The dark side of innovation: How new SKUs affect brand choice in the presence of consumer uncertainty and learning

New product activity is critical for sustained success of consumer packaged goods (CPG) brands. However, the impact of new SKUs on the perceived quality, quality uncertainty and subsequent choice of the brand as a whole is, as of yet, not well understood. The authors study how new additions to the brand line shape consumers’ quality perceptions, and how this – next to the mere line length effect – influences their choice of brands over time. They do so in the setting of an emerging market (China), where new product activity is particularly pervasive. Using a unique scanner panel dataset of Chinese households over the period 2011–2014, they estimate a Bayesian learning model that accommodates varying quality, on two CPG categories, and for two types of new-product additions (new sensory SKUs vs. new non-sensory SKUs). They show that while adding new SKUs may lift the brand’s perceived quality level, it also makes consumers more uncertain about the quality of the brand – dampening their brand choice. This holds especially for light customers – an important part of the brand clientele. Managerial implications are discussed.     

Past,present, and future of green product innovation

Firms are under constant pressure from various governmental and nongovernmental agencies to switch from conventional environmentally polluting products to green product innovations (GPIs). However, the relevant research pertaining to GPI has been published in a diverse set of journals that vary in their scope and readership and, therefore, the scholarly contribution to the topic remains largely fragmented. This study has utilised a systematic literature review approach to examine the literary corpus on GPI to paint a holistic picture of its different aspects. The content and thematic analysis of 85 studies resulted in the extraction of seven key research themes: organisational capabilities, organisational learning, institutional pressures, barriers, structural changes, benefits of GPI, and methodological choices. This study's findings further highlight the various gaps in the GPI literature and raise some research questions that warrant scholarly investigation in the future. Likewise, our study has important implications for practitioners who are likely to benefit from a holistic understanding of the different aspects of GPI. Similarly, policymakers can use this study's findings to introduce policy interventions, especially in countries where GPI adoption is low.     

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.     

Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic

Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers’ needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers’ opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.     

A neural network based price sensitive recommender model to predict customer choices based on price effect

The impact of price and price changes should not be ignored while designing algorithms for predicting customer choice. Consumer preferences should be modeled with consideration of price effects. Businesses need to consider for efficient prediction of an individual's purchase behaviour. Personalized recommendation systems have been studied with machine learning algorithms. However, the price-aware personalized recommendation has received little attention. In this paper, we attempt to capture insightful economic results considered in the marketing and economics disciplines by employing modern machine learning architecture for predicting customer choice in a large-scale supermarket context. We extract personalized price sensitivities and examine their importance in consumer behaviour. The employed data collected from a supermarket chain in Germany consists of implicit feedback based on customer-product interactions and the price of every interaction. We propose a two-pathway matrix factorization (2way-MF) model that is price-aware and tries to memorize customer-product interaction's implicit feedback. The proposed models achieve better model performance than standard Matrix Factorization models widely used in the industry. The approach was re-validated with data from supermarket chain in Taiwan. Other industries can adopt the proposed framework of modeling customer's preferences based on price sensitivity. We suggest that further research and analyses could help understand the cross-price elasticities.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

Failure to learn from failure: The 2008 mortgage crisis as a déjà vu of the mortgage meltdown of 1994

This article traces the developments in the market for residential mortgage-backed securities (MBS) during the period 1970–2008. Drawing on an analysis of trade publications, business press, and interviews with practitioners, it shows that an MBS market meltdown in 1994 provided clear signals of problems with MBS. The market participants did not re-evaluate their use of risk management tools or adjust security design in response to the 1994 crisis, suggesting a lack of understanding of the implications of the crisis. The 1994 meltdown showed that MBS were vulnerable to systematic risks and that these risks could precipitate an MBS market crash. Furthermore, the 1994 meltdown demonstrated that large-scale investment in MBS could affect the primary mortgage market, thereby rendering the MBS risks unpredictable. After 1994, MBS investment shifted to MBS backed by mortgages with default risk – a development that led to the crash of 2008. By drawing parallels between the 1994 and 2008 crises, this article shows how the MBS market failed to self-correct. The results suggest that financial market participants do not always incorporate relevant information in their decision-making and that market participants have difficulties in both foreseeing the effect of financial innovations on markets and interpreting these effects.     

Mobile learning adoption for tourism education in a developing country

Since developing countries are gradually introducing mobile-based tourism education, it is a growing demand to understand the students’ intention to adopt mobile learning. The study used partial least squares-based structural equation modelling to analyse survey data from 176 questionnaires at three tourism education institutes in Bangladesh. The study contributes to the theory of planned behaviour by examining the antecedent impact of innovativeness and moderating effect of self-efficacy. Results confirmed innovativeness as a significant antecedent on the attitude–intention relationship; however, the moderating effect of self-efficacy has not been supported. The study has marketing implications for tourism education institutes and government bodies.     

The behaviour of betting and currency markets on the night of the EU referendum

We study the behaviours of the Betfair betting market and the sterling/dollar exchange rate (futures price) during 24 June 2016, the night of the EU referendum. We investigate how the two markets responded to the announcement of the voting results by employing a Bayesian updating methodology to update prior opinion about the likelihood of the final outcome of the vote. We then relate the voting model to the real-time evolution of the market-determined prices as the results were announced. We find that, although both markets appear to be inefficient in absorbing the new information contained in the vote outcomes, the betting market seems less inefficient than the FX market. The different rates of convergence to the fundamental value between the two markets lead to highly profitable arbitrage opportunities.     

Consequences of Debt Forgiveness: Strategic Default Contagion and Lender Learning

I use a unique data set of loans to small business owners to examine whether lenders face adverse consequences when they grant debt forgiveness to borrowers. I provide evidence consistent with borrowers communicating their debt forgiveness to other borrowers, who then more frequently strategically default on their own obligations. This strategic default contagion is economically large. When the lender doubles debt forgiveness, the default rate increases by 10.9% on average. Using an exogenous shock to the lender's forgiveness policy, my findings suggest that as the lender learns about the extent of borrower communication the lender tightens its debt forgiveness policy to mitigate default contagion.