Responding to Online Reviews in Competitive Markets: A Controlled Diffusion Approach

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Yang, Mingwen; Zheng, Zhiqiang (Eric); Mookerjee, Vijay; Chen, Hongyu

Abstract: We study how firms respond to online customer reviews in a competitive market w ... Expand

Abstract: We study how firms respond to online customer reviews in a competitive market where they jostle with one another for sales based on online ratings. The focus of this paper is on how firms can optimally manage their ratings through management response and how review ratings affect the sales and profits of competing firms. We develop a controlled diffusion process to model the coevolution of sales and ratings as a function of the response strategy chosen to maximize profit over time. Our model considers a variety of factors, such as profit margin and customer rating sensitivity, that influence a firm's effort to manage ratings and subsequently its sales and profits. More response effort needs to be exerted to manage ratings when either the profit margin of a tour is very high or customers are very sensitive to ratings. We estimate our model using data on Ctrip's tours that include each tour's sales, reviews, prices, and tour features. We find that consumers anchor their beliefs in the mean market rating and that their purchase decisions depend on the tour's rating relative to this anchor. Thus, relative, rather than absolute, ratings matter. Our study informs firms on how competition and other primitives impact their efforts to manage ratings and hence profit. Our methodology allowed us to conduct "what-if" analyses, for example, to study what would happen to the review ratings, sales, and profits of a tour if a firm adopted a different response strategy. We were also able to provide turnaround strategies for struggling tours, i.e., factors that a loss-making tour should change if it wishes to make a positive profit. Ultimately, we conducted a competitive analysis that allowed us to modify certain parameters that affect the intensity of competition and hence the sales and the profits of competing tours. Finally, we demonstrate the flexibility of the model by extending it to incorporate multiple state variables that might affect the response strategy. Collapse

Topics: application programming interface online learning system Twitter innovation management service quality

Methods: longitudinal research time series analysis generalized autoregressive conditional heteroskedasticity model natural language processing autoregressive moving average model

Theories: co-evolution theory

Bitcoin price forecasting: A perspective of underlying blockchain transactions

2021 | Decision Support Systems | Citations: 7

Authors: Guo, Haizhou; Zhang, Dian; Liu, Siyuan; Wang, Lei; Ding, Ye

Abstract: Cryptocurrency price forecasting plays an important role in financial markets. T ... Expand

Abstract: Cryptocurrency price forecasting plays an important role in financial markets. Traditional approaches face two challenges: (1) it is difficult to ascertain the influential factors related to price forecasting; and (2) due to the 24/ 7 trading policy, cryptocurrencies’ prices face very large fluctuations, thus weakening the forecasting power of traditional models. To address these issues, we focus on Bitcoin and identify the influential factors related to its price forecasting from the perspective of underlying blockchain transactions. We then propose a price forecasting model WT-CATCN, which leverages Wavelet Transform (WT) and Casual Multi-Head Attention (CA) Temporal Convolutional Network (TCN), to forecast cryptocurrency prices. Our model can capture important positions of input sequences and model the correlations among different data features. Using real-world Bitcoin trading data, we test and compare WT-CATCN with other state-of-the-art price forecasting models. The experiment results show that our model improves the price forecasting performance by 25%. Collapse

Topics: Bitcoin cryptocurrency blockchain Google+ competitive advantage

Methods: experiment longitudinal research autoregressive integrated moving average computational algorithm deep learning

“Stonks to the Moon”: Evidence from Reddit posts and Corresponding Market Manipulation

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Padalkar, Nakul

Abstract: BloombergBusinessweek is calling the $GME rally a bonkers manipulation of the s ... Expand

Abstract: BloombergBusinessweek is calling the $GME rally a bonkers manipulation of the stock market by smalltime traders. This paper analyses more than 1.1 million Reddit comments and posts belonging to a few subreddits like r/wallstreetbets, r/stocks, r/stockmarkets, r/options about the 'meme stocks' on the New York stock exchange (NYSE) and Chicago Board of Options Exchange (CBOE) and evaluate their impact on stock price, volume, and volatility. We measure the Bullishness of given stocks using text mining and semantic analysis methods and utilize Wall Street Journal news stories as controls. The preliminary finding suggests strong evidence of predicting market volatility. We believe that the higher Reddit sentiment indicates higher stock volume, and the high frequency of posts (representing agreement) affects stock returns, traded volume, and volatility. We hope to show that social medial message boards, like Reddit, can be used as a reliable source to predict returns, volatility, and volume of the commodities traded on the stock market. Collapse

Topics: Reddit application programming interface web forum price management

Methods: natural language processing longitudinal research computational algorithm machine learning method econometric modeling

Theories: financial theory behavioral economics

Information Asymmetry on Cryptocurrency Trading: The Spillover Effect and Quality of Social Sentiment.

2021 | Americas Conference on Information Systems | Citations: 0

Authors: kim, kwansoo; DAIDJ, Nabyla

Abstract: This study examines whether social sentiment can influence information asymmetr ... Expand

Abstract: This study examines whether social sentiment can influence information asymmetry in cryptocurrency markets, and if so, in what way and its qualitative effect? Using a data set on social sentiment by crawling Twitter and trading information on an exchange in Korea. We empirically study the linkage between social sentiment and asymmetric information in two cryptocurrency trading settings. Our results highlight the differentiated impact of positive and negative sentiment variance on information asymmetry according to 1) the spillover effects and 2) the leverage effect of social news characterizing social sentiment. However, the quality of the social sentiment related information is less valuable. Our findings contribute to the growing IS literature on the social media linked to Fintech as well as to the study of verifiability in the cryptocurrency market. Collapse

Topics: cryptocurrency social media Twitter Bitcoin communication service infrastructure

Methods: autoregressive conditional heteroscedasticity model generalized autoregressive conditional heteroskedasticity model sentiment analysis longitudinal research time series analysis

Theories: lemon market theory

Electric Vehicle Charging Decisions using Only Market Trends with Persistence

2020 | HICSS | Citations: 0

Authors: Hammerstrom, Donald J; Pratt, Richard M

Abstract: Electric vehicle (EV) charging can take advantage of real-time electricity mark ... Expand

Abstract: Electric vehicle (EV) charging can take advantage of real-time electricity market price volatility. Presuming that an EV must be fully charged at a future target time, the EV should choose to charge using the lowest future electricity prices and thereby minimize electricity cost. Statistical methods must be used if forward prices are unavailable. In this case, historical prices and trends must be mined to anticipate which prices should be used to charge the EV. Price persistence, a tendency for electricity prices to inexplicably become and remain relatively high or low for extended durations, is particularly difficult to forecast and mitigate. This paper formulates and tests a pragmatic strategy for integrating conventional static statistical prices and the Bayesian propagation of price persistence from the current price to prices in the current and future hours. Simulations were conducted to test the cost effectiveness of charging strategy using real-time electricity prices. 1 Collapse

Topics: price management programming language website

Methods: simulation longitudinal research autoregressive integrated moving average autocorrelation analysis autoregressive conditional heteroscedasticity model

Measuring extreme risk of sustainable financial system using GJR-GARCH model trading data-based

2020 | International Journal of Information Management | Citations: 0

Authors: Ma, Xiaomeng; Yang, Ruixian; Zou, Dong; Liu, Rui

Abstract: This paper investigates the role of gold as a safe haven for stock markets and t ... Expand

Abstract: This paper investigates the role of gold as a safe haven for stock markets and the US dollar by examining the extreme risk spillovers. The extreme risk is measured by Value at Risk (VaR), which is estimated by GJR-GARCH model based on skewed t distribution. Two test statistics of one-way and two-way Granger causality in risk are used to detect extreme risk spillovers. In general, the empirical results show that there are negative extreme risk spillovers between gold and stock markets and between gold and foreign exchange markets of US dollar, which indicate that gold can act as an effective safe haven against extreme stock and US dollar exchange rate movements. In addition, the global financial crisis can affect the safe haven role of gold. Collapse

Topics: information management innovation management database system

Methods: Granger causality generalized autoregressive conditional heteroskedasticity model longitudinal research time series analysis cluster analysis

The Voice of the Customer: Managing Customer Care in Twitter

2020 | Information Systems Research | Citations: 6

Authors: Mousavi, Reza; Johar, Monica; Mookerjee, Vijay S.

Abstract: In recent years, managing customer sentiment—particularly on social media—has be ... Expand

Abstract: In recent years, managing customer sentiment—particularly on social media—has become crucial as more customers use social media to seek help from firms. Therefore, we strive to determine an optimal strategy to manage customer sentiment on social media sites such as Twitter. We also aim to identify factors and external events that can influence the effectiveness of customer care. To understand the antecedents of digital customer care, we model a diffusion process of customer sentiment over time. This diffusion process is influenced (or controlled) by the firm through the strategy employed to respond to customer tweets. We then use real data consisting of sentiments expressed by customers directed at Twitter’s service accounts of four major U.S. telecommunication-service providers (AT&T, Verizon, Sprint, and T-Mobile) to estimate the parameters in our analytical model and shed several insights into digital customer care in this industry. First, we find a clear separation among the firms in terms of digital customer care effectiveness. Second, we find that good customer care is not merely a matter of responding to customer tweets. Third, the quality of digital customer care that customers expect varies across firms: Customers of higher priced firms (e.g., Verizon and AT&T) expect better customer care. Fourth, seemingly unrelated events (such as signing an exclusive contract with a celebrity) can impact digital customer care. Our study has important implications for managers as it can help firms determine the optimal strategy to influence customer sentiment. Collapse

Topics: Twitter customer service application programming interface social media Apple iPhone

Methods: sentiment analysis longitudinal research stochastic model simulation generalized autoregressive conditional heteroskedasticity model

Longitudinal healthcare analytics for disease management: Empirical demonstration for low back pain

2020 | Decision Support Systems | Citations: 0

Authors: Mueller-Peltzer, Michael; Feuerriegel, Stefan; Molgaard Nielsen, Anne; Kongsted, Alice; Vach, Werner; Neumann, Dirk

Abstract: Clinician guidelines recommend health management to tailor the form of care to t ... Expand

Abstract: Clinician guidelines recommend health management to tailor the form of care to the expected course of diseases. Hence, in order to decide upon a suitable treatment plan, health professionals benefit from decision support, i.e., predictions about how a disease is to evolve. In clinical practice, such a prediction model requires interpretability. Interpretability, however, is often precluded by complex dynamic models that would be capable of capturing the intrapersonal variability of disease trajectories. Therefore, we develop a cross-sectional ARMA model that allows for inference of the expected course of symptoms. Distinct from traditional time series models, it generalizes to cross-sectional settings and thus patient cohorts (i.e., it is estimated to multiple instead of single disease trajectories). Our model is evaluated according to a longitudinal 52-week study involving 928 patients with low back pain. It achieves a favorable prediction performance while maintaining interpretability. In sum, we provide decision support by informing health professionals about whether symptoms will have the tendency to stabilize or continue to be severe. Collapse

Topics: decision support decision making healthcare data missing data personalization

Methods: cross sectional research longitudinal research time series analysis autoregressive moving average model generalized autoregressive conditional heteroskedasticity model

Cryptocurrency price prediction based on multiple market sentiment

2020 | HICSS | Citations: 0

Authors: Wang, Yu; Chen, Runyu

Abstract: With the rapid development of the Internet, cryptocurrencies have been gaining i ... Expand

Abstract: With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically. As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market. Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions. However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult. Collapse

Topics: cryptocurrency social media Tether Ethereum Bitcoin

Methods: computational algorithm experiment deep learning support vector machine machine learning

RISK MITIGATION CAPABILITY OF FLEXIBILITY PERFORMANCE CONTRACTS FOR DEMAND RESPONSE IN ELECTRICITY SYSTEMS

2019 | European Conference On Information Systems | Citations: 0

Authors: Jã, Florian

Abstract: The transition of the energy system increases the urgency to cope with the inter ... Expand

Abstract: The transition of the energy system increases the urgency to cope with the intermittency of renewable energy sources to keep the electricity network balanced. Demand Response (DR) measures are a promising approach to align the electricity consumption, especially of industrial consumers, with current electricity supply. While adequate information systems (IS) are already in place to dynamically adapt electricity consumption patterns, industrial consumers are still reluctant to implement DR measures due to uncertainty of their financial performance. Nevertheless, studies on risk transfer instruments related to DR investments are still scarce. To contribute to the closure of this research gap, we examine the risk transfer capability of Flexibility Performance Contracts (FPC). We derive cash flow structures for representative FPC designs, calculate risk premiums and enable the comparison of corresponding risk profiles. Presented FPCs are evaluated based on a real-world industrial use case. Thereby, the financial performance is modelled stochastically, taking electricity price fluctuation, industrial process characteristics and IS-backed decisions into account. Our results reveal that FPCs represent well-suited risk transfer instruments for DR measures. Thus, FPCs have the potential to accelerate the application of DR measures and therefore to complement existing capabilities of IS in the context of electricity networks. Collapse

Topics: energy information system financial performance decision support price management moral hazard

Methods: simulation generalized autoregressive conditional heteroskedasticity model longitudinal research autoregressive moving average model case study

Theories: transaction cost economics