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All eyes on me: Predicting consumer intentions on social commerce platforms using eye-tracking data and ensemble learning
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2023 | Decision Support Systems | Citations: 0
Authors: Mikalef, Patrick; Sharma, Kshitij; Chatterjee, Sheshadri; Chaudhuri, Ranjan; Parida, Vinit; Gupta, Shivam

Abstract: Understanding what information is important for consumers when making a purchase ... Expand

Abstract: Understanding what information is important for consumers when making a purchase-related decision has been a key question for researchers and practitioners ever since the advent of empirical research in commerce. Nevertheless, our knowledge of what information is important has been formed primarily through post-purchase conscious capturing approaches, such as surveys and questionnaires. To overcome these limitations, we ground this research on an exploratory study that captures eye-tracking data during a decision-making task of product selection. Grounded on the dynamic attention theory, we utilize different information types and formats present on a popular social commerce platform, to identify elements which are important when deciding about online product purchase decision. Specifically, we employ a series of prediction algorithms and use an ensemble learning setup to predict the aspects that contribute to product selection by consumers. Our analysis highlights the most important informational cues to accurately predict product selection among alternatives. In addition, the results showcase how such elements shift in importance during the temporal sequence of comparing different product alternatives. Our results provide insight into how we can understand the journey of decision-making for social commerce customers when navigating through information to select a product. In addition, it opens the discussion about the shifts that eye-tracking in combination with machine learning can create for researchers and marketers. Collapse

Semantic filters: cognitive attention theory

Topics: decision making social commerce website user requirement web page

Methods: oculometry computational algorithm ensemble learning machine learning eye tracking tool

Theories: cognitive attention theory
Impacts of Hiding Friends’ Liked Content on User-Content Engagement across Newsfeed Channels

2022 | International Conference on Information Systems | Citations: 0
Authors: Zhang, Xiaohui; He, Qinglai; Zhang, Zhongju

Abstract: Social media platforms often distribute content through different newsfeed chan ... Expand

Abstract: Social media platforms often distribute content through different newsfeed channels, most commonly, social networks, algorithmic recommendations and trending content. Prior literature has investigated each channel's impact on user-content engagement. However, little is known about the relationships between these channels. We investigate the impacts of limiting content display from the social network channel on the quantity and diversity of user-engaged content across channels. We leverage a natural experiment, where a social media platform hides friends' liked content from the social network channel, to identify the impacts. Results show that hiding friends' liked content reduces the quantity of users' content engagement on the entire platform. Across channels, users increase their engagement with trending content but decrease their engagement with algorithmic recommendations. Further, restricting exposure to friends' liked content reduces the diversity of users' content engagement. Our results highlight the intercorrelation of usercontent engagement across newsfeed channels and provide insights for newsfeed designs. Collapse

Semantic filters: cognitive attention theory

Topics: social network social media mobile application IT skill societal evaluation criteria

Methods: computational algorithm longitudinal research natural experiment experimental group k-means clustering

Theories: cognitive attention theory
News-Induced Dynamic Networks for Market Signaling: Understanding the Impact of News on Firm Equity Value
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2021 | Information Systems Research | Citations: 0
Authors: Chen, Kun; Li, Xin; Luo, Peng; Zhao, J. Leon

Abstract: Firm relations, which inform the competitive environments of firms, are critical ... Expand

Abstract: Firm relations, which inform the competitive environments of firms, are critical to firm operations and are often factored into investor decisions. Previous studies and practices have considered relatively stable and long-term business relations as an indicator of firm market value. Public news often reports on business relations, especially dynamic and short-term opportunities and challenges involving different partners. Learning about firm relations from news is commonly done by human investors but has not been studied systematically in previous research, leading to a research opportunity in market signaling via dynamic firm relations. In this study, we propose a new approach to market signaling by leveraging text-mining methods to extract cobenefit/counter-benefit networks based on firms’ mutual or conflicting interests in business events. We find that the resulting networks in the long term are partially aligned with firm cooperation and competition relationships, confirming their semantic implications for investor perception and attention. Our empirical study further shows that dynamic networks formed in a short time period (measured by firm centrality in networks) have significant impacts on firm equity value, after controlling for market activities and other information from news, such as volume, sentiment, and comentions. We show that dynamic networks can provide additional value in predicting firm equity value over stable networks. Moreover, the negative effects of counter-benefit networks emerge rapidly and persist longer than the positive effects of cobenefit networks. This study provides new insights into investor perception of news and suggests new research directions for financial text mining. Our research findings on market signaling via news-induced networks also have an impact on financial practices, such as market analysis and automatic trading. Collapse

Semantic filters: cognitive attention theory

Topics: network effect database system supply chain management blog accounting

Methods: computational algorithm machine learning experiment longitudinal research natural language processing

Theories: cognitive attention theory
Popular News Are Relevant News! How Investor Attention Affects Algorithmic Decision-Making and Decision Support in Financial Markets
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2021 | Information Systems Frontiers | Citations: 0
Authors: Clapham, Benjamin; Siering, Michael; Gomber, Peter

Abstract: Algorithmic decision-making plays an important role in financial markets. Curren ... Expand

Abstract: Algorithmic decision-making plays an important role in financial markets. Current tools in trading focus on popular companies which are discussed in thousands of news items. However, it remains unclear whether methodologies from the field of data analytics relying on large samples can also be applied to small datasets of less popular companies or whether these methodologies lead to the discovery of meaningless patterns resulting in economic losses. We analyze whether the impact of media sentiment on financial markets is influenced by two levels of investor attention and whether this impacts algorithmic decision-making. We find that the influence differs substantially between news and companies with high and low investor attention. We apply a trading simulation to outline the practical consequences of these interrelations for decision support systems. Our results are of high importance for financial market participants, especially for algorithmic traders that consider sentiment for investment decision support. Collapse

Semantic filters: cognitive attention theory

Topics: social media decision support system website digital media decision making

Methods: simulation sentiment analysis sensitivity analysis descriptive statistic regression analysis method

Theories: behavioral economics cognitive attention theory transaction cost economics
Promoting or attenuating? An eye-tracking study on the role of social cues in e-commerce livestreaming
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2021 | Decision Support Systems | Citations: 0
Authors: Fei, Mengqi; Tan, Huizhong; Peng, Xixian; Wang, Qiuzhen; Wang, Lei

Abstract: Unlike general e-business, e-commerce livestreaming innovatively enables anchors ... Expand

Abstract: Unlike general e-business, e-commerce livestreaming innovatively enables anchors to use instant social functions to communicate with viewers and present products in more vivid ways. However, little research has been done to understand the effects of social cues in e-commerce livestreaming. Drawing on stimulus-organism-response (S-OR) theory, we develop a two-phase research framework to examine how two widely used social cues (i.e., herding message and interaction text) can affect viewers’ attention allocation procedure and purchase intention when watching e-commerce livestreaming. We also propose that anchor characteristics (i.e., attractiveness) may moderate the effects of social cues. To examine the above effects, we conduct a within-subject eye-tracking experiment. Using generalized linear mixed-effects models, we find that both herding message and interaction text can capture exogenous attention. In terms of endogenous attention to the product and anchor, interaction text shows a negative distracting effect, whereas herding message shows a positive spillover effect, negatively moderated by anchor attractiveness. Moreover, endogenous attention is positively related to purchase intention, while the effect of exogenous attention is relatively more complex, involving the competing mechanisms of the distracting effect and social influence. Theoretical and practical implications are discussed. Collapse

Semantic filters: cognitive attention theory

Topics: live streaming platform electronic commerce advertising management usability human computer interaction

Methods: oculometry experiment descriptive statistic longitudinal research structural equation modeling

Theories: cognitive attention theory
The Impact of Animated Banner Ads on Online Consumers: A Feature-Level Analysis Using Eye Tracking
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2021 | Journal of the Association for Information Systems | Citations: 0
Authors: Hong, Weiyin; Cheung, Muller; Thong, James

Abstract: Despite the popular use of animated banner ads on websites, extant research on t ... Expand

Abstract: Despite the popular use of animated banner ads on websites, extant research on the effects of web animation has generated mixed results. We argue that it is critical to identify feature-level animation characteristics and examine their individual and combined effects on capturing online consumers’ attention across different task conditions. We identify three key animation features (i.e., motion, lagging, and looming) based on three attention theories and investigate their effects on online consumers’ attention and recall across browsing and searching tasks in three laboratory experiments using an eye tracking machine. Experiment 1 found that both motion and looming (animation features) are effective in attracting online consumers’ attention to animated ads when they are performing a browsing task. However, combining a salient feature (e.g., motion) with another salient feature (e.g., looming) does not improve the original attention attraction effect, suggesting a “banner saturation” effect. Further, we found that online consumers’ attention positively affects their recall performance. In Experiment 2, none of the animation features or their interactions had a significant effect when the subjects were performing a searching task, indicating that task is an important boundary condition when applying attention theories. Experiment 3 replicated Experiment 1 in a more realistic context and produced similar results. We conclude the paper by discussing theoretical and practical implications as well as avenues for future research. Collapse

Semantic filters: cognitive attention theory

Topics: website DVD web page online advertising robotic

Methods: experiment oculometry longitudinal research within subject experiment multivariate analysis of variance

Theories: cognitive attention theory cognitive theory
Collecting Useful Information from Crowds: Is Experience Required?

2021 | International Conference on Information Systems | Citations: 0
Authors: Ogunseye, Shawn; Parsons, Jeffrey

Abstract: Consumers of crowdsourced data expect experienced contributors to report more u ... Expand

Abstract: Consumers of crowdsourced data expect experienced contributors to report more useful data than inexperienced contributors. Guided by selective attention theory, we propose to examine the veracity of this expectation in two types of data crowdsourcing platforms -an online review platform and a citizen science platform. We contend that as the experience of data contributors increase, it leads to selective attention, causing contributors to report similar data over time. We, therefore, predict that data diversitythe number and type of attributes present in contributed data -and the usefulness of contributed data will decrease as contributors gain experience in a crowdsourcing task. We propose an experiment to test these predictions and find from our first study that increasing experience from participation in crowdsourcing tasks may be detrimental to collecting diverse and useful data from crowds. Collapse

Semantic filters: cognitive attention theory

Topics: crowdsourcing citizen science personality perceived usefulness online review

Methods: spaCy statistical hypothesis test experiment computational algorithm hierarchical linear modeling

Theories: cognitive attention theory
Mitigating Information Asymmetry to Achieve Crowdfunding Success: Signaling and Online Communication
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2021 | Journal of the Association for Information Systems | Citations: 17
Authors: Wang, Nianxin; Liang, Huigang; Xue, Yajiong; Ge, Shilun

Abstract: This paper examines how signals and the herding factor originating from multipl ... Expand

Abstract: This paper examines how signals and the herding factor originating from multiple sources complement or substitute for each other's effects in the crowdfunding context. Drawing on the elaboration likelihood model, we propose that signals from campaigns (videos) and creators (experience) can mitigate information asymmetry concerns about project quality and creator credibility and that creator-originating signals offset the effect of campaign-originating signals on crowdfunding success. Further, we posit that online communication between creators and backers (backer comments and creator replies) complements or substitutes for the effects of signals originating from the campaign or creator. We collected and analyzed objective data on 9,884 crowdfunding projects from a major reward-based crowdfunding platform in China and were able to confirm most of our hypotheses.Online communication also can help alleviate information asymmetry between creators and potential backers. Such communication occurs when backers post comments and creators post replies. Comments from backers can signal interest of the crowd, and potential backers may choose to follow the crowd to save time and avoid risks. For example, research has shown that more comments in the form of online reviews by early consumers may lead to online herding purchases (Shen et al. 2016). Prior studies (Wang et al. 2018) have also shown that online communication using computer-mediated communication (CMC) tools can enhance backers' perceived interactivity with the creator, overcome communication barriers, improve communication quality, and facilitate mutual understanding.Past crowdfunding research on the challenges of information asymmetry has typically examined the direct impacts of specific signaling factors or online backer-creator communication in mitigating information asymmetry in isolation. However, multiple signals and online backer-creator communication often Collapse

Semantic filters: cognitive attention theory

Topics: crowdfunding crowdsourcing logistics management decision making entrepreneurship

Methods: propensity score matching descriptive statistic statistical hypothesis test quantitative observational study experimental group

Theories: lemon market theory elaboration likelihood model cognitive attention theory
Unintentional Insecure Behavior

2021 | Americas Conference on Information Systems | Citations: 0
Authors: Williams, Jason; Gupta, Saurabh

Abstract: Recent research has shown that IT use may be classified as either "continued us ... Expand

Abstract: Recent research has shown that IT use may be classified as either "continued use" that emphasizes efficiency or "novel use" that emphasizes innovation. Given these fundamental IT use types, this research seeks to understand which IT use type produces more secure behaviors in users. Using attention theory, we seek to understand secure behaviors from a purely user-completing-work-tasks approach instead of one of adherence to policy. A potential method for studying this phenomenon is given. Potential implications for research and practice are also shared. KeywordsSecure behavior, unintentional insecure behavior, continued use, novel use. Collapse

Semantic filters: cognitive attention theory

Topics: information system use

Methods: experiment statistical hypothesis test

Theories: cognitive attention theory
If erring is human, is system use divine? Omission errors during post-adoptive system use
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2020 | Decision Support Systems | Citations: 0
Authors: Goutas, Lazaros; Hess, Basil; Sutanto, Juliana

Abstract: Our study contributes to the research on human error during IS use by studying t ... Expand

Abstract: Our study contributes to the research on human error during IS use by studying the antecedents of the omission errors that occur during routine instances of computerized work. While attention lapses have been identified as the main mechanism leading to omission errors, we still know little about how such lapses come about during post-adoptive system use. To address this limitation, we draw our theoretical insights from theories of attention and prospective memory to illustrate how the different forms of system use carry the potential to explain patterns of human error. Accordingly, we distinguish between two forms of use history that can consist of features that are either related or unrelated to the execution of a focal task and examine their effects on the frequency of omission errors. We also examine the interaction effects of task variation on the aforementioned relationship. Our hypotheses are tested by analyzing log data associated with the use of a newly introduced mobile application in the context of a sailing sports event. Our results indicate that restricting one's system use on related task features reduces omission errors, whereas a use history based on unrelated task features produces the opposite effects. Further, task diversity positively moderates the relationship between a use history of unrelated features and omission errors, but has no significant moderating effect on the relationship between a use history of related features and omission errors. Our findings hold a number of implications for the literature on human error, and these are discussed alongside with the implications of our study for practitioners and system design. Collapse

Semantic filters: cognitive attention theory

Topics: information system use mobile application task characteristic user training tablet

Methods: statistical hypothesis test regression analysis method robustness check descriptive statistic conceptual modelling

Theories: cognitive attention theory

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Found 31 articles

All eyes on me: Predicting consumer intentions on social commerce platforms using eye-tracking data and ensemble learning

Impacts of Hiding Friends’ Liked Content on User-Content Engagement across Newsfeed Channels

News-Induced Dynamic Networks for Market Signaling: Understanding the Impact of News on Firm Equity Value

Popular News Are Relevant News! How Investor Attention Affects Algorithmic Decision-Making and Decision Support in Financial Markets

Promoting or attenuating? An eye-tracking study on the role of social cues in e-commerce livestreaming

The Impact of Animated Banner Ads on Online Consumers: A Feature-Level Analysis Using Eye Tracking

Collecting Useful Information from Crowds: Is Experience Required?

Mitigating Information Asymmetry to Achieve Crowdfunding Success: Signaling and Online Communication

Unintentional Insecure Behavior

If erring is human, is system use divine? Omission errors during post-adoptive system use