The differential impact of types of app innovation on customer evaluation

Tian, Hengqi; Grover, Varun; Jing, Zhao; Jiang, Yi

doi:10.1016/j.im.2020.103358

Cited by 21 publications

(39 citation statements)

References 66 publications

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“…If they do not perceive much value from these new functions, users who seek traditional banking services may be distressed by the overload of features (Ye & Kankanhalli, 2020). For instance, the new function of “post and comment” launched by Alipay received many complaints (Tian et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Value added or overload? A study of the countervailing effects of non‐core features on mobile banking apps

Lyu

Chin

Zhang

et al. 2021

J of Consumer Behaviour

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Section: Introductionmentioning

confidence: 99%

Value added or overload? A study of the countervailing effects of non‐core features on mobile banking apps

Lyu

Chin

Zhang

et al. 2021

J of Consumer Behaviour

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“…Haardle and Schaafer constructed an early warning model based on a support vector machine and compared it with the neural network model and multivariate discriminant analysis model, and the results showed that the prediction performance of the support vector machine is better compared with other models. In their study, Tian et al [21] analyzed the financial data of A-share listed companies in China with the help of the SVM model and the nearest domain method and showed that the SVM model is prone to overwarning, while the best domain method is relatively robust. Chen [22] constructed an early warning model for financial crisis based on the adaptive PSO-SVM model in their study, and the study showed that the method is good at dealing with scalability problems and also has good ability to.…”

Section: Related Workmentioning

confidence: 99%

Research on Early Warning of Financial Crisis of Listed Companies Based on Random Forest and Time Series

Zhang

Zhong

2022

Mobile Information Systems

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The global economy has entered a new normal, and the economic environment is evolving at a rapid pace. This requires the establishment of a financial crisis early warning system that can be dynamically analyzed based on historical data information. To address this research objective, this study proposes a k-fold random forest algorithm combined with a time series analysis model as an early warning algorithm for corporate financial crises. The algorithm takes advantage of the ability of the time series analysis model to make short-term forecasts of historical data and uses the time series analysis model to make forecasts of newly constructed financial index data. The k-fold random forest is used to analyze the financial situation of the predicted financial data and achieve the purpose of dynamic financial crisis early warning. The experimental results show that the prediction accuracy of the financial crisis early warning model based on the random forest algorithm and time series is 89%, which indicates that the model is effective and feasible.

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“…However, download ranking is more related to the objective download behavior of users [44]. Consequently, we believe that users are more confident in well-known apps with high rankings [45]. High rankings are the result of users' objective download behavior, indicating that these apps have gained the preference of many users and will have more powerful functions to meet user needs [46].…”

Section: Interaction Between App's Iterative Innovation and Visibilitymentioning

confidence: 99%

The Sustainability of Superior Performance of Platform Complementor: Evidence from the Effects of Iterative Innovation and Visibility of App in iOS Platform in China

et al. 2021

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The sustainability of superior performance is a vital goal for complementor in mobile platform ecosystem, but it is becoming increasingly difficult to realize it. The previous literature about the sustainability of superior performance is insufficient and lacks deep research the mechanisms that affect the sustainability of superior performance. Based on user-oriented perspective, this study examines the direct and interactive effects of app’s iterative innovation and visibility on the sustainability of complementor’s superior performance. Using a two-way fixed effect model and monthly panel data, with the final dataset of 4596 observations from 384 apps of top-ranking positions by revenue available from December 2018 to December 2019 on the Apple iOS App Store in China, the results show that a) app’s iterative innovation and visibility (including platform recommendations and download rankings) promote the sustainability of app’s superior performance; b) platform recommendations and iterative innovation have a synergistic effect on the sustainability of app’s superior performance; and c) the download ranking has a substitution effect on iterative innovation to promote the sustainability of app’s superior performance. The findings of this study have theoretical and practical implications for improving the sustainability of app’s superior performance in the digital age.

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The differential impact of types of app innovation on customer evaluation

Cited by 21 publications

References 66 publications

Value added or overload? A study of the countervailing effects of non‐core features on mobile banking apps

Value added or overload? A study of the countervailing effects of non‐core features on mobile banking apps

Research on Early Warning of Financial Crisis of Listed Companies Based on Random Forest and Time Series

The Sustainability of Superior Performance of Platform Complementor: Evidence from the Effects of Iterative Innovation and Visibility of App in iOS Platform in China

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