External pressure or internal culture? An innovation diffusion theory account of small retail businesses’ social media use

COVID-19 put unprecedented external pressure on small businesses to adopt or increase use of social media while not all small businesses are internally ready for this rapid change. This study investigated the roles of external pressures and organizational culture of openness and learning in driving small retail business owners'/managers' social media use decisions by impacting their perceived usefulness and barriers, based on the innovation-decision process model from the diffusion of innovations theory and the theory of reasoned action. An online survey with structured measurements was administered to 411 U.S. small retail business owners/managers. Results from structural equation modeling revealed that external pressures positively influenced small business owners'/managers’ perceived usefulness and barriers and social media use intention. In addition, the culture of openness and learning positively influenced the perceived usefulness while mitigating the perceived barriers, thereby directly and indirectly influencing the social media use intention. Theoretical and managerial implications are discussed.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.     

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.     

When push comes to shove: How coach versus student athlete misconduct affects event attendance intentions

Though onand off-the-field misconduct is common among U.S. college athletic programs, little is known regarding the ramifications that may result. Drawing on social learning theory, the current research suggests consumers intentions (e.g., likelihood of attending a game) differ depending on violator's team role. Across one qualitative and five experimental studies, we demonstrate that consumers' intentions are influenced by violator's team role, such that likelihood of attending a game is lower when a coach (vs. student athlete) misbehaves, an effect driven by evaluation of the academic institution. This effect is robust across both winning and losing records and moderated by perceived fairness of the university's actions toward the violator.     

iSDS: a self-configurable software-defined storage system for enterprise

ABSTRACT

Storage is one of the most important aspects of IT infrastructure for various enterprises. But, enterprises are interested in more than just data storage; they are interested in such things as more reliable data protection, higher performance and reduced resource consumption. Traditional enterprise-grade storage satisfies these requirements at high cost. It is because traditional enterprise-grade storage is usually designed and constructed by customised field-programmable gate array to achieve high-end functionality. However, in this ever-changing environment, enterprises request storage with more flexible deployment and at lower cost. Moreover, the rise of new application fields, such as social media, big data, video streaming service etc., makes operational tasks for administrators more complex. In this article, a new storage system called intelligent software-defined storage (iSDS), based on software-defined storage, is described. More specifically, this approach advocates using software to replace features provided by traditional customised chips. To alleviate the management burden, it also advocates applying machine learning to automatically configure storage to meet dynamic requirements of workloads running on storage. This article focuses on the analysis feature of iSDS cluster by detailing its architecture and design.     

Uncertainty,story-telling and transformative learning: An instructor’s experience of TEFI’s Walking Workshop in Nepal

ABSTRACT

This article recounts three stories from TEFI’s walking workshop in Nepal: the construction of a road through what was once a trekking path; a dance-floor encounter at a Himalayan party; and the arrival of one participant, fatigued by jet lag and disoriented by the new surroundings. These stories of confusion, discomfort and fear are linked by one common theme: the potential of uncertainty to foster deep reflection and nuanced conclusions. The premise that uncertainty is to be valued and even cultivated has been explored in educational theory, spiritual traditions, and research on transformative learning. These sources affirm the role of uncertainty in the process of knowledge creation. However, accepting this role can be challenging for educators because it requires they assume a new identity, one which they may perceive as being at odds with their status as “teacher” the identity of learner. One way for the educator to address this challenge may be through recounting their own stories of uncertainty. Stories of being immersed in unfamiliar situations that challenge, confuse and even frighten – stories, in other words of being a tourist – can foster reflection on an intellectual, emotional and spiritual level, engaging the “whole” person, and thus initiating the educator/learner’s transformative journey.     

Impact of international entry choices on the nature and type of innovation: Evidence from emerging economy firms from the Indian bio-pharmaceutical industry

As emerging economy multinational enterprises (EMNEs) enter foreign countries in search of new markets, seeking to expand their knowledge bases, research on the type and nature of innovation activity is needed to address the impact of EMNEs’ choices related to international expansion. Building on prior literature on entry mode and location choices, as well as on organizational learning, we argue that how and where an EMNE expands internationally will impact the nature of its innovation. We carry out empirical analysis on a sample of 167 Indian bio-pharmaceutical firms for the period from 1997–2017. Our findings suggest that greenfield ventures foster innovation in core technologies, while cross-border mergers and acquisitions (M&As) foster innovation in non-core technologies. In addition, locating subsidiaries in high income countries encourages product innovations, while locating in low income countries encourages process innovations. Our findings contribute to the growing literature on differences in learning outcomes of EMNE internationalization.     

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.