Making it Better: Value Perceptions of Usability Workshops in Education Outreach

2023 | International Conference on Information Systems | Citations: 0

Authors: Rajala, Joni; Iivari, Netta; Kinnula, Marianne

Abstract: IS Education outreach programs are important today, as the need for Information ... Expand

Abstract: IS Education outreach programs are important today, as the need for Information Technology (IT) professionals has risen in the last decades. Information Systems (IS) outreach efforts are often short lived and rely heavily on interested individuals to make them happen. This study looks at one case in Finland, where two universities collaborate with a company to create usability improvement workshops to upper secondary school students. By interviewing the stakeholders, this study aims to map the value for each stakeholder group, and their reasons for participating in the studied outreach project. Service dominant logic and value co-creation are used as the theoretical framework to categorize stakeholders' value expectations, perceptions, and propositions. The paper reports the value experienced by each stakeholder group, compares those to what others expect them to gain, and seeks to find ways to create outreach programs that benefit all participants. Collapse

Topics: usability participatory design IS education marketing management IT career

Methods: qualitative interview case study design methodology qualitative content analysis qualitative coding

Unlocking the Potential of ChatGPT: A Grounded Theory Exploration of its Impact on the Business Landscape

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Sirithumgul, Pornpat

Abstract: ChatGPT is an AI tool that performs natural language processing (NLP) tasks, pr ... Expand

Abstract: ChatGPT is an AI tool that performs natural language processing (NLP) tasks, primarily language generation. Its standout feature is question answering, providing quick and accurate answers to a wide range of questions. This paper aims to demonstrate the use of ChatGPT for data collection and to examine its impact on the business landscape through a grounded theory approach. The analysis, based on grounded theory principles, highlights ChatGPT's ability to automate and augment human tasks across multiple industries. This paper presents examples of 11 industries, including marketing and advertising, retail, healthcare, banking and finance, news and media, e-commerce, social media, legal services, hospitality, telecommunications, and government, that are likely to be affected by ChatGPT. It also suggests changes in customer behavior and business models being adapted to the disruptions caused by ChatGPT in these industries. Collapse

Topics: artificial intelligence marketing management communication service infrastructure social media electronic commerce

Methods: chatGPT qualitative coding grounded theory natural language processing qualitative content analysis

Examining interdependence between product users and employees in online user communities: The role of employee-generated content

2021 | Journal of Strategic Information Systems | Citations: 0

Authors: (Kevin) Yan, Jie; Leidner, Dorothy E.; Benbya, Hind; Zou, Weifei

Abstract: Firm-sponsored online user communities have become product innovation and suppor ... Expand

Abstract: Firm-sponsored online user communities have become product innovation and support hubs of strategic importance to firms. Product users and host firm employees comprise the participants of firm-sponsored online user communities. The online user community provides a forum wherein the product users and firm employees discuss questions, problems or issues resulting from the use of host firms’ products. Extant research on online user communities has largely focused on either product users or employees and has examined the various dynamics that ensue from each entity’s community participation. This paper seeks to investigate the interdependence between the two entities in the communities and, in particular, how product users’ reading of employee-generated content influences subsequent knowledge contribution by product users as well as employees. Analyzing data from an online user community over a two-year period, our study shows that employees whose content is read by product users generate additional content and product users who read employee content themselves contribute more knowledge to the community. Thus, the reading of content is not entirely a passive, individual action that only affects the reader. On the contrary, reading sparks additional knowledge contribution by the reader and having readers sparks additional knowledge contribution by the original source of the content, thereby creating a sustainable online user community. Collapse

Topics: database system online community organizational culture knowledge sharing blog

Methods: longitudinal research document generation descriptive statistic qualitative interview statistical hypothesis test

Theories: social exchange theory

Using Word Embeddings to Deter Intellectual Property Theft through Automated Generation of Fake Documents

2021 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Abdibayev, Almas; Chen, Dongkai; Chen, Haipeng; Poluru, Deepti; Subrahmanian, V. S.

Abstract: Theft of intellectual property is a growing problem—one that is exacerbated by t ... Expand

Abstract: Theft of intellectual property is a growing problem—one that is exacerbated by the fact that a successful compromise of an enterprise might only become known months after the hack. A recent solution called FORGE addresses this problem by automatically generating N “fake” versions of any real document so that the attacker has to determine which of the N + 1 documents that they have exfiltrated from a compromised network is real. In this article, we remove two major drawbacks in FORGE: (i) FORGE requires ontologies in order to generate fake documents—however, in the real world, ontologies, especially good ontologies, are infrequently available. The WE-FORGE system proposed in this article completely eliminates the need for ontologies by using distance metrics on word embeddings instead. (ii) FORGE generates fake documents by first identifying “target” concepts in the original document and then substituting “replacement” concepts for them. However, we will show that this can lead to sub-optimal results (e.g., as target concepts are selected without knowing the availability and/or quality of the replacement concepts, they can sometimes lead to poor results). Our WE-FORGE system addresses this problem in two possible ways by performing a joint optimization to select concepts and replacements simultaneously. We conduct a human study involving both computer science and chemistry documents and show that WE-FORGE successfully deceives adversaries. Collapse

Topics: information retrieval knowledge representation IT security Google+ innovation management

Methods: optimization model word embedding term frequency–inverse document frequency experiment ontological modelling

Mining Bilateral Reviews for Online Transaction Prediction: A Relational Topic Modeling Approach

2021 | Information Systems Research | Citations: 3

Authors: Chen, Jiawei; Yang, Yinghui (Catherine); Liu, Hongyan

Abstract: On most e-commerce platforms, reviews are often written by buyers to evaluate se ... Expand

Abstract: On most e-commerce platforms, reviews are often written by buyers to evaluate sellers or products offered by sellers. In recent years, more and more platforms allowing both buyers and sellers to write reviews for each other have emerged. These bilateral reviews are important information sources in the decision-making process of both buyers and sellers but have not been properly investigated in the literature before. We develop a comprehensive relational topic modeling approach to analyze bilateral reviews to predict transaction results. The prediction results will enable the platform to increase the chance that the buyer and seller reach a transaction by presenting buyers with offerings that are more likely to lead to a transaction. Within the framework of the relational topic model, we embed a topic structure with both shared and corpus-specific topics to better handle text corpora generated from different sources. Our model facilitates the extraction of the appropriate topic structure from different document collections that helps enhance the transaction prediction performance. Comprehensive experiments conducted on real-world data sets collected from sharing economy platforms demonstrate that our new model significantly outperforms other alternatives. The robust results obtained from multiple sets of comparisons demonstrate the value of bilateral reviews if they are processed properly. Our approach can be applied to many platforms where bilateral reviews are available. Collapse

Topics: Airbnb decision making logistics management sharing platform recommender system

Methods: topic model experiment machine learning logistic regression robustness check

Theories: transaction cost economics

Notation-Agnostic Subprocess Modeling for Adaptive Case Management

2021 | International Conference on Business Informatics | Citations: 0

Authors: Tenschert, Johannes; Dunzer, Sebastian; Matzner, Martin

Abstract: Even though knowledge work comprises tasks that cannot be modeled a priori, some ... Expand

Abstract: Even though knowledge work comprises tasks that cannot be modeled a priori, some structure for routine work or an outlined course of action is often necessary. Knowledge workers perform structured and ad-hoc activities – and a wide range of requirements typically yields many tools. To avoid redundant and scattered process information, a single system of record capable of consolidating flexibility and modeling of different aspects of processes is desirable. For knowledge-intensive processes, we outline how to combine process models for structured routine aspects with the ad-hoc activities and flexibility of social software in the same context. Therefore, a case comprises modeled subprocesses as well as ad-hoc activities and artifacts. The shared context allows to transparently combine aspects and deviate from predefined models. We prototypically implement our approach and show that ad-hoc project management and different notations can be applied within the same case. Collapse

Topics: knowledge work process mining implicit knowledge social media project management

Methods: modeling language business process model and notation business process modeling literature study Petri net

Theories: speech act theory

Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics

2019 | International Conference on Business Informatics | Citations: 0

Authors: Lommel, Lasse; Riebeling, Meike; Funk, Burkhardt; Junginger, Christian

Abstract: Traditional unsupervised topic modeling approaches like Latent Dirichlet Allocat ... Expand

Abstract: Traditional unsupervised topic modeling approaches like Latent Dirichlet Allocation (LDA) lack the ability to classify documents into a predefined set of topics. On the other hand, supervised methods require significant amounts of labeled data to perform well on such tasks. We develop a new unsupervised method based on word embeddings to classify documents into predefined topics. We evaluate the predictive performance of this novel approach and compare it to seeded LDA. We use a real-world dataset from online advertising, which is comprised of markedly short documents. Our results indicate the two methods may complement one another well, leading to remarkable sensitivity and precision scores of ensemble learners trained thereupon. Collapse

Topics: website electronic commerce social media search engine search engine marketing

Methods: term frequency–inverse document frequency word embedding topic model latent dirichlet allocation word2vec

The determinants of crowdfunding success: A semantic text analytics approach

2016 | Decision Support Systems | Citations: 33

Authors: Yuan, Hui; Lau, Raymond Y. K.; Xu, Wei

Abstract: In the era of the Social Web, crowdfunding has become an increasingly more impor ... Expand

Abstract: In the era of the Social Web, crowdfunding has become an increasingly more important channel for entrepreneurs to raise funds from the crowd to support their startup projects. Previous studies examined various factors such as project goals, project durations, and categories of projects that might influence the outcomes of the fund raising campaigns. However, textual information of projects has rarely been studied for analyzing crowdfunding successes. The main contribution of our research work is the design of a novel text analytics-based framework that can extract latent semantics from the textual descriptions of projects to predict the fund raising outcomes of these projects. More specifically, we develop the Domain-Constraint Latent Dirichlet Allocation (DC-LDA) topic model for effective extraction of topical features from texts. Based on two real-world crowdfunding datasets, our experimental results reveal that the proposed framework outperforms a classical LDA-based method in predicting fund raising success by an average of 11% in terms of F1 score. The managerial implication of our research is that entrepreneurs can apply the proposed methodology to identify the most influential topical features embedded in project descriptions, and hence to better promote their projects and improving the chance of raising sufficient funds for their projects. Collapse

Topics: crowdsourcing crowdfunding semantic network digital news website

Methods: topic model natural language processing support vector machine machine learning computational algorithm

An Exploratory Study On The Appropriateness Of Latent Dirichlet Allocation For Automatic Discovery Of Product Associations From User-Generated Content

2013 | European Conference On Information Systems | Citations: 0

Authors: Putzke, Johannes; Fischbach, Kai; Schoder, Detlef

Abstract: Latent Dirichlet Allocation (LDA) is a method that can be used to generate word ... Expand

Abstract: Latent Dirichlet Allocation (LDA) is a method that can be used to generate word association networks from unstructured text documents. However, no study has yet examined the applicability of LDA for deriving product associations from user-generated content. In this work, we apply LDA on 9,529 unstructured and uncategorized McDonald’s product reviews that were crawled from a German online review platform. We evaluate the applicability of LDA for deriving product associations from user-generated content. For this reason, we conducted a survey among 95 Information Systems undergraduate students about their associations with 17 McDonald’s-related nouns. Results indicate that LDA is a valid method for deriving product associations from user-generated content. Collapse

Topics: user-generated content online review missing data computer system brainstorming

Methods: product review machine learning literature study descriptive statistic survey

Applied Learning with the Virtual Teaching Assistant

2011 | Americas Conference on Information Systems | Citations: 0

Authors: Parker, Kevin R; Davey, Bill

Abstract: The relevance of a university degree depends upon the ability of graduates to ap ... Expand

Abstract: The relevance of a university degree depends upon the ability of graduates to apply their learning in complex problem solving situations. This ability should be taught and practiced wherever possible in the curriculum, but often requires a substantial commitment of face-to-face instructor time. However, in many disciplines software systems can be constructed to provide or supplement relevant learning environments. This paper describes a system developed through the use of the design and development research technique. The case shows how collaborative, intentional, and project-based learning can be incorporated in and stimulated by design decisions informed by the research technique. The paper provides details about a system built around realistic role playing and supported by a range of tools. The combination of a scenario with a real-world context, along with a software system that evaluates student work as well as interactively mentoring students, provides the services of a virtual teaching assistant. Collapse

Topics: accounting

Methods: document generation