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

Scientific Workflows in IoT Environments: A Data Placement Strategy Based on Heterogeneous Edge-Cloud Computing

2022 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Du, Xin; Tang, Songtao; Lu, Zhihui; Gai, Keke; Wu, Jie; Hung, Patrick C. K.

Abstract: In Industry 4.0 and Internet of Things (IoT) environments, the heterogeneous edg ... Expand

Abstract: In Industry 4.0 and Internet of Things (IoT) environments, the heterogeneous edge-cloud computing paradigm can provide a more proper solution to deploy scientific workflows compared to cloud computing or other traditional distributed computing. Owing to the different sizes of scientific datasets and the privacy issue concerning some of these datasets, it is essential to find a data placement strategy that can minimize data transmission time. Some state-of-the-art data placement strategies combine edge computing and cloud computing to distribute scientific datasets. However, the dynamic distribution of newly generated datasets to appropriate datacenters and exiting the spent datasets are still a challenge during workflows execution. To address this challenge, this study not only constructs a data placement model that includes shared datasets within the individual and among multiple workflows across various geographical regions, but also proposes a data placement strategy (DYM-RL-DPS) based on algorithms of two stages. First, during the build-time stage of workflows, we use the discrete particle swarm optimization algorithm with differential evolution to pre-allocate initial datasets to proper datacenters. Then, we reformulate the dynamic datasets distribution problem as a Markov decision process and provide a reinforcement learning–based approach to learn the data placement strategy in the runtime stage of scientific workflows. Through using the heterogeneous edge-cloud computing architecture to simulate IoT environments, we designed comprehensive experiments to demonstrate the superiority of DYM-RL-DPS. The results of our strategy can effectively reduce the data transmission time as compared to other strategies. Collapse

Topics: cloud computing mobile system internet of things privacy innovation management

Methods: computational algorithm experiment reinforcement learning simulation Markov decision process

Effect of Market Information on Bidder Attrition in Online Auction Markets

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Ghasemkhani, Hossein; Goes, Paulo; Tripathi, Arvind

Abstract: Information generated in online markets can affect both buyers’ and sellers’ exp ... Expand

Abstract: Information generated in online markets can affect both buyers’ and sellers’ expectations and therefore their choices. In this research, we investigate the effect of market information, generated in online auction markets, on buyers’ expectations and choices. To clear large inventories, sellers often conduct many auctions selling identical items over time, which creates an online auction market where competition dynamics spill over from one to auction to another. In these markets, bidders can participate in many auctions over a period of time, observe market information (supply, demand, and competition), and gain experience to increase their payoffs. We observe that despite having an opportunity to compete and win in future auctions, many bidders stop participating in these auction markets. We argue that observed market information affects their choices. We explore how bidders form expectations about market supply, demand, and competition based on information from two sources—market design parameters and behavior of market participants. By employing a hierarchical Bayesian latent attrition model, we empirically detect and investigate the effect of bidders’ expected market supply, demand, and competition on their attrition in these markets. Our study shows that the effect of market information on attrition is nuanced by bidders’ value heterogeneity. Through the lenses of behavioral economics theories, we show that the attrition behavior of high-value bidders is completely opposite that of low-value bidders. We discuss the practical implications of our findings. Collapse

Topics: online auction decision making electronic market willingness to pay consumer behavior

Methods: descriptive statistic hidden Markov model Markov chain Monte Carlo conceptual modelling theoretical contribution

Theories: economic theory endowment effect behavioral economics lemon market theory social learning theory

The dynamic customer engagement behaviors in the customer satisfaction survey

2022 | Decision Support Systems | Citations: 2

Authors: Han, Saram; Anderson, Christopher K.

Abstract: Conducting customer satisfaction surveys (CSS) as a means of encouraging custome ... Expand

Abstract: Conducting customer satisfaction surveys (CSS) as a means of encouraging customers’ engagement with a firm is a widely used managerial practice. In an effort to convert this customer-to-firm engagement into customer-to-customer engagement, many firms have recently started to ask customers who reach the final question of the CSS to write an online review that will be automatically posted on a review site (e.g., Google or TripAdvisor). Using a unique longitudinal data set provided by a large global hotel chain, we aim to understand the drivers behind CSS participation and, ultimately, behind online review posting in response to a prompt at the end of a survey. We employ the Hidden Markov model (HMM) to analyze how a customer’s engagement state changes as a function of previous managerial responses, loyalty program membership, and satisfaction. The results reveal that customers have a greater tendency to transfer to higher engagement states if managers have responded to prior customer surveys. We have also found that the higher the customer’s loyalty program membership status is, the more likely it is that they will participate in a survey. Lastly, we show that customers have a greater tendency to post negative versus positive reviews. We then discuss the implications of this study for researchers and managers. Collapse

Topics: online review user-generated content marketing management customer satisfaction social media

Methods: survey longitudinal research hidden Markov model Markov chain Monte Carlo Markov chain

Theories: social exchange theory

How Information Contributed After an Idea Shapes New High-Quality Ideas in Online Ideation Contests

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Lindberg, Aron; Majchrzak, Ann; Malhotra, Arvind

Abstract: Findings on how prior high-quality ideas affect the quality of subsequent ideas ... Expand

Abstract: Findings on how prior high-quality ideas affect the quality of subsequent ideas in online ideation contests have been mixed. Some studies find that high-quality ideas lead to subsequent high-quality ideas, while others find the opposite. Based on computationally intensive exploratory research, utilizing theory on blending of mental spaces, we suggest that the effects of prior ideas on the generation of subsequent ideas depend on the alignment of (1) crowd participants’ subjective quality assessments of prior ideas and (2) subsequent problemrelated contributions made by the crowd. When a prior idea is assessed as high-quality, this motivates the crowd to emulate that idea. When this motivation is aligned with subsequent contributions that expand the mental space of the prior idea, a new high-quality idea can be created. In contrast, when a prior idea is assessed as low-quality, it motivates the crowd to redirect away from that idea. When this motivation is aligned with subsequent contributions that shift the mental space of the prior idea, a new high-quality idea can be created. The mixed findings in the literature can then be explained by a failure to consider non-idea information contributions made by the crowd. Collapse

Topics: wiki open source electronic brainstorming 3D printing production management

Methods: theory development Markov chain Markov chain Monte Carlo digital trace data qualitative coding

Cross-Modality Transfer Learning for Image-Text Information Management

2022 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Niu, Shuteng; Jiang, Yushan; Chen, Bowen; Wang, Jian; Liu, Yongxin; Song, Houbing

Abstract: In the past decades, information from all kinds of data has been on a rapid incr ... Expand

Abstract: In the past decades, information from all kinds of data has been on a rapid increase. With state-of-the-art performance, machine learning algorithms have been beneficial for information management. However, insufficient supervised training data is still an adversity in many real-world applications. Therefore, transfer learning (TF) was proposed to address this issue. This article studies a not well investigated but important TL problem termed cross-modality transfer learning (CMTL). This topic is closely related to distant domain transfer learning (DDTL) and negative transfer. In general, conventional TL disciplines assume that the source domain and the target domain are in the same modality. DDTL aims to make efficient transfers even when the domains or the tasks are entirely different. As an extension of DDTL, CMTL aims to make efficient transfers between two different data modalities, such as from image to text. As the main focus of this study, we aim to improve the performance of image classification by transferring knowledge from text data. Previously, a few CMTL algorithms were proposed to deal with image classification problems. However, most existing algorithms are very task specific, and they are unstable on convergence. There are four main contributions in this study. First, we propose a novel heterogeneous CMTL algorithm, which requires only a tiny set of unlabeled target data and labeled source data with associate text tags. Second, we introduce a latent semantic information extraction method to connect the information learned from the image data and the text data. Third, the proposed method can effectively handle the information transfer across different modalities (text-image). Fourth, we examined our algorithm on a public dataset, Office-31. It has achieved up to 5% higher classification accuracy than “non-transfer” algorithms and up to 9% higher than existing CMTL algorithms. Collapse

Topics: knowledge sharing information management website Netflix digital service

Methods: computational algorithm data transformation autoencoder experiment machine learning

On the Environmental and Economic Impact of Utility-Scale Renewable Energy Deployment

2022 | HICSS | Citations: 0

Authors: Peerzada, Aaqib; Begovic, Miroslav M.; Ostojic, Dejan

Abstract: As per the U.S Energy Information Administration’s latest inventory of electrici ... Expand

Abstract: As per the U.S Energy Information Administration’s latest inventory of electricity generators, renewable energy, most notably solar and wind, will account for roughly 70% of nearly 40 gigawatts of new electricity generating capacity to start commercial operation in 2021. The year 2021 will also set a record in the deployment of utility-scale solar capacity by adding 15.4 gigawatts of capacity to the grid, which surpasses the 12 gigawatts increase in 2020. The rapid increase of renewable energy is expected to significantly decrease emissions of greenhouse gases and change the load profile in the power grid by suppressing production from conventional generators. This paper aims to propose a framework to study the impact of utility-scale solar PV deployment on the generation resource allocation and investigate the economics and policy of electricity generation and carbon emissions. The investigation is carried on the generation resource pool of the southeast region of the U.S augmented by a substantial amount of utility-scale solar generation. Collapse

Topics: resource allocation cloud computing database system

Methods: simulation Markov chain Monte Carlo computational algorithm Markov chain

A machine learning approach to identifying decision-making styles for managing customer relationships

2022 | Electronic Markets | Citations: 0

Authors: Tudoran, Ana Alina

Abstract: Decision-making styles have been studied in non-situational settings using the c ... Expand

Abstract: Decision-making styles have been studied in non-situational settings using the classical survey instrument. This study proposes a novel methodology for identifying decision-making styles in a real-world purchasing situation using only behavioral data and machine learning. We base our analysis on a two-week sample of 1,347,854 clickstream sessions from an e-commerce company and extract a series of parameters to infer the search goal, strategy, and decision difficulty. We implement a range of unsupervised algorithms, and we identify and validate three internally stable classes of decision-makers. One category corresponds to the classical style of satisficers; the other two subcategorize the maximisers' classical style. The customer’s entry channel preferences and movement patterns provide compelling support for the style's predictive validity. This study contributes to research and practice by proposing a new methodology to recognize the customer decision style in the e-commerce setting. Collapse

Topics: decision making information storage and retrieval system electronic commerce tablet mobile system

Methods: cluster analysis digital trace data computational algorithm machine learning unsupervised learning

Theories: theory of bounded rationality

Theorizing Routines with Computational Sequence Analysis: A Critical Realism Framework

2022 | Journal of the Association for Information Systems | Citations: 6

Authors: Zhang, Zhewei; Lee, Habin; Yoo, Youngjin; Choi, Youngseok

Abstract: We develop a methodological framework to develop process theories on routines b ... Expand

Abstract: We develop a methodological framework to develop process theories on routines by leveraging large volumes of digital trace data following critical realism principles. Our framework begins with collecting and preprocessing digital trace data, corresponding to the empirically observed experience of critical realism. In the second and third steps of the framework, we identify a finite set of similar repetitive patterns (routines) through computational analysis. We accomplish this by combining frequent subsequence mining and clustering analysis to transform empirical observation into a set of routines that correspond to actual happening in critical realism. Then, we employ a retroduction approach to identify generative mechanisms of the routines. In the final step, we validate the generative mechanisms by evaluating proposed processual explanations and/or eliminating alternatives. We provide an illustrative example of developing a process theory in relation to the collaboration pattern in Wikipedia.Suprateek Sarker was the accepting senior editor. This research article was submitted on April 17, 2017 and underwent three revisions.Zhewei Zhang is an assistant professor in the Information Systems Management group at Warwick Business School, University of Warwick. He received his PhD at Temple University (2016). His research primarily focuses on digital innovation, particularly the organizational and societal impacts brought by the adoption of emerging technologies and innovation through collaboration in nontraditional organizational settings. He is experienced in applying computational approaches, such as sequence analysis and machine learning, to support his research. He served as the co-investigator of several funded projects, such as Innovate UK and GCRF, leading the development of artificial intelligence solutions.Habin Lee is a chair in digital business analytics at Brunel Business School, Brunel University London. He obtained his PhD and MEng in management engineering and management science respectively at KAIST (Korea Advanced Institute of Science and Technology). His research interests include computational social science, digital governance, data analytics for policy modeling and simulation, and sustainable operations management. He published articles in Collapse

Topics: Wikipedia online community knowledge representation computer supported cooperative work information technology infrastructure

Methods: digital trace data cluster analysis hierarchical clustering computational algorithm theory development

The Return on Information Technology: Who Benefits Most?

2021 | Information Systems Research | Citations: 0

Authors: Dhyne, Emmanuel; Konings, Jozef; Van den bosch, Jeroen; Vanormelingen, Stijn

Abstract: This paper uses a new microdata set of business-to-business firm-level transacti ... Expand

Abstract: This paper uses a new microdata set of business-to-business firm-level transactions in Belgium to construct a measure of information and communication technology (ICT) investment at the firm level, which we combine with the income statement of firms to analyze the impact of ICT on productivity. We find that a firm investing an additional euro in ICT increases value added by 1 euro and 35 cents on average. This marginal product of ICT investment increases with firm size and varies across sectors. Although we find substantial returns of ICT at the firm level, such returns are much lower at the aggregate level. This is due to underinvestment in ICT (ICT capital deepening is low) and misallocation of ICT investments. Collapse

Topics: IT investment productivity business-to-business accounting computer software

Methods: robustness check survey longitudinal research cross sectional research inventory method