Editorial: the “nip and tuck” service framework: bright and dark side of beautification/modification services

Purpose The purpose of this study is to propose a conceptual framework of beautification/modification services, to introduce the special issue on the topic “Mirror, Mirror on the Wall! Examining the Bright and Dark Side of Face and Body Beautification/Modification Services” and to provide a future research agenda. Design/methodology/approach Building on the available literature, the authors developed the “Nip and Tuck” service framework of beautification/modification services depicting the motives, benefits and outcomes while it identifies current industry trends. Findings The authors explore the antecedents and consequences (positive and negative) of consuming face and body beautification/modification services and integrate these in the “Nip and Tuck” service framework. In the framework, the authors acknowledge the critical role of new technologies such as augmented reality apps and the internet in enabling and transforming beautification/modification services into commodities. The framework also identifies the benefits consumers seek and derive from these services while it recognizes current trends that shape the industry. The authors conclude with a set of future research directions that arise from the framework and the papers in the special issue. Practical implications The attained insights are useful to managers of beautification and modification services seeking to understand and satisfy their customers’ needs while securing their well-being. Social implications Understanding the role of beautification and modification services in consumers’ well-being is essential for business managers and policymakers. Originality/value The authors propose a novel, “Nip and Tuck” framework of face and body beautification/modification services and its key antecedents and consequences while considering both their bright and dark sides.

An Approach towards IoT-Based Predictive Service for Early Detection of Diseases in Poultry Chickens

The poultry industry contributes majorly to the food industry. The demand for poultry chickens raises across the world quality concerns of the poultry chickens. The quality measures in the poultry industry contribute towards the production and supply of their eggs and their meat. With the increasing demand for poultry meat, the precautionary measures towards the well-being of the chickens raises the concerns of the industry stakeholders. The modern technological advancements help the poultry industry in monitoring and tracking the health of poultry chicken. These advancements include the identification of the chickens’ sickness and well-being using video surveillance, voice observations, ans feces examinations by using IoT-based wearable sensing devices such as accelerometers and gyro devices. These motion-sensing devices are placed over a chicken and transmit the chicken’s movement data to the cloud for further analysis. Analyzing such data and providing more accurate predictions about chicken health is a challenging issue. In this paper, an IoT based predictive service framework for the early detection of diseases in poultry chicken is proposed. The proposed study contributes by extending the dataset through generating the synthetic data using Generative Adversarial Networks (GAN). The experimental results classify the sick and healthy chicken in a poultry farms using machine learning classification modeling on the synthetic data and the real dataset. Theoretical analysis and experimental results show that the proposed system has achieved an accuracy of 97%. Moreover, the accuracy of the different classification models are compared in the proposed study to provide more accurate and best performing classification technique. The proposed study is mainly focused on proposing an Industrial IoT-based predictive service framework that can classify poultry chickens more accurately in real time.

Introducing an Intelligent Goods Service Framework

With the increasing diffusion of Internet of Things (IoT) technologies, the transportation of goods sector is in a position to adopt novel intelligent services that cut across the otherwise highly fragmented and heterogeneous market, which today consists of a myriad of actors. Legacy systems that rely upon direct integration between all actors involved in the transportation ecosystem face considerable challenges for information sharing. Meanwhile, IoT based services, which are designed as devices that follow goods and communicate directly to cloud-based backend systems, may provide services that previously were not available. For the purposes of this paper, we present a theoretical framework for classification of such intelligent goods systems based on a literature study. The framework, labelled as the Intelligent Goods Service (IGS) framework, aims at increasing the understanding of the actors, agents, and services involved in an intelligent goods system, and to facilitate system comparisons and the development of new innovative solutions. As an illustration of how the IGS framework can be used and contribute to research in this area, we provide an example from a direct industry–academia collaboration.