Antecedents and Consequences of Digital Shadow Work in Mobile Shopping Apps Context

Shadow work continues to witness a significant uptick in the context of mobile shopping. Therefore, we question whether shadow work perceived by mobile shoppers may become a bigger problem, create fatigue for mobile shoppers, and lead them to discontinue the use of mobile shopping apps. This study examines the relationship between shadow work and the discontinuance of mobile shopping apps. Data from a total of 266 completed surveys were collected by a market research firm. We adopted partial least squares structural equation modeling (PLS-SEM) to assess both the measurement and structural components of the model. The results show that both information overload and system feature overload positively influence individuals’ shadow work. This study explores the concept of shadow work in the context of mobile shopping apps. Specifically, the study developed the relationships between the antecedents and consequences of shadow work in the mobile shopping context. The main contribution of our study is that it introduces an integrative model of shadow work in the mobile shopping context, highlighting the importance of shadow work.

Emotionally Intelligent Chatbot

An emotionally intelligent chatbot system aims to make an effective conversation between humans and machines in as natural and interactive a manner as possible. The chatbot agent has the pre-embedded knowledge base to identify the sentences, intents, entities, and context of the input query to be precise for making a valid, predictable decision itself as a self-generated response to answer the query. The present technical project consists of developing an intelligent system for college enquiry purposes using a web-based chatbot agent, through machine learning, query processing, and sentiment and emotion classification system to analyze the sentiment of the visitor towards the college. Emotionally Intelligent College Enquiry Chatbot System is nothing but a chatbot to understand the user queries and respond to them during a conversation. A chatbot can actively help humans to involve in a digital automated conversation with a machine or a system effective. In the following proposed system, feature extraction and data cleaning techniques are applied to the dataset, and classifiers such as multinomial naive Bayes, logistic regression and k nearest neighbours are used to train the model. The classifier with the highest accuracy is further used for the emotion classification of users.

The Prescription Drug Market

This chapter describes how prescription drug markets deviate from “normal” competitive markets. On the demand side, there is considerable uncertainty in disease prognosis and treatment effects; buyers have much less information than sellers; consumers usually do not pay directly for their prescriptions but rather have health insurance, which protects them from a drug’s full price; and physicians and insurers play a major role in consumer choices. On the supply side, firms are heavily regulated, subject to laws requiring that drugs undergo extensive testing before entering the marketplace. Monopolies are a system feature, designed to incentivize companies to invest in expensive and risky drug discovery by providing the prospect of a big payoff if the investment succeeds. Patents and market exclusivity restrict competition and provide drug developers considerable pricing power. The challenge for policymakers is how to achieve reasonable or fair drug prices in light of these market distortions.

iMER-FM: Iterative Process of System Feature Model Extraction from the Requirements

Software product line engineering (SPLE) is a paradigm to promote systematic software reuse. A Feature Model (FM) is a common means to illustrate the commonality and variability of software products in a family. In most existing FM extraction approaches, keywords in the requirement document or certain types of system behavior or external events are considered features. The resulting FM is a combination of user activities and system actions (SAs), making it hard to understand. In this paper, we present an automatic approach to generate a product line FM from multiple requirement documents. We consider user activity and SAs separately in our approach and focus on the expected behaviors of the software system, together with the data being processed. The resulting FM clearly illustrates the expected functionalities of the software system and their variability in the product line. We also compared our approach with existing techniques by processing the same textual documents, and noted improvements in our results.