A Sentiment Analysis Approach of Data Volatility for Consumer Satisfaction in the Fashion Industry

© 2019 IEEE. Consumer satisfaction forms a critical part of every business and directly impacts on the ability to retain customers. The ability to measure and define indexes for consumer satisfaction can be very useful for businesses as these can be used to swiftly respond to customer needs accordingly. The consumer satisfaction data for certain products exhibit extreme volatility because of their short requirement duration. Hence, it is necessary to identify present consumer satisfaction in a timely manner. This research adopts the fast fashion industry as a case study due to the high volatile nature of its social media data, among several other characteristics that influenced the decision. The research focused on investigating existing sentiment analysis techniques and the development of a novel one for the fast fashion industry based on its peculiar characteristics. This involved the development of a novel sentiment analysis framework with a sentiment scaling technique, making use of data mining strategies towards obtaining, identifying and analysing fast fashion social media data, for the identification of consumer satisfaction

Contemporary Research on Management and Business

This book contains 74 selected papers presented at the 5th International Seminar of Contemporary Research on Business and Management (ISCRBM 2021), which was organized by the Alliance of Indonesian Master of Management Program (APMMI) and held in Jakarta, Indonesia on 18 December 2021. This online conference was hosted by the Master of Management Program of Indonesia University. This year, ISCRBM focused on research related to driving sustainable business through innovation. Business has had to deal with the Covid-19 pandemic, so a new approach towards managing business to survive competition is indispensable. Innovation is the key for all organizations in surviving in the new normal and beyond. The Seminar aimed to provide a forum for leading scholars, academics, researchers, and practitioners in the business and management area to reflect on the issues, challenges and opportunities, and to share the latest innovative research and best practices. This seminar brought together participants to exchange ideas on the future development of management disciplines: human resource, marketing, operation, finance, strategic management and entrepreneurship

A Text Mining Based Approach for Mining Customer Attribute Data on Undefined Quality Problem

Understanding how the consumer perceives quality is a key issue in supply chain management. However, as the market structure continues to deepen, traditional evaluation methods using SEVRQUAL are unable identify all issues related to customer quality and unable to supply solutions. The maturation of data mining technology, however, has opened the possibilities of mining customer attribute data on quality problems from unstructured data. Based on the consumer perspective, this research uses an unsupervised machine learning text mining approach and the Recursive Neural Tensor Network to resolve the attribution process for undefined quality problems. It was found that the consumer quality perception system has a typical line-of-sight that can assist consumers quickly capture the logical structure of the quality problem. Although attributions related to quality problems are very scattered, a highly unified view was found to exist within each group, and a strategy to solve the undefined quality problem was agreed through group consensus by 61% of the consumers

How do Securities Laws Influence Affect, Happiness, & Trust?

This Article advocates that securities regulators promulgate rules based upon taking into consideration their impacts upon investors\u27 and others\u27 affect, happiness, and trust. Examples of these impacts are consumer optimism, financial stress, anxiety over how thoroughly securities regulators deliberate over proposed rules, investor confidence in securities disclosures, market exuberance, social moods, and subjective well-being. These variables affect and are affected by traditional financial variables, such as consumer debt, expenditures, and wealth; corporate investment; initial public offerings; and securities market demand, liquidity, prices, supply, and volume. This Article proposes that securities regulators can and should evaluate rules based upon measures of affect, happiness, and trust in addition to standard observable financial variables. This Article concludes that the organic statutes of the United States Securities and Exchange Commission are indeterminate despite mandating that federal securities laws consider efficiency among other goals. This Article illustrates analysis of affective impacts of these financial regulatory policies: mandatory securities disclosures; gun-jumping rules for publicly registered offerings; financial education or literacy campaigns; statutory or judicial default rules and menus; and continual reassessment and revision of rules. These regulatory policies impact and are impacted by investors\u27 and other people\u27s affect, happiness, and trust. Thus, securities regulators can and should evaluate such affective impacts to design effective legal policy

Coping with demand volatility in retail pharmacies with the aid of big data exploration

Data management tools and analytics have provided managers with the opportunity to contemplate inventory performance as an ongoing activity by no longer examining only data agglomerated from ERP systems, but also, considering internet information derived from customers' online buying behaviour. The realisation of this complex relationship has increased interest in business intelligence through data and text mining of structured, semi-structured and unstructured data, commonly referred to as "big data" to uncover underlying patterns which might explain customer behaviour and improve the response to demand volatility. This paper explores how sales structured data can be used in conjunction with non-structured customer data to improve inventory management either in terms of forecasting or treating some inventory as "top-selling" based on specific customer tendency to acquire more information through the internet. A medical condition is considered - namely pain - by examining 129 weeks of sales data regarding analgesics and information seeking data by customers through Google, online newspapers and YouTube. In order to facilitate our study we consider a VARX model with non-structured data as exogenous to obtain the best estimation and we perform tests against several univariate models in terms of best fit performance and forecasting

Emotional Impact Analysis in Financial Regulation: Going Beyond Cost-Benefit Analysis

This Article advocates that financial regulators analyze, measure, and take into account the emotional impacts of their policies and procedures. Examples of emotional impacts are investor confidence, process concerns, and overall market or social mood. Investor confidence or trust in securities markets, process concerns about how much securities regulators actually deliberate over proposed rules, and financial anxiety or investment stress affect and are affected by financial economic variables, such as consumer debt, consumer expenditures, consumer wealth, corporate investment, initial public offerings, and securities market demand, liquidity, prices, supply, and volume. Cost-benefit analysis does not quantitatively consider interdependencies between regulations’ emotional impacts and their financial outcomes. Emotional impact analysis does. This Article addresses general conceptual and measurement issues about emotional impact analysis. Because financial regulations affect investors’ confidence, process concerns, and social moods, this Article analyzes how financial regulators can quantitatively analyze emotional impacts of their regulations.

On Volatility, Outliers, and Uncertainty

This dissertation is composed of three loosely related chapters, all of which are empirical.In Chapter 1, I examine whether expectations are formed in a systematically different manner during periods of low volatility versus periods of high volatility. I achieve this by measuring non-linearities in relationship between the SP 500 and the VIX across different market regimes. Three distinct market regimes are identified through a Markov Process, allowing for the capture of non-constant behavior in the relationship between contemporaneous price changes and future volatility expectations. The results indicate that the effect of the underlying asset on the supply and demand dynamics of its derivative is strongest during periods of low volatility and weakest during periods of high volatility. The decrease in magnitude of the SP 500 coefficient as the market switches from low volatility to high, suggests that information scarcity (low volatility) makes additional data (price changes) more impactful. Measures to limit market volatility may make market participant prone to expect changes in the state of the system. The purpose of Chapter 2 is to draw inference from the tail behavior of financial market price volatility in order to compare and contrast volatility expectations with volatility realizations. In doing so, I discuss the implications of slowly decaying tails as they relate to systems susceptible to unpredictable and consequential events. In such cases where fat tails are identified, typical values such as the average and variance, do not properly characterize the risk and unpredictability of the dynamic process under study. Prior research has identified asset prices and asset volatility as being drawn from a power law distribution. This paper aims to quantitatively confirm this characterization, specifically for market volatility. Further, this paper identifies whether or not volatility expectations exhibit similar power law characteristics. Goodness of fit and log likelihood tests indicate that most realized volatility series are plausibly drawn from a power- law distribution. However, none of the studied implied volatility series show evidence of power-law behavior, suggesting that risk premia may exist for lower levels of volatility but does not scale proportionally to the more extreme crisis events. That is, risk premia does not scale proportionally as values move farther into the tail. In Chapter 3, co-authored with Minh Pham, we investigate how economic uncertainty, specifically stock market uncertainty, correlates to individuals\u27 life-satisfaction. Using expected price volatility (VIX) as our anticipatory indicator and life-satisfaction as our measure of utility, our hypothesis is built on the Anticipatory Utility framework, which suggests that people also derive utility from their beliefs. After accounting for associations with the unemployment rate and stock ownership, we find a positive relationship between the VIX and low self-reported life- satisfaction. This analysis captures the contemporaneous effects of future beliefs and indicates that economic sentiment about the future plays an important role in individuals\u27 feelings about the present. This work was inspired by a desire to understand the economic crises that redirect and ultimately redefine our socioeconomic lives, as individuals and as nations. I began my economic studies during one of the most profound crises in recent history, the global financial crisis of the late 2000s. Here again in 2021, as my studies conclude, economies grapple with another, albeit different crisis. Both the Covid-19 pandemic and the subprime financial crisis highlight a salient fact; we never really know when, why, or from where such extreme events arrive. But they do, and do so more frequently than we like or predict. Each of the chapters presented in this dissertation seek to understand the ways in which we anticipate and interact with a characteristic marker of economic and financial crises, uncertainty

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

Is Your Brand Going Out of Fashion? A Quantitative, Causal Study Designed to Harness the Web for Early Indicators of Brand Value

Can Internet search query data be a relevant predictor of financial measures of brand value? Can Internet search query data enrich existing financial measures of brand valuation tools and provide more timely insights to brand managers? Along with the financial based motivation to estimate the value of a brand for accounting purposes, marketers desire to show “accountability” of marketing activity and respond to the customer’s perception of the brand quickly to maintain their competitive advantage and value. The usefulness of the “consumer information processing” framework for brand, consumer and firm forecasting is examined. To develop our hypotheses, we draw from the growing body of work relating web searches to real world outcomes, to determine if a search query for a brand is causal to, and potentially predictive of brand, consumer and firm value. The contribution to current literature is that search queries can predict perception, whereas previous research in this nascent area predicted behavior and events. In this direction, we propose arguments underpinning this research as follows: the theoretical background relative to brand valuation and the theoretical frame based on an in-depth review of how scholars have used search query data as a predictive measure across several disciplines including economics and the health sciences. From a practitioner perspective, unlike traditional valuation methods search query data for brands is more timely, actionable, and inclusive