Purpose The purpose of this paper is to develop a theoretical model based on Push–Pull–Mooring (PPM) framework consisting of direct, indirect and moderating effects, derived from technology acceptance model, unified theory of acceptance and use of technology and other extended theory, to address the main factor influencing an individual in adopting mobile payment (MP) in physical stores. The research, therefore, utilized individual switching behavior as an underpinning to explain MP adoption in an offline context. Design/methodology/approach The theoretical model was tested by collecting data from 459 respondents in Indonesia through online self-administered questionnaires. Findings The finding indicated consumer innovativeness has the most influential direct effect on MP adoption, followed by deal proneness, perceived convenience and perceived herd behavior. Meanwhile, perceived enjoyment and subjective norms were found to have an indirect effect on the adaptation of MP through mediator convenience. Furthermore, age, gender, occupation and income did not have any moderating effect for all the direct influence of MP adoption. Originality/value Previous literature only focused on direct intention. However, this study observed the adoption of MP in a physical store by involving the switching behavior. It specifically puts concern and objective as the factors that influence user intention to switch from their old payment system to the MP system in bricks and mortar store using PPM framework.

Purpose This study aims to propose a theoretical model to explain mobile payment (MP) continuance usage in a physical store in Indonesia from a habit perspective. In detail, continuance usage was argued to be a consequence of habitual behavior which is related to specific actions conducted automatically, repeatedly and frequently. Therefore, the theoretical model was constructed on the theory of habit establishment. Design/methodology/approach In total, 220 Indonesian respondents were used to examine the theoretical model. Furthermore, a cross-sectional study was used through the use of a descriptive statistical approach to preparing data and descriptive analyses and structural equation modeling method for analysis. Findings Satisfaction was found to have the most substantial direct influence on the establishment of habit to use MP followed by perceived usefulness and perceived compatibility. Meanwhile, deal proneness and social ties were discovered to have a significant indirect effect on habit through the mediation of usefulness. Originality/value This study used the theory of habit formation to understand how user develops repeated behavior in MP usage which leads to continuance usage of the platform. There is limited explicit exploration and development of a theory based on this concept, therefore, this study is a contribution to the body of knowledge with respect to habit formation and its impacts on MP continuance usage.

Wildlife trade is one of the main factors causing endangered bird species. In Indonesia, trade has caused 28 bird species to be classified in the endangered bird category. Protection efforts have been made with the establishment of 564 species of Indonesian birds as protected birds. For law enforcement, certainty is needed in the identification of these bird species. This study begins with a Forum of Discussion Groups from relevant institutions in Java and Bali to determine the types of protected birds that are prioritized to developed in this application. Based on the results of the Forum of Discussion Group, a bird photo dataset compiled using 17 categories or types of bird photos as prioritized in this study. The method used in this study is the Convolutional Neural Network (CNN) method, which combined the structure of MobileNet and the weight of the network that has previously trained using ImageNet. The results of this study are the differences of results between CNN standards and those combined with the structure of MobileNet. For better accuracy, using the CNN standard, which is around 98.38% for the accuracy of the training, while in terms of size, combined with MobileNet has a relatively smaller model size, which is 68 megabytes.

Purpose This study aims to build a prototype of a smart waste recycling bin to transform organic waste into liquid fertilizer. The internet of things (IoT) was used as a base to develop this bin to offer a recycling system that convenient to the household. Design/methodology/approach In general, this system will integrate a microcontroller and several sensors that able to be controlled by a smartphone app to manage the decomposition process of organic waste in the bin. In the end, black-box testing was conducted to ensure all hardware and software that construct the system can perform well as expected. Findings All the validation testing reveals all the integration of hardware and software that constructs the smart bin satisfied the performance requirement except for the real-time clock sensor that implies the slight error for a few seconds compares to the actual time. Originality/value Different from the previous works, this study focused on the involvement of society to participate in the recycling garbage process by designing the smart waste recycling bin system that fits to locate in the household environment, which allows users to monitor the fertilizer making process using IoT technology.

One of the essential things in research engaged in the field of Computer Vision is image classification, wherein previous studies models were used to classify an image. Javanese Letters, in this case, is a basis of a sentence that uses the Javanese language. The problem is that Javanese sentences are often found in Yogyakarta, especially the use of name tourist attractions, making it difficult for tourists to translate these Javanese sentences. Therefore, in this study, we try to create a Javanese character classification model hoping that this model will later be used as a basis for developing research into the next stage. One of the most popular methods lately for dealing with image classification problems is to use Deep Learning techniques, namely using the Convolutional Neural Network (CNN) method using the KERAS framework. The simplicity of the training model and dataset used in this work brings the advantage of computation weight and time. The model has an accuracy of 86.68% using 1000 datasets and conducted for 50 epochs based on the results. The average inference time with the same specification mentioned above is 0.57 seconds, and again the fast inference time is because of the simplicity of the model and dataset toolbar. This model's advantages with fast and light computation time bring the possibility to use this model on devices with limited computation resources such as mobile devices, familiar web server interface, and internet-of-things devices.