. Research limitations/implications Several other important characteristics of traditional wrist-watches such as durability or workmanship are not considered in this study, but should be included in future studies. The three-item measure of autonomy may be insufficient for more sophisticated wearable devices in the future. In future studies, the impact of product attachment on users’ continued usage should be examined. Practical implications This study provides important practical implications for smartwatch makers interested in product development, as users were found to consider fashion-related characteristics to be as important as technological characteristics. Originality/value This study is the first study that considers both aesthetic and technological factors for IT acceptance in the context of wearable devices. Also, instead of traditional IT acceptance measures such as continued use, this study investigates users’ product attachment, which is more relevant to the case of wearable devices.

Iatrogenic injuries due to intramuscular (IM) injection, although less frequently reported than before, are still common. The sciatic nerve is the most commonly injured nerve because of an IM injection owing to its large size and the buttock being a common injection site. Iatrogenic injury to the sciatic nerve resulting from a misplaced gluteal IM injection is a persistent problem worldwide affecting patients in economically rich and poor countries alike. The consequences of sciatic nerve injection injury (SNII) are potentially devastating and may result in serious neurological and medico-legal problems. A 68-year-old male presented with intractable neuropathic pain from SNII that occurred during gluteal IM injection of an analgesic for post-appendectomy pain. This chronic SNII pain did not improve despite his gradual recovery from weakness in the left foot. Partial improvement was seen following an external neurolysis, performed three months post-appendectomy. SNII is a preventable complication of gluteal IM injection. While the complete avoidance of gluteal IM injection is desirable, should need arise, the use of an appropriate administrative technique is recommended.

Abstract. Nowadays, automated Web document classification is considered as an important method to manage and process an enormous amount of Web documents in digital forms that are extensive and constantly increasing. Recently, document classification has been addressed with various classified techniques such as naïve Bayesian, TFIDF (Term Frequency Inverse Document Frequency), FCA (Formal Concept Analysis) and MCRDR (Multiple Classification Ripple Down Rules). We suggest the BayesTH-MCRDR algorithm for useful new Web document classification in this paper. We offer a composite algorithm that combines a naïve Bayesian algorithm using Threshold and the MCRDR algorithm. The prominent feature of the BayesTH-MCRDR algorithm is optimisation of the initial relationship between keywords before final assignment to a category in order to get higher document classification accuracy. We also present the system we have developed in order to demonstrate and compare a number of classification techniques.

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