How Board of Directors’ Social Capital Enhances the Effectiveness of IT and R&D Resources Toward More Effective Innovation

2023 | HICSS | Citations: 0

Authors: Shekarian, Naser; Ramirez, Ronald; Tallon, Paul; Shekarian, Mansoor

Abstract: A board of directors (BOD) plays a critical governance and strategic oversight r ... Expand

Abstract: A board of directors (BOD) plays a critical governance and strategic oversight role in an organization; acting as a fiduciary for shareholders, advising strategic decision making, and providing supportive resources and information to key decision makers. Especially critical is the role and contribution of corporate governance in guiding firm innovation. Such guidance has implications for investment in new products and services. In this paper, we examine the synergistic relationship between a firm’s BOD and technology and R&D inputs to innovation. We focus on the influence of the social capital of a BOD on different types of innovation. Our longitudinal findings show IT, R&D, and BOD social capital individually contribute to innovation performance, reflected in exploitative and exploratory innovation productivity. Moreover, BOD social capital enhances innovation enabled by IT activities. However, the combination of R&D activities and dimensions of BOD social capital leads to both negative and positive innovation performance. Collapse

Topics: innovation management social capital research and development knowledge creation productivity

Methods: longitudinal research White test Breusch-Pagan test archival research econometric modeling

Theories: organizational knowledge creation

Introduction to the Minitrack on EdTech and Emerging Technologies

2023 | HICSS | Citations: 0

Authors: Shekhar, Gaurav; Khazanchi, Deepak

Abstract: The value that we seek in our societies cannot be created on a one-way street. ... Expand

Abstract: The value that we seek in our societies cannot be created on a one-way street. In fact the value that we seek cannot be created, but co-created. Edtech has the power of unifying these different elements in the society to create those long term and meaningful solutions through the use of technology. 'Education beyond borders', 'Education for All' are some schools of thought that have limitless benefits and the advancements in edtech can help us explore creative ways to make it a possibility. At the end of the day, the number one goal of education is to be that positive influence that helps brings solutions together. In the world of technology, edtech is that vehicle to make this happen. This minitrack brings together empirical studies, design, theory, theoretical framework, case studies, and etc. The minitrack encompasses research from an implementation standpoint of a technical or economical model, which engages emerging technologies in the area of edtech. Collapse

Topics: online learning system

Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian Learning

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Shen, Dazhong; Zhu, Hengshu; Xiao, Keli; Zhang, Xi; Xiong, Hui

Abstract: Personality has been considered as a driving factor for work engagement, which s ... Expand

Abstract: Personality has been considered as a driving factor for work engagement, which significantly affects people’s role performance at work. Although existing research has provided some intuitive understanding of the connection between personality traits and employees’ work behaviors, it still lacks effective quantitative tools for modeling personality traits, job position characteristics, and employee work behaviors simultaneously. To this end, in this article, we introduce a data-driven joint Bayesian learning approach, Joint-PJB, to discover explainable joint patterns from massive personality and job-position-related behavioral data. Specifically, Joint-PJB is designed with the knowledgeable guidance of the four-quadrant behavioral model, namely, DISC (Dominance, Influence, Steadiness, Conscientiousness). Based on the real-world data collected from a high-tech company, Joint-PJB aims to highlight personality-job-behavior joint patterns from personality traits, job responsibilities, and work behaviors. The model can measure the matching degree between employees and their work behaviors given their personality and job position characteristics. We find a significant negative correlation between this matching degree and employee turnover intention. Moreover, we also showcase how the identified patterns can be utilized to support real-world talent management decisions. Both case studies and quantitative experiments verify the effectiveness of Joint-PJB for understanding people’s personality traits in different job contexts and their impact on work behaviors. Collapse

Topics: personality IT personell turnover IT job missing data decision making

Methods: experiment artificial neural network survey topic model case study

DeepPsych: Harnessing Market Psychology with Deep Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Shen, Jiancheng; Wang, Jia; Zhu, Hongwei; Liu, Benyuan

Abstract: Investor psychology provides an important avenue for modeling non-fundamental b ... Expand

Abstract: Investor psychology provides an important avenue for modeling non-fundamental behaviors in financial analysis. Yet, whether market psychological information has a practical application in predicting asset returns is still under debate. Thus, a burgeoning number of machine learning algorithms have been developed to test the effectiveness of investor psychology in financial predictions. With all the merits of machine learning approach, the drawbacks are prediction biases, data overfitting issues and poor performance. To address these issues, we developed a DeepPsych system to harness the power of high frequency TRMI psychology data for market prediction. In a "hybridization-generalization-dual-channel-fusion" three-stage experiment, we evaluate each proposed module and the entire framework against the state-of-art machine learning benchmarks on investor psychology and trading data of the SPY (SP500 ETF). Results demonstrate that our deep learning framework can automatically identify features that are more effective than fundamental factors and support profitable trading. Collapse

Topics: social media electronic trading big data innovation management

Methods: experiment deep learning machine learning design science convolutional neural network

Theories: transaction cost economics

What Drives Workers to Learn Online during COVID 19 Pandemics?

2023 | HICSS | Citations: 0

Authors: Shen, Wen-Cheng; Lin, Fu-Ren

Abstract: One of the common practices during the COVID-19 pandemic is to work or study fro ... Expand

Abstract: One of the common practices during the COVID-19 pandemic is to work or study from home. This study aims to reexamine the factors affecting individual continuance intention of e-learning. During the pandemic, via a survey conducted in 2022, we assessed workers’ continuance intention of e-learning from different sectors in Taiwan. This research brought motivations as mediators in continuance intention to e-learning. Through the statistical analysis, we identified the mediation effect of motivations based on the self-determination theory. The results show that autonomous motivation facilitates the learners’ computer self-efficacy, the quality of the system and content toward continuance intention; controlled motivation could mediate the monetary award in influencing the continuance intention. The internalization of motivation is also an effective mediator. The obtained results not only add new knowledge of what affected the continuance intention of e-learning during the pandemic but also provide guidance for employers to allocate resources to boost e-learning after the pandemic. Collapse

Topics: online learning system pandemic self efficacy system quality Facebook

Methods: survey partial least squares path modeling PLS tool exploratory factor analysis data transformation

Theories: self determination theory

Converging Tides Lift All Boats: Consensus in Evaluation Criteria Boosts Investments in Firms in Nascent Technology Sectors

2023 | Organization Science | Citations: 0

Authors: Shen, Xirong (Subrina); Li, Huisi (Jessica); Tolbert, Pamela S.

Abstract: Although previous studies show that the emergence of evaluation criteria for a n ... Expand

Abstract: Although previous studies show that the emergence of evaluation criteria for a new technology improves the life chances of well-performing firms, we theorize that consensus in such criteria among technology experts increases investments to all firms in the new sector. We provide a variety of supportive evidence for this claim. First, in an experiment with 80 Chinese investors (Study 1), we provide evidence of a causal relation between evaluation consensus and investments. We follow this with a second experiment with 412 U.S. participants (Study 2), showing that evaluation criteria consensus increases participants’ propensity to view a firm as technologically competent and to expect others to favor investing in the firm. Analyses of longitudinal archival data on investment in artificial intelligence technology firms in the United States (Study 3a) and China (Study 3b) support the generalizability of our findings. By exploring the social-cognitive processes that link evaluation criteria consensus to investors’ decisions to invest in firms in nascent technology fields, this paper advances the scholarly understanding of the microfoundations of the institutionalization processes in new market sectors.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: This work was supported by Cornell University (Innovation, Entrepreneurship, and Technology Theme Research Grant, Jeffrey S. Lehman Fund for Scholarly Exchange with China).Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1493. Collapse

Topics: evaluation criteria artificial intelligence database system decision making intellectual property

Methods: experiment hazard function archival research survey theory development

Relationships between Involuntary Visual Cueing and Sensemaking of YouTube Videos

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Shen, Zixing; Tan, Songxin

Abstract: Sensemaking plays a critical role in the everyday use of social media, which ar ... Expand

Abstract: Sensemaking plays a critical role in the everyday use of social media, which are increasingly visual as video, images, and other visual content permeate. Yet, research on the sensemaking of visual information on social media is minimal. This study addresses this research gap by investigating the relationships between involuntary visual cues (color contrast and visual complexity) and making sense of videos on YouTube and Reddit. It finds that color contrast has a significant association with sensemaking on both platforms and that visual complexity is a significant predictor of sensemaking only on Reddit. Our study adds to the research on sensemaking on social media. It expands social media analysis from textual to visual information and contributes to the knowledge of visual sensemaking on social media. It also provides practitioners with guidelines for designing videos that facilitate visual sensemaking. Collapse

Topics: sensemaking YouTube social media Reddit

Methods: survey design multiple linear regression image processing statistical hypothesis test computational algorithm

Theories: information theory

Learner Engagement with YouTube Videos in Informal Online Learning: An Investigation of the Effects of Segmenting, Signaling, and Weeding

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Shen, Zixing

Abstract: Millions of educational videos available on YouTube offer unprecedented opportu ... Expand

Abstract: Millions of educational videos available on YouTube offer unprecedented opportunities for online learning.As it invites open-ended and self-paced exploration of almost any topic, YouTube has emerged as an important platform for informal online learning that occurs outside the formal classroom. A considerable number of studies have been directed toward YouTube educational videos. However, research on learner engagement with YouTube educational videos is limited, despite the central role of engagement in learning and the increasing popularity of YouTube videos in informal online learning. This paper addresses this research gap. We adopt the conceptualization that learner engagement has three dimensionsbehavioral, emotional, and cognitive -and investigate how the features of segmenting, signaling, and weeding (SSW), the three multimedia learning principles, in YouTube educational video presentations collectively affect learner engagement in informal online learning. Our analysis shows that different SSW features have various associations with the three dimensions of learner engagement. These findings substantiate the empirical knowledge of learner engagement with YouTube educational videos. Our study corroborates extant video engagement research and extends its relevance to informal learning on social media. It also informs video designers and developers on adding video presentation features to optimize video engagement on social media. Collapse

Topics: YouTube online learning system social media Reddit social media analytics

Methods: survey design statistical hypothesis test multiple linear regression design science design guideline

"Goodnight Alexa" – Theorising interactions between people with visual impairments and digital voice assistants

2023 | International Conference on Information Systems | Citations: 0

Authors: Shethia, Shikha; Waizenegger, Lena; Techatassanasoontorn, Angsana

Abstract: ... Expand

Abstract: Collapse

Topics: conversational agent pseudonymity smart home information system use frequency of use

Methods: qualitative interview theory development personal interview qualitative coding

Deep Learning in Predicting Real Estate Property Prices: A Comparative Study

2023 | HICSS | Citations: 0

Authors: Shi, D.; Zhang, H.; Guan, J.; Zurada, J.; Chen, Z.; Li, X.

Abstract: The dominant methods for real estate property price prediction or valuation are ... Expand

Abstract: The dominant methods for real estate property price prediction or valuation are multi-regression based. Regression-based methods are, however, imperfect because they suffer from issues such as multicollinearity and heteroscedasticity. Recent years have witnessed the use of machine learning methods but the results are mixed. This paper introduces the application of a new approach using deep learning models to real estate property price prediction. The paper uses a deep learning approach for modeling to improve the accuracy of real estate property price prediction with data representing sales transactions in a large metropolitan area. Three deep learning models, LSTM, GRU and Transformer, are created and compared with other machine learning and traditional models. The results obtained for the data set with all features clearly show that the RF and Transformer models outperformed the other models. LSTM and GRU models produced the worst results, suggesting that they are perhaps not suitable to predict the real estate price. Furthermore, the implementations of Transformer and RF on a data set with feature reduction produced even more accurate prediction results. In conclusion, our research shows that the performance of the Transformer model is close to the RF model. Both models produce significantly better prediction results than existing approaches in terms of accuracy. Collapse

Topics: Python open source

Methods: deep learning transformer model machine learning autoregressive moving average model experiment