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Exploring Parental Influence on Children's Cyber Hygiene

2023 | Americas Conference on Information Systems | Citations: 0
Authors: Armeen, Inaiya; Yang, Ning; Abbaraju, Pranith

Abstract: In today's digital age, children are highly vulnerable to various cybersecurity ... Expand

Abstract: In today's digital age, children are highly vulnerable to various cybersecurity threats, highlighting the importance of instilling good cyber hygiene. While several factors contribute to children's cyber hygiene, parents in particular play a crucial role. This study examines how parents influence their children's cyber hygiene. Drawing on role model theory and parental mediation theory, we propose that parental factors, such as their cyber hygiene practices, guidance through open conversations about online safety, restrictions on device and internet usage, and digital safety measures, can positively impact their children's cyber hygiene. We plan to use a mixed-methods design that includes semi-structured interviews and surveys to test our hypotheses. The findings of this study will contribute to the literature on cyber hygiene and aid in developing effective strategies to promote good cyber hygiene among children. Collapse

Semantic filters: psychological theory

Topics: information system use

Methods: mixed method survey personal interview qualitative interview conceptual modelling

Theories: psychological theory social learning theory
A temporal graph framework for intelligence extraction in social media networks
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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

Semantic filters: psychological theory

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
A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media
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2023 | Information Systems Frontiers | Citations: 0
Authors: Chung, Wingyan; Zhang, Yinqiang; Pan, Jia

Abstract: The spreading of disinformation in social media threatens cybersecurity and unde ... Expand

Abstract: The spreading of disinformation in social media threatens cybersecurity and undermines market efficiency. Detecting disinformation is challenging due to large volumes of social media content and a rapidly changing environment. This research developed and validated a theory-based, novel deep-learning approach (called TRNN) to disinformation detection. Grounded in social and psychological theories, TRNN uses deep-learning and data-centric augmentation to enhance disinformation detection in financial social media. Temporal and contextual information is encoded as specific knowledge about human-validated disinformation, which was identified from our unique collection of 745,139 financial social media messages about four U.S. high-tech company stocks and their fine-grained trading data. TRNN uses multiple series of long short-term memory (LSTM) recurrent neurons to learn dynamic and hidden patterns to support disinformation detection. Our experimental findings show that TRNN significantly outperformed widely-used machine learning techniques in terms of precision, recall, F-score and accuracy, achieving consistently better classification performance in disinformation detection. A case study of Apple Inc.’s stock price movement demonstrates the potential usability of TRNN for secure knowledge management. The research contributes to developing novel approach and model, producing new information systems artifacts and dataset, and providing empirical findings of detecting online disinformation. Collapse

Semantic filters: psychological theory

Topics: disinformation social media Twitter fake news usability

Methods: machine learning experiment design artifact deep learning recurrent neural network

Theories: psychological theory
The (Bounded) Role of Stated-Lived Value Congruence and Authenticity in Employee Evaluations of Organizations
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2023 | Organization Science | Citations: 0
Authors: Deeds Pamphile, Vontrese; Ruttan, Rachel Lise

Abstract: A growing body of research documents that audiences reward organizations perceiv ... Expand

Abstract: A growing body of research documents that audiences reward organizations perceived to be authentic with positive evaluations. In the current work, we adopt a mixed-methods approach—using data collected from Glassdoor.com and two experiments—to establish that perceptions of authenticity are elicited by perceived congruence between an organization’s stated values (i.e., the values it claims to hold) and its lived values (i.e., values members perceive as embodied by the organization), which in turn lead to more positive organizational evaluations. We then explore the conditions under which audiences are less likely to respond favorably to organizational authenticity, finding that the positive effects of stated-lived value congruence on evaluations are attenuated when audiences have a lower preference for stated values. Although scholars have often explored whether and how organizations can successfully make themselves appear authentic to reap rewards, our findings suggest that the perceived authenticity that results from stated-lived value congruence may not prove fruitful unless the audience holds a higher preference for an organization’s stated values.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: This research was supported by the Interdisciplinary Research Award from the Management and Organizations Department at the Kellogg School of Management and by the Michael-Lee Chin Institute for Corporate Citizenship Research Grant from the Rotman School of Management.Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1578. Collapse

Semantic filters: psychological theory

Topics: innovation management organizational value online review website organizational success

Methods: experiment parametric test product review statistical power analysis two-way ANOVA

Theories: cognitive dissonance theory organizational behavior theory organizational theory psychological theory
The Interplay of IT Identity and Digital Mindset in the Workplace

2023 | Americas Conference on Information Systems | Citations: 0
Authors: Hildebrandt, Yannick; Finze, Nikola; Wagner, Heinz-Theo

Abstract: IT identity plays a significant role in individuals' IT adoption and use behavi ... Expand

Abstract: IT identity plays a significant role in individuals' IT adoption and use behaviors and can impact job satisfaction. However, other individual traits, like the digital mindset, previously showed to also affect the effects of the intertwining of IT and workplaces on job satisfaction. Therefore, we examine the role of a digital mindset in shaping IT and job identity and survey 167 employees. By analyzing the relationship between these concepts, the paper contributes to a better understanding of how identities are formed and how they affect job satisfaction in the digitally-enabled workplace. Our results demonstrate that IT identity's effect on job satisfaction is fully mediated by job identity. We also reveal that a digital mindset moderates the relationship between IT and job identities. Further, we discuss the key challenges and opportunities associated with the adoption and use of IS in organizations and provide insights into the future directions of IS research. Overall, we contribute to the ongoing discourse on the role of IT identity and digital mindset in the workplace and provide valuable insights for researchers and practitioners. Collapse

Semantic filters: psychological theory

Topics: job satisfaction information system use mindfulness data quality crowdsourcing

Methods: survey partial least squares regression descriptive statistic PLS tool mediation analysis

Theories: psychological theory
See No Evil, Hear No Evil: How Users Blindly Overrely on Robots with Automation Bias

2023 | International Conference on Information Systems | Citations: 0
Authors: Stock-Homburg, Ruth; Nguyen, Mai Anh

Abstract: Recent developments in generative artificial intelligence show how quickly user ... Expand

Abstract: Recent developments in generative artificial intelligence show how quickly users carelessly adhere to intelligent systems, ignoring systems' vulnerabilities and focusing on their superior capabilities. This is detrimental when system failures are ignored. This paper investigates this mindless overreliance on systems, defined as automation bias (AB), in human-robot interaction. We conducted two experimental studies (N1 = 210, N2 = 438) with social robots in a corporate setting to investigate psychological mechanisms and influencing factors of AB. Particularly, users experience perceptual and behavioral AB with the robot that is enhanced by robot competence depending on task complexity and is even stronger for emotional than analytical tasks. Surprisingly, robot reliability negatively affected AB. We also found a negative indirect-only mediation of AB on robot satisfaction. Finally, we provide implications for the appropriate use of robots to prevent employees from using them as a self-sufficient system instead of a supporting system. Collapse

Semantic filters: psychological theory

Topics: robotic task complexity data quality remote work IT supported collaboration

Methods: experiment survey design experimental group survey parametric test

Theories: uncanny valley theory psychological theory theory of technology dominance
Cross-cultural Analysis of Online Patient Reviews from the Caregiver Perspective

2023 | Americas Conference on Information Systems | Citations: 0
Authors: Yang, Alan; Talaei-Khoei, Amir; Liu, Caihua

Abstract: Online reviews have had an undeniable impact in the field of healthcare. Despit ... Expand

Abstract: Online reviews have had an undeniable impact in the field of healthcare. Despite the growing trend of online patient reviews, physician attitudes towards them vary greatly and their effect on physician behavior is uncertain. We propose a study exploring how physicians perceive online reviews through the lens of social-psychological theory and propose a framework for examining the phenomenon of online patient review valuation from the caregiver perspective. Collapse

Semantic filters: psychological theory

Topics: online review user-generated content national culture health information system

Methods: survey theory development literature study statistical hypothesis test longitudinal research

Theories: psychological theory
Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales

2023 | Management Information Systems Quarterly | Citations: 2
Authors: Yu, Yifan; Yang, Yang; Huang, Jinghua; Tan, Yong

Abstract: Emotion artificial intelligence, the algorithm that recognizes and interprets v ... Expand

Abstract: Emotion artificial intelligence, the algorithm that recognizes and interprets various human emotions beyond valence (positive and negative polarity), is still in its infancy but has attracted attention from industry and academia. Based on discrete emotion theory and statistical language modeling, this work proposes an algorithm to enable automatic domain-adaptive emotion lexicon construction and multidimensional emotion detection in texts. Using a large-scale dataset of China's movie market from 2012 to 2018, we constructed and validated a domain-specific emotion lexicon and demonstrated the predictive power of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) in online reviews on box office sales. We found that representing overall emotions through discrete emotions yields higher prediction accuracy than valence or latent emotion variables generated by topic modeling. To understand the source of the predictive power from a theoretical perspective and to test the cross-culture generalizability of our prediction study, we further conducted an experiment in the U.S. movie market based on theories on emotion, judgment, and decision-making. We found that discrete emotions, mediated by perceived processing fluency, significantly affect the perceived review helpfulness, which further influences purchase intention. Our work shows the economic value of emotions in online reviews, generates insight into the mechanism of their effects, and has managerial implications for online review platform design, movie marketing, and cinema operations. Collapse

Semantic filters: psychological theory

Topics: online review Python user-generated content microblog marketing management

Methods: computational algorithm word2vec parametric test product review experiment

Theories: psychological theory
Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods
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2023 | Decision Support Systems | Citations: 0
Authors: Zazon, Dor; Fink, Lior; Gordon, Shirley; Nissim, Nir

Abstract: Based on the assumption that the success of an organization is largely determine ... Expand

Abstract: Based on the assumption that the success of an organization is largely determined by the knowledge and skills of its employees, human resource (HR) departments invest considerable resources in the employee recruitment process with the aim of selecting the best, most suitable employees. Due to the high cost of the recruitment process along with its high rate of uncertainty, HR recruiters utilize a variety of methods and instruments to improve the efficiency and effectiveness of this process. Thus far, however, neurological methods, in which neurobiological signals from an examined person are analyzed, have not been utilized for this purpose. This study is the first to propose a neuro-based decision support system to classify cognitive functions into levels, whose target is to enrich the information and indications regarding the candidate along the employee recruitment processes. We first measured relevant functional and cognitive abilities of 142 adult participants using traditional computer-based assessment, which included a battery of four tests regarding executive functions and intelligence score, consistent with actual recruitment processes. Second, using electroencephalogram (EEG) technology, which is one of the dominant measurement tools in NeuroIS research, we collected the participants' brain signals by administering a resting state EEG (rsEEG) on each participant. Finally, using advanced machine and deep learning algorithms, we leveraged the collected rsEEG to classify participants' levels of executive functions and intelligence score. Our empirical analyses show encouraging results of up to 72.6% accuracy for the executive functions and up to 71.2% accuracy for the intelligence score. Therefore, this study lays the groundwork for a novel, generic (non-stimuli based) system that supports the current employee recruitment processes, that is based on psychological theories of assessing executive functions. The proposed decision support system could contribute to the development of additional medium of assessing employees remotely which is especially rele vant in the current Covid-19 pandemic. While our method aims at classification rather than at explanation, our intriguing findings have the potential to push forward NeuroIS research and practice. Collapse

Semantic filters: psychological theory

Topics: computer based testing privacy organizational context personality task productivity

Methods: electroencephalography computational algorithm machine learning NeuroIS deep learning

Theories: psychological theory
Tackling Crises Together? – An Econometric Analysis of Charitable Crowdfunding During the COVID-19 Pandemic

2022 | International Conference on Information Systems | Citations: 0
Authors: Althaus, Maike; Poniatowski, Martin; Kundisch, Dennis

Abstract: Crowdfunding platforms enable individuals or groups to appeal to the public to ... Expand

Abstract: Crowdfunding platforms enable individuals or groups to appeal to the public to support a variety of ventures or campaigns. Whilst the majority of campaigns raise funds for private causes, some of the issues for which help is being sought have arisen as a direct consequence of world events and crises. Nevertheless, the research on charitable crowdfunding has largely ignored this connection. We use the COVID-19 pandemic, and related public health policies, to explore the impact of the global crisis on donation behavior on the donation-based crowdfunding platform GoFundMe. By using a quasiexperimental research design, we find that after the introduction of stay-at-home orders, campaigns in U.S. states with such measures experienced a significant decline in the number of donors and amounts donated, which is more pronounced for crisis-related than for non-crisis-related campaigns. Our findings contribute to the literature by providing novel insights on crowdfunding behaviors in times of societal crisis. Collapse

Semantic filters: psychological theory

Topics: crowdsourcing crowdfunding pandemic social identity natural hazard

Methods: experimental group computational algorithm longitudinal research natural experiment descriptive statistic

Theories: self categorization theory psychological theory

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Found 127 articles

Exploring Parental Influence on Children's Cyber Hygiene

A temporal graph framework for intelligence extraction in social media networks

A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media

The (Bounded) Role of Stated-Lived Value Congruence and Authenticity in Employee Evaluations of Organizations

The Interplay of IT Identity and Digital Mindset in the Workplace

See No Evil, Hear No Evil: How Users Blindly Overrely on Robots with Automation Bias

Cross-cultural Analysis of Online Patient Reviews from the Caregiver Perspective

Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales

Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods

Tackling Crises Together? – An Econometric Analysis of Charitable Crowdfunding During the COVID-19 Pandemic