More Enduring Questions in Cognitive IS Research

In the April 2012 issue of the Journal of the Association for Information Systems, Michael Davern, Teresa Shaft, and Dov Te’eni published an article titled “Cognition Matters: Enduring Questions in IS Research”. Their paper reviewed much of the history of cognitive research in the IS discipline, especially that related to human-computer interaction and decision support systems. While we believe their article is excellent in many respects, we also believe that it omitted a great deal of the most basic cognitive research performed in the IS domain over the past 10-15 years, especially work in the area of systems analysis and design. Our purpose in this paper is to supplement the work of Davern et al. by discussing much of this recent work. We use two theoretical lenses to organize our review: basic cognition and behavioral decision-making research. Our review provides many illustrations of IS research in these areas, including memory and categorization (basic cognition) and heuristics and biases (behavioral decision making). The result, we believe, is a fuller picture of the breadth of cognition-based work in the IS discipline in general and systems analysis and design in particular. The paper provides further evidence of the importance of cognitive research in IS and suggests additional enduring questions for future investigations

More Enduring Questions in Cognitive IS Research: A Reply

In this short reply, Michael Davern, Teresa Shaft, and Dov Te\u27eni respond to Glenn Brown and Jeffrey Parson\u27s dialogue paper, More enduring questions in cognitive IS research

Designing attention-aware business intelligence and analytics dashboards

The design of user interface is known to influence the users’ attention while they are interacting with applications such as Business Intelligence and Analytics (BI&A) dashboards. BI&A dashboards are considered as critical because they contain a lot of compressed information and managers only spend a little time to process the provided information. Thereby, they need to manage their visual attention properly due to inattentional blindness and change blindness issues. We propose to investigate the design of BI&A dashboards that are sensitive to the users’ attention. So called attention-aware BI&A dashboards are of utmost importance in the field of BI&A systems since attention is known to play a major role in constructing decisions. We motivate our research project and present the initial design of attention-aware BI&A dashboards. Especially the inclusion of eye-tracking technology is an important aspect of our proposed design

Do We Behave Based on Our Implicit Attitudes? Proposing a Research Model and an Experimental Study to Investigate Their Influence on Behavioral Intentions

Attitudes are one of the three most-frequently studied independent variables to explain user behavior. However, although psychological literature distinguishes between explicit and implicit attitudes, most of the investigations in the research stream of IS acceptance and usage have a pure focus on explicit attitudes and do not consider implicit attitudes. Explicit and implicit attitudes can be contradictory and both might predict behavioral intention. Therefore, the present research-in-progress focuses on closing the research gap of refraining to differentiate attitudes in explicit and implicit attitudes and hence examining the influence of implicit attitudes on user behavior. Based on the Implicit Association Test (IAT) and surveys, we propose an experimental setting that measures explicit and implicit attitudes to validate the research model. The proposed research might contribute to the research stream of IS acceptance and usage by better predicting behavioral intentions by also considering implicit attitudes. Future results might explain distorted predictions of behavior and reduce the intention-behavior gap. Furthermore, the present research-in-progress introduces a suitable method to measure implicit attitudes

Is Human Information Processing Affected by Emotional Content? Understanding The Role of Facts and Emotions in the Stock Market

The Securities and Exchange Commission(SEC) mandates stock-listed companies in the U.S. to file regulated disclosures that should allow investors to make an informed decision before exercising ownership in stock. We thus hypothesize that investors do not rely solely upon the essential facts but are also impaired by unconscious and idiosyncratic characteristics in their perception. In fact, such affective processing is suggested by behavioral finance and information processing theory, while empirical evidence in large-scale settings remains rare. As a remedy, this paper statistically locates decisive words in financial news that reflect the complete bandwidth of drivers behind investment decisions. According to our results, the decision-making of investors is significantly influenced by emotionally-charged content and non-informative wording

Affordance Theory in the IS Discipline: a Review and Synthesis of the Literature

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Noise Trader Behavior - A Disaggregated Approach to Understanding News Reception in Financial Markets

Financial disclosures serve as primary intermediaries between companies and investors. However, investors have different information processing skills and might easily be misled by noisy signals that lack a deeper meaning. In financial markets, this is formalized by the noise trader theory, which groups investors into two categories: (a) informed investors assumed to form rational decisions and (b) noise traders forming beliefs partly based on non-fundamental noise signals and news sentiment. Yet, little is known about how these groups actually interpret textual information in financial statements and how the resulting stock market reaction differs. This work extends previous research by unraveling the role of word choice and semantic orientation in financial disclosures for both investor types. For this purpose, we use Kalman filtering to decompose the stock market reaction following the publication of U.S. regulated Form 8-K filings into a fundamental price component and a noise residual. We then use LASSO regression to identify the statistically relevant words for informed investors and noise traders. According to our results, each investor type assigns significantly different interpretations and degrees of importance to individual words and documents. Keywords: Noise trader theory, Information processing, Decision-making, Financial markets, News sentiment, Kalman filte

The influence of the information stimulus on the human information behavior

The use of multiple digital technologies to perform tasks or solve problems become a regular practice in the corporate environment while the amount of information available to people grows at an impressive pace. However, scant studies dedicated to understanding the influence of the actual use of multiple digital technologies and the influence of information stimulus on human information behavior. Recent literature on the information system positions the study of human information behavior as a critical research area for its power to predict and explain the human behavior. For this reason, this research focus on the influence of information stimulus on human information behavior during the use of multiple digital technologies, precisely the individual behavior in the organizational setting. This study developed three articles, comprehending a literature review, qualitative research, and quantitative research to validate the conceptualized the information stimuli and the proposed cognitive model. The central objective is to understand the influence of the use of multiple digital technologies on human behavior. The findings showed that the information load and information diversity represent the information stimulus that influences the capacity of the information workers to determine their information need, their ability to seek for specific information, and the use of information while performing a task. The recognition of the negative influence of the use multiple technologies was expressed in behaviors such as the need to focus, the strategies to prioritize tasks, the development of self-control, and the uncertainty. The quantitative research with 565 information workers presented support on the hypotheses between information diversity, information load, information need, information seeking, and information use. The results are important once they represent the measurement of the perception about the influence of the information stimulus on the human behavior. Finally, this research makes significant contributions conceptualizing human information behavior in the information system literature and providing a new approach to evaluate human information behavior in the context of high information stimuli

Conceptual modeling research in information systems: What we now know and what we still do not know

Much of conceptual modeling research over recent times has been guided by a seminal research agenda developed by Wand and Weber (2002), which identified twenty-two research opportunities. In this paper, we explore whether existing research has provided sufficient answers to these questions. Our findings from a review of the literature show a dialectic: several of the opportunities noted in 2002 have been addressed substantially while others have been entirely neglected. We also found several path breaking studies that addressed problems not spotted by the initial framework. To stimulate a forward-looking wave of conceptual modeling research, we provide a new framework that draws the attention of conceptual modeling research to the interplay between digital representations and outcomes

Understanding the Role of IS and Application Domain Knowledge on Conceptual Schema Problem Solving: A Verbal Protocol Study

One of the most neglected areas of information systems research is the role of the domain to which researchers apply IS methods, tools, and techniques; that is, the application domain. For example, little prior information systems (IS) or related research has examined how IS and application domain knowledge (ISDK and ADK, respectively) influence how individuals solve conceptual schema problem-solving tasks. In this research, we investigate the effects of both ISDK and ADK on two types of conceptual schema problem-solving tasks: schema based and inferential. We used verbal protocol analysis to explore the roles that ISDK and ADK play in the problem-solving processes participants use when addressing these tasks. We found that, for the two types of conceptual schema problem-solving tasks, ADK and ISDK have similar effects on problem-solving processes. That is, we found that, for schema-based problem-solving tasks, participants used focused (depth-first) processes when the application domain was familiar as did participants with greater IS domain knowledge. We also found that, for inferential problem-solving tasks, participants used exploratory (breadth-first) processes when the application domain was familiar as did participants with greater IS domain knowledge. We then show how cognitive psychology literature on problem solving can help explain the effects of ISDK and ADK and, thus, provide the theoretical foundation for analyzing the roles of each type of knowledge in the process of IS problem solving