Applying the Onion Model to Scaffold Writing Development in IS Courses

2018 | Americas Conference on Information Systems | Citations: 0

Authors: Liginlal, Divakaran; Pessoa, Silvia; Gomez-Laich, Maria Pia; Aoun, Chadi

Abstract: Recent information systems (IS) curriculum guidelines emphasize the importance o ... Expand

Abstract: Recent information systems (IS) curriculum guidelines emphasize the importance of writing skill development (Topi et al., 2010). Although IS faculty are expected to take an active role in the development of students’ writing abilities (Pomykalski, 2006), this is a daunting task for professors who lack explicit knowledge of the various rhetorical moves that are accepted within their specific discourse community. In this study, an interdisciplinary collaboration between IS faculty and applied linguists was carried out in a first-year course about core concepts of IS at a branch campus of an English-medium American university in the Middle East to: (1) unpeel the different discourse patterns involved in the case analysis genre, (2) revise the professor’s writing assignments to make his expectations more explicit, and (2) develop writing workshops aimed at strengthening students’ skills in scholarly writing. The case analysis genre involves examining a case, identifying an organization’s problem(s), and proposing an IS solution through stages of analysis and recommendation (Forman & Rymer, 1999). The valued linguistic features of the case analysis genre were identified through: (1) interviews with the IS faculty, (2) think-alouds where the IS faculty read and commented on student writing from previous semesters, and (3) detailed analysis of the linguistic resources present in higher-and lower-graded assignments. These data informed the re-design of the assignment guidelines and the scaffolding materials for a writing workshop delivered in class. Our approach to scaffolding writing in the IS classroom is grounded in Systemic Functional Linguistics (SFL)based genre pedagogy (de Oliveira & Iddings, 2014) and the Onion Model (Humphrey & Economou, 2015), which discloses the discourse patterns that students need to master to generate successful academic genres across numerous disciplines. Our analysis of the students’ case analysis writing reveals more evidence of analytical writing compared to a previous semester where no writing workshops were offered. Collapse

Methods: case study cognitive walkthrough qualitative interview

Theories: systemic functional linguistics theory

Leveraging Financial Social Media Data for Corporate Fraud Detection

2018 | Journal of Management Information Systems | Citations: 67

Authors: Wei Dong; Shaoyi Liao; Zhongju Zhang

Abstract: Corporate fraud can lead to significant financial losses and cause immeasurable ... Expand

Abstract: Corporate fraud can lead to significant financial losses and cause immeasurable damage to investor confidence and the overall economy. Detection of such frauds is a time-consuming and challenging task. Traditionally, researchers have been relying on financial data and/or textual content from financial statements to detect corporate fraud. Guided by systemic functional linguistics (SFL) theory, we propose an analytic framework that taps into unstructured data from financial social media platforms to assess the risk of corporate fraud. We assemble a unique data set including 64 fraudulent firms and a matched sample of 64 nonfraudulent firms, as well as the social media data prior to the firm's alleged fraud violation in Accounting and Auditing Enforcement Releases (AAERs). Our framework automatically extracts signals such as sentiment features, emotion features, topic features, lexical features, and social network features, which are then fed into machine learning classifiers for fraud detection. We evaluate and compare the performance of our algorithm against baseline approaches using only financial ratios and language-based features respectively. We further validate the robustness of our algorithm by detecting leaked information and rumors, testing the algorithm on a new data set, and conducting an applicability check. Our results demonstrate the value of financial social media data and serve as a proof of concept of using such data to complement traditional fraud detection methods. Collapse

Topics: social media fraud detection social network accounting database system

Methods: computational algorithm support vector machine machine learning applicability check design science

Theories: systemic functional linguistics theory

Stakeholder Analyses of Firm-Related Web Forums: Applications in Stock Return Prediction

2015 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Zimbra, David; Chen, Hsinchun; Lusch, Robert F.

Abstract: In this study, we present stakeholder analyses of firm-related web forums. Prior ... Expand

Abstract: In this study, we present stakeholder analyses of firm-related web forums. Prior analyses of firm-related forums have considered all participants in the aggregate, failing to recognize the potential for diversity within the populations. However, distinctive groups of forum participants may represent various interests and stakes in a firm worthy of consideration. To perform the stakeholder analyses, the Stakeholder Analyzer system for firm-related web forums is developed following the design science paradigm of information systems research. The design of the system and its approach to stakeholder analysis is guided by two kernel theories, the stakeholder theory of the firm and the systemic functional linguistic theory. A stakeholder analysis identifies distinctive groups of forum participants with shared characteristics expressed in discussion and evaluates their specific opinions and interests in the firm. Stakeholder analyses are performed in six major firm-related forums hosted on Yahoo Finance over a 3-month period. The relationships between measures extracted from the forums and subsequent daily firm stock returns are examined using multiple linear regression models, revealing statistically significant indicators of firm stock returns in the discussions of the stakeholder groups of each firm with stakeholder-model-adjusted R2 values reaching 0.83. Daily stock return prediction is also performed for 31 trading days, and stakeholder models correctly predicted the direction of return on 67% of trading days and generated an impressive 17% return in simulated trading of the six firm stocks. These evaluations demonstrate that the stakeholder analyses provided more refined assessments of the firm-related forums, yielding measures at the stakeholder group level that better explain and predict daily firm stock returns than aggregate forum-level information. Collapse

Topics: web forum social network database system accounting evaluation criteria

Methods: regression analysis method sentiment analysis cluster analysis forums posting multiple linear regression

Theories: transaction cost economics stakeholder theory systemic functional linguistics theory

Social Media Semantics: Analysing meanings in multimodal online conversations

2014 | International Conference on Information Systems | Citations: 0

Authors: Mehmet, Mehmet; Clarke, Rodney; Kautz, Karlheinz

Abstract: ... Expand

Abstract: Collapse

Topics: social media IS discipline social network hypertext user experience

Methods: social semiotic case study design artifact business process modeling theory development

Theories: semiotic theory systemic functional linguistics theory theory of signs theory of affordance

Eliciting tacit knowledge about requirement analysis with a Grammar-targeted Interview Method (GIM)

2010 | European Journal of Information Systems | Citations: 0

Authors: Zappavigna, Michele; Patrick, Jon

Abstract: The assumption that tacit knowledge cannot be articulated remains dominant in kn ... Expand

Abstract: The assumption that tacit knowledge cannot be articulated remains dominant in knowledge elicitation. This paper, however, claims that linguistic theory does not support such a position and that language should not be factored out of accounts of tacit knowledge. We argue that bib27Polanyi's (1966, p. 4) widely cited notion that ‘we know more than we can tell’ uses a folk model of language. This model does not acknowledge the linguistic patterns that competent language speakers deploy without direct awareness. This paper draws upon Systemic Functional Linguistics (SFL) to propose a Grammar-targeted Interview Method (GIM). The GIM uses SFL to unpack linguistic patterning, which we refer to as ‘under-representation’, to reveal tacit assumptions. It is a strategy that can be applied within a traditional interview method when the interviewer feels that there is confusion resulting from assumptions, such as those often embedded in terminology, that have not been directly expressed. This paper reports findings from an empirical study of tacit knowledge about requirements analysis in a Content Management System redevelopment. We compared the GIM with a Content-motivated Interview Method (CMIM) and show that, when the GIM is used, interviewees respond with less nominalised talk, that is the less nominalised content has more meaning unpacked as verbs and agents rather than hidden tacitly in nouns. Collapse

Topics: implicit knowledge requirements analysis user requirement computer hardware content management system

Methods: qualitative interview parametric test repeated measures ANOVA data modeling linguistic analysis

Theories: semiotic theory systemic functional linguistics theory

Cybergate: A Design Framework and System for Text Analysis of Computer-Mediated Communication

2008 | Management Information Systems Quarterly | Citations: 7

Authors: Abbasi, Ahmed; Chen, Hsinchun

Abstract: Content analysis of computer-mediated communication (CMC) is important for evalu ... Expand

Abstract: Content analysis of computer-mediated communication (CMC) is important for evaluating the effectiveness of electronic communication in various organizational settings. CMC text analysis relies on systems capable of providing suitable navigation and knowledge discovery functionalities. However, existing CMC systems focus on structural features, with little support for features derived from message text. This deficiency is attributable to the informational richness and representational complexities associated with CMC text. In order to address this shortcoming, we propose a design framework for CMC text analysis systems. Grounded in systemic functional linguistic theory, the proposed framework advocates the development of systems capable of representing the rich array of information types inherent in CMC text. It also provides guidelines regarding the choice of features, feature selection, and visualization techniques that CMC text analysis systems should employ. The CyberGate system was developed as an instantiation of the design framework. CyberGate incorporates a rich feature set and complementary feature selection and visualization methods, including the writeprints and ink blots techniques. An application example was used to illustrate the system's ability to discern important patterns in CMC text. Furthermore, results from numerous experiments conducted in comparison with benchmark methods confirmed the viability of CyberGate's features and techniques. The results revealed that the CyberGate system and its underlying design framework can dramatically improve CMC text analysis capabilities over those provided by existing systems. Collapse

Topics: electronic mail anonymity Java frequency of use

Methods: support vector machine experiment meta design design framework qualitative content analysis

Theories: systemic functional linguistics theory

Eliciting Tacit Knowledge from Spoken Discourse

2004 | Americas Conference on Information Systems | Citations: 0

Authors: Zappavigna-Lee, Michele; Patrick, Jon

Abstract: Information Systems research has proposed a range of knowledge elicitation and r ... Expand

Abstract: Information Systems research has proposed a range of knowledge elicitation and requirements analysis techniques, few of which apply specific strategies for eliciting implicit knowledge from participants. This paper demonstrates a methodology for eliciting tacit knowledge from the spoken discourse of organisational participants through directed interviews. It argues that Polanyi’s (1966:4) widely cited notion that “we know more than we can tell” represents an impoverished model of language that does not acknowledge the implicit component of discourse. An argument is made to characterise tacit knowledge as implicit meaning-making. Our conception of tacit knowledge as implicit meaning-making is aligned with Polanyi’s model in the sense that it acknowledges that tacit knowing is a subsidiary process, however we argue that such an implicit process is also present in making meaning with language. We may articulate what we know implicitly through patterns and features of language to which we do not directly attend. Hence analysis of language appears a means for understanding and eliciting tacit knowledge. The present research provides a case-study demonstrating a practical application of such analysis. Our response is to extend Polanyi: “we tell more than we realise we know”. Collapse

Topics: implicit knowledge digital media requirements analysis knowledge acquisition

Methods: qualitative interview linguistic analysis case study experiment

Theories: theory of language systemic functional linguistics theory