This research investigates online consumer behavior in an e-commerce context with a focus on consumer online shopping cart use and subsequent cart abandonment. A model rooted in the Uses and ...Gratifications Theory, the Unified Theory of Acceptance and Use of Technology, and the concept of the purchase funnel is developed to explain the predicted relationships. Empirical findings based on clickstream data show that returning to an existing cart increases the subsequent cart use and decreases cart abandonment. Conversely, viewing clearance pages and viewing a large number of product reviews increases both cart use and cart abandonment. Browsing product pages decreases cart use, and increases cart abandonment. The moderating role of smartphone-based shopping is also examined, with the moderating effects primarily occurring early in the purchase funnel affecting cart use, and influencing cart abandonment to a smaller degree. Theoretical contributions and managerial implications for digital marketers are provided.
•Deep neural networks (cf. shallower architectures) better predict stock indices.•A rectifier linear (cf. tanh) activation function better predicts stock indices.•Relative predictive accuracy of deep ...neural networks peaks with increasing data.
Many existing learning algorithms suffer from limited architectural depth and the locality of estimators, making it difficult to generalize from the test set and providing inefficient and biased estimators. Deep architectures have been shown to appropriately learn correlation structures in time series data. This paper compares the effectiveness of a deep feedforward Neural Network (DNN) and shallow architectures (e.g., Support Vector Machine (SVM) and one-layer NN) when predicting a broad cross-section of stock price indices in both developed and emerging markets. An extensive evaluation is undertaken, using daily, hourly, minute and tick level data related to thirty-four financial indices from 32 countries across six years. Our evaluation results show a considerable advantage from training deep (cf. shallow) architectures, using a rectifier linear (RELU) activation function, across all thirty-four markets when ‘minute’ data is used. However, the predictive performance of DNN was not significantly better than that of shallower architectures when using tick level data. This result suggests that when training a DNN algorithm, the predictive accuracy peaks, regardless of training size. We also examine which activation function works best for stock price index data. Our results demonstrate that the RELU activation function performs better than TANH across all markets and time horizons when using DNN to predict stock price indices.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We model the effect of online information search across mobile (smartphone and tablet) and nonmobile (personal computer PC, both desktop and laptop) platforms on frequency of purchasing per online ...shopping session. Using clickstream data from a multinational retailer, we find that device modality drives purchase frequency, likely due to the differential ease of use of PCs, tablets, and smartphones. In particular, frequency of completed orders is highest when information search and purchase completion are highly convenient, such as when shopping via tablet. We also determine that information search in the form of reading online product reviews has no effect on mobile platforms, while it does on other platforms. These findings contribute to information search theory, suggesting that information search increases purchase likelihood when it is goal directed, extensive, and easy to conduct. Thus, the broad role of digital advertising should be to make the information search process easier and more convenient for consumers to stimulate purchases. These findings help digital advertisers understand information search patterns across device modalities. Implications for digital advertisers on electronic commerce (e-commerce) platforms are offered.
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BFBNIB, IZUM, KILJ, NUK, PILJ, SAZU, UL, UM, UPUK
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