The Influence of Sentiment Orientation in Open Innovation Communities: Empirical Evidence from a Business Analytics Community

Daradkeh, Mohammad

doi:10.1142/s0219649221500313

Cited by 19 publications

(25 citation statements)

References 80 publications

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“…Ardito, Antonio [5] found that users' preference for a review decreases or even disappears if the review title has no relevance to the content of the review, as there is no mere contact effect. Conversely, the higher the relevance of the review title to the content of the review, the higher the users' preference for a review [62]. Therefore, in this study, it is postulated that the higher the relevance of the title of the knowledge payment product to the product description, the more likely it is that users will prefer this knowledge product and stimulate their knowledge payment behavior.…”

Section: Information Quality Of Product Descriptionmentioning

confidence: 89%

Information Adoption Patterns and Online Knowledge Payment Behavior: The Moderating Role of Product Type

2022

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The development of online knowledge payment platforms in recent years has increased their respective market value by nurturing content resources and improving content ecology. Yet, the underlying factors of knowledge seekers’ payment behaviors and their information adoption mechanisms are poorly understood. Based on the information adoption model, this study develops a research model to examine the relationship between information adoption patterns and knowledge seekers’ payment behavior, and explore the moderating effect of product type on this relationship. To test the research model and hypotheses, we used a multi-analytic approach combining text and regression analysis on a sample of 4366 social Q&A data collected from Quora+ between August 2021 and August 2022. We further classified the product types into utilitarian and hedonic, and compared the differences in influence paths between product types. The results show that the completeness, vividness, and relevance of the product description have a significant positive impact on knowledge payment behavior. The reputation, experience, and integrity of the knowledge provider have a positive impact on knowledge payment behavior. Compared to utilitarian knowledge products, the payment behavior for hedonic products is more related to the reputation and experience of the knowledge provider. This study provides insights into the factors that influence online knowledge payment behavior and practical guidance for the development of online knowledge payment services and platforms.

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Section: Information Quality Of Product Descriptionmentioning

confidence: 89%

Information Adoption Patterns and Online Knowledge Payment Behavior: The Moderating Role of Product Type

2022

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“…Since open innovation communities (OICs) are characterized by openness and cooperation, the issue of value co-destruction, which refers to the situation where the inappropriate use of resources leads to a reduction in one party’s welfare [ 1 ], is inherent to these communities. While OICs has been viewed as a means for firms to leverage external resources, expand their knowledge base, and maintain competitiveness, recent research indicates that interactions between enterprises and users may result in value co-destruction rather than value co-creation [ 6 , 7 ]. Consequently, it has become imperative for businesses to explore the mechanisms underlying this phenomenon to optimize the benefits of open innovation while minimizing potential risks.…”

Section: Introductionmentioning

confidence: 99%

Navigating Value Co-Destruction in Open Innovation Communities: An Empirical Study of Expectancy Disconfirmation and Psychological Contracts in Business Analytics Communities

Daradkeh

2023

Behavioral Sciences

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Enterprises seeking to enhance their innovation capabilities are increasingly turning to open innovation communities (OICs), which allow them to leverage the collective knowledge and collaborative potential of external users, providing a powerful source of new and innovative ideas. Despite their potential for value co-creation, recent research suggests that value co-destruction can also occur within OICs. However, the mechanisms underlying value co-destruction in OICs have not yet been fully explored or empirically examined. To address this gap, this study employs expectancy disconfirmation theory and psychological contract theory to investigate the relationship between user expectancy disconfirmation and value co-destruction in OICs. Drawing upon data collected from a questionnaire survey of business analytics OICs, this study reveals that self-interest expectancy disconfirmation has a positive effect on value co-destruction, which is mediated by the transactional psychological contract breach. In addition, social interaction expectancy disconfirmation is found to have a positive impact on value co-destruction, which is mediated by the relational psychological contract breach. The study further reveals that self-worth expectancy disconfirmation of community users positively influences value co-destruction, which is mediated by the ideological psychological contract breach. Moreover, the study demonstrates the crucial role of perceived organizational status in moderating the ideological psychological contract breach resulting from self-worth expectancy disconfirmation. Collectively, these findings contribute valuable insights into the phenomenon of value co-destruction in OICs, and provide practical guidance for enterprises seeking to enhance the development and performance of these innovation paradigms.

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“…The mixed-ownership stock exchange is invested in businesses with common shares that can be traded on the public stock market only in limited circumstances. Mixed-ownership stock exchanges are established in countries such as the United Kingdom (London Stock Exchange) and the United States (New York Stock Exchange) [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Aldhyani

Alzahrani

2022

Electronics

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The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

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The Influence of Sentiment Orientation in Open Innovation Communities: Empirical Evidence from a Business Analytics Community

Cited by 19 publications

References 80 publications

Information Adoption Patterns and Online Knowledge Payment Behavior: The Moderating Role of Product Type

Information Adoption Patterns and Online Knowledge Payment Behavior: The Moderating Role of Product Type

Navigating Value Co-Destruction in Open Innovation Communities: An Empirical Study of Expectancy Disconfirmation and Psychological Contracts in Business Analytics Communities

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

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