ICT-Based Country-Level Determinants of Social Media Diffusion

2023 | Information Systems Frontiers | Citations: 0

Authors: Arayankalam, Jithesh; Krishnan, Satish

Abstract: There is a broad consensus that social media platforms have become critical tool ... Expand

Abstract: There is a broad consensus that social media platforms have become critical tools for policymakers in achieving various policy objectives in a country. Therefore, an in-depth understanding of the ICT-based country-level determinants of social media diffusion has become necessary. To this end, we propose a government-people-technology (GTP) framework for categorizing the ICT-based country-level determinants of social media diffusion into three dimensions. Using this framework and utilizing archival data from 107 countries, we examine the roles of ICT in the government's vision, ICT law maturity, Internet bandwidth, ICT cost, ICT skill, and capacity to own ICT in influencing social media diffusion. A quantile panel data regression results reveal that ICT in the government's vision, ICT law maturity, Internet bandwidth, and capacity to own ICT were positively associated with social media diffusion. In contrast, ICT cost was negatively associated with it. Thus, by offering a new framework, we contribute to the technology diffusion literature by emphasizing the role of government, people, and supportive technologies in driving social media diffusion at the country level. Collapse

Topics: social media IT investment internet technology IT skill technology diffusion

Methods: longitudinal research regression analysis method post-hoc analysis cross sectional research archival research

Theories: theory of everything

A temporal graph framework for intelligence extraction in social media networks

2023 | Information & Management | Citations: 0

Authors: Chung, Wingyan; Lai, Vincent S.

Abstract: Social media networks (SMNs) are increasingly used in professional management of ... Expand

Abstract: Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for in­ telligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experi­ mental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing un­ derstanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence. Collapse

Topics: cryptocurrency social network systems development situation awareness user behavior

Methods: experiment machine learning longitudinal research computational algorithm time series analysis

Theories: psychological theory social contagion theory social interactionist theory social impact theory social sciences theory

Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Jiang, Jinhang; Feng, Mei; Srinivasan, Karthik

Abstract: In recent years, social media has become an indispensable source of information ... Expand

Abstract: In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research. Collapse

Topics: Reddit social media explainable artificial intelligence Python permutation feature importance

Methods: descriptive statistic time series analysis longitudinal research topic model experiment

Cultural Roots of Entrepreneurship: Evidence from Second-Generation Immigrants

2023 | Organization Science | Citations: 0

Authors: Kleinhempel, Johannes; Klasing, Mariko J.; Beugelsdijk, Sjoerd

Abstract: Does national culture influence entrepreneurship? Given that entrepreneurship an ... Expand

Abstract: Does national culture influence entrepreneurship? Given that entrepreneurship and the economic, formal institutional, and cultural characteristics of nations are deeply intertwined and co-vary, it is difficult to isolate the effect of culture on entrepreneurship. In this study, we examine the self-employment choices of second-generation immigrants who were born, educated, and currently live in one country, but were raised by parents stemming from another country. We argue that entrepreneurship is influenced by durable, portable, and intergenerationally transmitted cultural imprints such that second-generation immigrants are more likely to become entrepreneurs if their parents originate from countries characterized by a strong entrepreneurial culture. Our multilevel analysis of two independent samples—65,323 second-generation immigrants of 52 different ancestries who were born, were raised, and live in the United States and 4,165 second-generation immigrants of 31 ancestries in Europe—shows that entrepreneurial culture is positively associated with the likelihood that individuals are entrepreneurs. Our results are robust to alternative non-cultural explanations, such as differences in resource holdings, labor market discrimination, and direct parent-child linkages. Overall, our study highlights the durability, portability, and intergenerational transmission of entrepreneurial culture as well as the profound impact of national culture on entrepreneurship. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1645. Collapse

Topics: entrepreneurship human resource management accounting national culture database system

Methods: hierarchical linear modeling survey robustness check descriptive statistic instrumental variables estimation

Theories: theory of economic development

Does the Electronic Medical Record (EMR) Adoption Matter? Exploring Patterns of EMR Implementation and its Impact on Hospital Performance

2023 | HICSS | Citations: 0

Authors: Lee, J.; Kim, J.; Shin, S.

Abstract: We aimed to explore the patterns of electronic medical records (EMR) adoption a ... Expand

Abstract: We aimed to explore the patterns of electronic medical records (EMR) adoption and its effects on hospital performance. We analyzed hospital-level panel data from 2008 to 2013 using Bayesian regression and the Naïve Bayes model. Our research analysis revealed 38 different adoption patterns for 1,919 hospitals that completed EMR implementation (having all of the four components) and 42 different adoption patterns for 1,341 hospitals that could not complete the EMR implementation. We examined the hospitals' EMR adoption patterns that were not completed; but predicted as completed using the Naïve Bayes model. Our results revealed that the hospitals that completed EMR adoption showed higher performance in terms of patient recommendation and net patient revenue than those that did not complete EMR adoption. More importantly, most of hospitals that observed as "not completed" but predicted as "completed" showed lower performance in terms of patient recommendation as well as net patient revenue. Collapse

Topics: technology adoption artificial intelligence database system missing data electronic health record

Methods: naïve bayes machine learning survey regression analysis method Markov chain Monte Carlo

Reidentification Risk in Panel Data: Protecting for k-Anonymity

2023 | Information Systems Research | Citations: 0

Authors: Li, Shaobo; Schneider, Matthew J.; Yu, Yan; Gupta, Sachin

Abstract: We consider the risk of reidentification of panelists in marketing research data ... Expand

Abstract: We consider the risk of reidentification of panelists in marketing research data that are widely used to obtain insights into buyer behavior and to develop marketing strategy. We find that 17%–94% of the panelists in 15 frequently bought consumer goods categories are subject to high risk of reidentification through a potential record linkage attack based on their unique purchasing histories even when their identities are anonymized. We first demonstrate that the risk of reidentification is vastly understated by unicity, the conventional measure. Instead, we propose a new measure of reidentification risk, termed sno-unicity, which accounts for the longitudinal nature of panel data, and show that it is much larger than unicity. To protect the privacy of panelists, we consider the well-known privacy notion of k-anonymity and develop a new approach called graph-based minimum movement k-anonymization (k-MM) that is designed especially for panel data. The proposed k-MM approach can be formulated as an optimization problem in which the objective is to minimally distort variables in the original data based on weights that users prespecify corresponding to their use case. We further show how our approach can be extended to achieve l-diversity. We apply the k-MM approach to two different panel data sets that are widely used in marketing research. To achieve a given privacy level, compared with several benchmark protection methods, the protected data from our method result in the least distortion in inferences about key marketing metrics, such as brand market shares, share of category requirements, brand switching rates, and marketing-mix parameters estimated from a hierarchical Bayesian brand choice model. History: Param Singh, Senior Editor and Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1169. Collapse

Topics: privacy marketing management differential privacy data protection record linkage

Methods: longitudinal research optimization model descriptive statistic simulation cluster analysis

Personalized Ranking at a Mobile App Distribution Platform

2023 | Information Systems Research | Citations: 0

Authors: Mao, Shengjun; Dewan, Sanjeev; Ho, Yi-Jen (Ian)

Abstract: The ease of customer data collection has enabled the widespread personalization ... Expand

Abstract: The ease of customer data collection has enabled the widespread personalization of content and services in digital platforms. We examine personalization in a hitherto unaddressed context: that of mobile app distribution. Specifically, we develop a comprehensive framework for the personalized ranking of app impressions, leveraging revealed preferences embedded in consumer clickstream data. To improve platform revenues, the framework jointly accounts for consumer utility and cost per action (CPA) margin, which is the revenue earned by the platform per app installation. To this end, we specify a structural model of click and installation choices, jointly estimated as a function of a comprehensive set of numerical (screen rank, quality, and popularity) and textual (titles, descriptions, and reviews) covariates. Our novel data set is at the granular user-impression level and uniquely includes app CPA margins paid to the platform. We conduct a series of policy experiments to quantify the value of personalization. Specifically, we show that a personalized hybrid margin and utility margin ranking scheme outperforms other personalized methods, including those based on utilities alone or a combination of utilities and margins. Overall, our analysis demonstrates how platforms can leverage routine consumer clickstream data to personalize the ranking of app impressions, thereby more effectively monetizing mobile app distribution.History: Param Singh, Senior Editor; Juan Feng, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1156. Collapse

Topics: mobile application personalization usability search engine accounting

Methods: experiment mathematical model robustness check latent dirichlet allocation Markov chain Monte Carlo

Theories: utility theory

Password and Passphrase Guessing with Recurrent Neural Networks

2023 | Information Systems Frontiers | Citations: 0

Authors: Nosenko, Alex; Cheng, Yuan; Chen, Haiquan

Abstract: Most online services continue their reliance on text-based passwords as the prim ... Expand

Abstract: Most online services continue their reliance on text-based passwords as the primary authentication mechanism. With a growing number of these services and the limited creativity to devise new memorable passwords, users tend to reuse their passwords across multiple platforms. These factors, combined with the increasing number of leaked passwords, make passwords vulnerable to cross-site guessing attacks. Over the years, researchers have proposed several prevalent methods to predict subsequently used passwords, such as dictionary attacks, rule-based approaches, neural networks, and combinations of the above. We exploit the correlation between the similarity and predictability of these subsequent passwords in a dataset of 28.8 million users and their 61.5 million passwords. We use a rule-based approach but delegate rule derivation, classification, and prediction to a Recurrent Neural Network (RNN). We limit the number of guessing attempts to ten yet get an astonishingly high prediction accuracy of up to 83% in under five attempts, twice as much as any other known model. The result makes our model effective for targeted online password guessing without getting spotted or locked out. To the best of our knowledge, this study is the first attempt of its kind using RNN. We also explore the use of RNN models in passphrase guessing. Passphrases are perceived to be more secure and easier to remember than passwords of the same length. We use a dataset that contains around 100,000 distinct phrases. We demonstrate that RNN models can predict complete passphrases given the initial word with rate up to 40%, which is twice better than other known approaches. Furthermore, our predictions can succeed in under 5,000 attempts, a 100% improvement compared to existing algorithms. In addition, this approach provides ease of deployment and low resource consumption. To our knowledge, it is the first attempt to exploit RNN for passphrase guessing. Collapse

Topics: password Google+ computer hardware electronic authentication Python

Methods: artificial neural network experiment computational algorithm naïve bayes Markov chain Monte Carlo

Removing order effects from human-classified datasets: A machine learning method to improve decision making systems

2023 | Decision Support Systems | Citations: 0

Authors: Romanov, Dmitry; Molokanov, Valentin; Kazantsev, Nikolai; Jha, Ashish Kumar

Abstract: Although recent developments in Artificial Intelligence (AI) and machine learnin ... Expand

Abstract: Although recent developments in Artificial Intelligence (AI) and machine learning (ML) aim to enhance the fairness and transparency of decision-making systems, research has found that neural networks (or other similar AI techniques) are still effected by human cognitive biases due to the training datasets. In this study, we focus on order effects, i.e., when the input of information impacts human perception and the decisions resulting from this information. We propose the Order Effect Removal Method (OERM) for handling the order effect which leads to bias and for helping organizations remove these biases from their training datasets and, therefore, from auto­ mated decision-making systems. Using design science principles to theoretically create, test, and validate the method, we can eliminate the order bias even in basic classification systems. Furthermore, the method can be applied in a multidisciplinary context, where an AI-based algorithm substitutes for manual work. Collapse

Topics: decision making artificial intelligence big data cloud computing enterprise resource planning

Methods: naïve bayes design science artificial neural network classification method kernel theory

Theories: probability theory

Software Components and Product Variety in a Platform Ecosystem: A Dynamic Network Analysis of WordPress

2023 | Information Systems Research | Citations: 0

Authors: Um, Sungyong; Zhang, Bin; Wattal, Sunil; Yoo, Youngjin

Abstract: Software components, such as application programming interfaces (APIs), provided ... Expand

Abstract: Software components, such as application programming interfaces (APIs), provided by both the platform owner and external developers in a platform ecosystem are vital to online digital platforms. These APIs are associated with functionality provided by the platform. A larger number of APIs used is generally associated with an increase in the platform ecosystem’s variety of products. However, the precise nature of the relationship between the use of APIs and product variety in a platform ecosystem is not yet known because of the lack of appropriate methodologies to analyze the complex data generated in such platform ecosystems. Drawing on a recombinatorial design perspective, we use a dynamic network model to create metrics for product variety in a digital ecosystem and study how the use of different types of APIs affects product variety. In particular, we theorize that, contrary to existing models, the structure of APIs in a large platform ecosystem is not limited to a core–periphery structure, but includes an additional third layer that we refer to as the regular core. We further hypothesize that external APIs in the regular core play a crucial role in increasing product variety in the ecosystem. To test our hypothesis, we conduct an empirical study using longitudinal data of all available digital product (i.e., plug-in) source codes on WordPress.org, the world’s largest content management system and one of the most extensive digital platform ecosystems. By analyzing all of the plug-ins on WordPress over 10 years from its inception, we find the support of our three-layer structure of APIs in a platform ecosystem: complete core, regular core, and periphery. Our empirical analysis further supports the role of external APIs in the regular core in increasing product variety. We, however, find that the strength of this effect diminishes in a newly created product category. History: Param Singh, Senior Editor; Pallab Sanyal, Associate Editor. Funding: This work was supported by the Ministry of Education Singapore [Grants R-253-000-122-133 and T1-251RES1916], the National Science Foundation [Grants 1261977 and 1120966], and Club Informatique des Grandes Entreprises Françaises. Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2022.1172. Collapse

Topics: application programming interface digital ecosystem digital product innovation management website

Methods: doc2vec hierarchical clustering longitudinal research cluster analysis chi squared test