The role of system-use practices for sustaining motivation in crowdsourcing: A technology-in-practice perspective

2023 | Information Systems Journal | Citations: 0

Authors: Alam, Sultana Lubna; Sun, Ruonan

Abstract: The success of crowdsourcing (CS) systems depends on sustained participation, wh ... Expand

Abstract: The success of crowdsourcing (CS) systems depends on sustained participation, which is an ongoing challenge for the majority of CS providers. Unfortunately, participants are frequently demotivated by technical difficulties and the incorrect use of CS systems, which can result in CS failure. Although the literature generally assumes that sustained participation in CS is determined by a shift between intrinsic and extrinsic motivation, the role of system-use practices in facilitating such a shift remains unknown. We explore how CS system-use practices influence participants' sustained motivation, evolving from initiation to progression to sustention. Using the notion of technology-in-practice as a lens, we develop and examine a process model using an in-depth case study of a large-scale ongoing CS project, the Australian Newspaper Digitisation Program. The findings suggest that CS participants' motivation is shaped by an evolving combination of three basic components (i.e., contextual condition, outcome and action intensity) and mediated by two types of system-use practice (i.e., passive and active). Passive-use practices facilitate sustaining motivation from initiation to progression, whereas active-use practices have a key role in sustention. Our study contributes to the emerging literature on the substantial role of system-use practices in sustaining motivation, resulting in sustained participation. The findings also offer actionable insights into improving the viability of CS systems in retaining and motivating continuous and increased contributions from participants. Collapse

Topics: information system use strategic management intrinsic motivation

Methods: qualitative interview case study business process modeling theory development synthesis

Theories: technology acceptance theory

COVERT RESISTANCE AGAINST ALGORITHMIC CONTROL ON ONLINE LABOR PLATFORMS – A SYSTEMATIC LITERATURE REVIEW

2023 | European Conference On Information Systems | Citations: 0

Authors: Jiang, Jennifer

Abstract: Online labor platforms (OLPs) such as Uber or Upwork heavily rely on algorithms ... Expand

Abstract: Online labor platforms (OLPs) such as Uber or Upwork heavily rely on algorithms instead of human managers to control workers' behavior. While algorithmic control (AC) allows platform providers to control their workers efficiently, it is often perceived by workers as a tighter control (compared to human-based control) which increases their motivation to resist. Especially covert resistance (i.e., workers' hard-to-observe oppositional actions) provides essential insights into how workers deal with AC that affect platforms' longevity. In this study, we conducted a systematic literature review to develop a theoretical framework showing how and why workers perform covert resistance against AC. Further, our analysis reveals the enabling role of sensemaking for diverse forms of covert resistance. Overall, our study expands the literature on AC by shedding light on the formation of workers' covert resistance. Therefore, we offer platform providers and policymakers crucial insights to create fairer working environments for workers under AC. Collapse

Topics: resistance to change sensemaking organizational control organizational change privacy

Methods: literature sample theory development computational algorithm structured literature research literature study

Identifying User Innovations through AI in Online Communities– A Transfer Learning Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Resch, Christian; Streibel, Julian; Feiter, Tim; Kock, Alexander

Abstract: Identifying innovative users and their ideas is crucial, for example, in crowds ... Expand

Abstract: Identifying innovative users and their ideas is crucial, for example, in crowdsourcing. But, analyzing large amounts of unstructured textual data from such online communities poses a challenge for organizations. Therefore, researchers started developing automated approaches to identify innovative users. Our study introduces an advanced machinelearning approach that minimizes manual work by combining transfer learning with a transformer-based design. We train the model on separate datasets, including an online maker community and various internet texts. The maker community posts represent need-solution pairs, which express needs and describe fitting prototypes. Then, we transfer the model and identify potential user innovations in a kitesurfing community. We validate the identified posts by manually checking a subsample and analyzing how words affect the model's classification decision. This study contributes to the growing portfolio of user innovation identification by combining state-of-the-art natural language processing and transfer learning to improve automated identification. Collapse

Topics: innovation management online community internet technology computer hardware open source

Methods: machine learning BERT qualitative coding transformer model natural language processing

Algorithms as co-workers: Human algorithm role interactions in algorithmic work

2023 | Information Systems Journal | Citations: 0

Authors: Tarafdar, Monideepa; Page, Xinru; Marabelli, Marco

Abstract: In algorithmic work, algorithms execute operational and management tasks such as ... Expand

Abstract: In algorithmic work, algorithms execute operational and management tasks such as work allocation, task tracking and performance evaluation. Humans and algorithms interact with one another to accomplish work so that the algorithm takes on the role of a co-worker. Human–algorithm interactions are characterised by problematic issues such as absence of mutually co-constructed dialogue, lack of transparency regarding how algorithmic outputs are generated, and difficulty of over-riding algorithmic directive – conditions that create lack of clarity for the human worker. This article examines human–algorithm role interactions in algorithmic work. Drawing on the theoretical framing of organisational roles, we theorise on the algorithm as role sender and the human as the role taker. We explain how the algorithm is a multi-role sender with entangled roles, while the human as role taker experiences algorithm-driven role conflict and role ambiguity. Further, while the algorithm records all of the human's task actions, it is ignorant of the human's cognitive reactions – it undergoes what we conceptualise as ‘broken loop learning’. The empirical context of our study is algorithm-driven taxi driving (in the United States) exemplified by companies such as Uber. We draw from data that include interviews with 15 Uber drivers, a netnographic study of 1700 discussion threads among Uber drivers from two popular online forums, and analysis of Uber's web pages. Implications for IS scholarship, practice and policy are discussed. Collapse

Topics: Uber mobile application ride sharing platform global navigation satellite system web page

Methods: computational algorithm qualitative interview qualitative coding netnography theory development

Theories: organizational role theory role theory

Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality

2023 | Information Systems Research | Citations: 0

Authors: Yang, Kai; Lau, Raymond Y. K.; Abbasi, Ahmed

Abstract: Analysts, managers, and policymakers are interested in predictive analytics capa ... Expand

Abstract: Analysts, managers, and policymakers are interested in predictive analytics capable of offering better foresight. It is generally accepted that in forecasting scenarios involving organizational policies or consumer decision making, personal characteristics, including personality, may be an important predictor of downstream outcomes. The inclusion of personality features in forecasting models has been hindered by the fact that traditional measurement mechanisms are often infeasible. Text-based personality detection has garnered attention because of the public availability of digital textual traces. However, the text machine learning space has bifurcated into two branches: feature-based methods relying on manually crafted human intuition, or deep learning language models that leverage big data and compute, the main commonality being that neither branch generates accurate personality assessments, thereby making personality measures infeasible for downstream forecasting applications. In this study, we propose DeepPerson, a design artifact for text-based personality detection that bridges these two branches by leveraging concepts from relevant psycholinguistic theories in conjunction with advanced deep learning strategies. DeepPerson incorporates novel transfer learning and hierarchical attention network methods that use psychological concepts and data augmentation in conjunction with person-level linguistic information. We evaluate the utility of the proposed artifact using an extensive design evaluation on three personality data sets in comparison with state-of-the-art methods proposed in academia and industry. DeepPerson can improve detection of personality dimensions by 10–20 percentage points relative to the best comparison methods. Using case studies in the finance and health domains, we show that more accurate text-based personality detection can translate into significant improvements in downstream applications such as forecasting future firm performance or predicting pandemic infection rates. Our findings have important implications for research at the intersection of design and data science, and practical implications for managers focused on enabling, producing, or consuming predictive analytics.History: Olivia Sheng, Senior Editor; Huimin Zhao, Associate Editor.Funding: This work was supported by Oracle for Research (NLP for the Greater Good) and the U.S. National Science Foundation’s Division of Information and Intelligent Systems [Grants BDS-1636933 and IIS-1816504]. The work was also partly supported by the Research Grants Council of the Hong Kong Special Administrative Region [Project: CityU 11507219] and the City University of Hong Kong SRG [Project: 7005780].Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1111. Collapse

Topics: personality Twitter social media pandemic Google+

Methods: machine learning BERT design science deep learning computational algorithm

Theories: big five model

Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multiarmed Bandit Approach

2023 | Information Systems Research | Citations: 3

Authors: Zhou, Tongxin; Wang, Yingfei; Yan, Lu (Lucy); Tan, Yong

Abstract: Online healthcare platforms provide users with various intervention programs to ... Expand

Abstract: Online healthcare platforms provide users with various intervention programs to promote personal wellness. Given the many options available, it’s often difficult for individuals to decide in which intervention to participate, especially when they lack the experience or knowledge to evaluate the interventions. This may discourage individuals’ continuous engagement in online health management. In this study, we are motivated to develop a personalized healthcare recommendation framework to help individuals better discover the interventions that fit their needs. Considering the challenges in intervention adaptation and diversification in a highly dynamic online healthcare environment, we propose an innovative online learning framework that synthesizes deep representation learning and a theory-guided diversity promotion scheme. We evaluate our approach through a real-world data set on users’ intervention participation in an online weight-loss community. Our results provide strong evidence for the effectiveness of our proposed recommendation framework and each of its design components. Our study contributes to the emerging information systems research on prescriptive analytics and the application of business intelligence. The proposed modeling framework and evaluation results offer important implications for multiple stakeholders, including online healthcare platforms, policymakers, and users. History: Ahmed Abbasi, Senior Editor; Huimin Zhao, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grant 71729001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1191. Collapse

Topics: recommender system logistics management online learning system collaborative filtering usability

Methods: deep learning computational algorithm artificial neural network experiment design artifact

Theories: social cognitive theory

A text-mining based cyber-risk assessment and mitigation framework for critical analysis of online hacker forums

2022 | Decision Support Systems | Citations: 0

Authors: Biswas, Baidyanath; Mukhopadhyay, Arunabha; Bhattacharjee, Sudip; Kumar, Ajay; Delen, Dursun

Abstract: Online hacker communities are meeting spots for aspiring and seasoned cybercrimi ... Expand

Abstract: Online hacker communities are meeting spots for aspiring and seasoned cybercriminals where they engage in technical discussions, share exploits and relevant hacking tools to be used in launching cyber-attacks on business organizations. Sometimes, the affected organizations can detect these attacks in advance, with the help of cyberthreat intelligence derived from the explicit and implicit features of hacker communication in these forums. Herein, we proposed a novel text-mining based cyber-risk assessment and mitigation framework, which performs the following critical tasks. (i) Cyber-risk Assessment - to identify hacker expertise (i.e., newbie, beginner, inter­ mediate, and advanced) using explicit and implicit features applying various classification algorithms. Among these features, cybersecurity keywords, sharing of attachments, and sentiments emerged as significant. Further, we found that expert hackers demonstrate leadership in the online forums that eventually serve as communities of practice. Consequently, novice hackers gradually develop their cyber-attack skills through prolonged observa­ tions, interactions, and external influences in this social learning process. (ii) Cyber-risk mitigation – computes financial impact for every {hacker expertise, attack-type} combination, and then by ranking them on a {likelihood, impact} decision-matrix to prioritize mitigation strategies in affected organizations. Through these novel rec­ ommendations, our framework can guide managers to decide on appropriate cybersecurity controls using an {expected loss, probability, attack-type, hacker expertise} metric against financial losses due to cyber-attacks. Collapse

Topics: IT security IT security threat darknet source code logistics management

Methods: classification method sentiment analysis decision tree classification term frequency–inverse document frequency literature study

Theories: social exchange theory social practice theory

Posting versus Replying: The Effects of Instructor Participation in MOOC Discussion Forums

2022 | HICSS | Citations: 0

Authors: Du, Zhao; Wang, Fang; Wang, Shan

Abstract: Previous research has suggested that the frequency of instructor participation i ... Expand

Abstract: Previous research has suggested that the frequency of instructor participation in online discussion forums can affect student participation and subsequently their learning results in online courses. However, the different approaches of instructor participation and their effects have not been well studied, particularly in MOOCs of very large class sizes. This research investigates how instructor participation via posting vs. replying in online discussion forums affects student participation in MOOCs. Our results show that instructors mainly participate through replying rather than posting. However, instructor posting is more effective than instructor replying on enhancing student participation, and this advantage of instructor posting over replying increases with class size. Class size negatively moderates the positive effect of instructor replying, but not that of instructor posting. These findings implicate the necessity of a well-planned participation strategy for instructors to engage in MOOC discussion forums. Collapse

Topics: online learning system

Methods: regression analysis method statistical hypothesis test forums posting post analysis descriptive statistic

Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Ebrahimi, Mohammadreza; Chai, Yidong; Samtani, Sagar; Chen, Hsinchun

Abstract: International dark web platforms operating within multiple geopolitical regions ... Expand

Abstract: International dark web platforms operating within multiple geopolitical regions and languages host a myriad of hacker assets such as malware, hacking tools, hacking tutorials, and malicious source code. Cybersecurity analytics organizations employ machine learning models trained on human-labeled data to automatically detect these assets and bolster their situational awareness. However, the lack of human-labeled training data is prohibitive when analyzing foreign-language dark web content. In this research note, we adopt the computational design science paradigm to develop a novel IT artifact for cross-lingual hacker asset detection (CLHAD). CLHAD automatically leverages the knowledge learned from English content to detect hacker assets in non-English dark web platforms. CLHAD encompasses a novel Adversarial deep representation learning (ADREL) method, which generates multilingual text representations using generative adversarial networks (GANs). Drawing upon the state of the art in cross-lingual knowledge transfer, ADREL is a novel approach to automatically extract transferable text representations and facilitate the analysis of multilingual content. We evaluate CLHAD on Russian, French, and Italian dark web platforms and demonstrate its practical utility in hacker asset profiling, and conduct a proof-of-concept case study. Our analysis suggests that cybersecurity managers may benefit more from focusing on Russian to identify sophisticated hacking assets. In contrast, financial hacker assets are scattered among several dominant dark web languages. Managerial insights for security managers are discussed at operational and strategic levels. Collapse

Topics: darknet IT security knowledge sharing shapley additive explanation spoofing

Methods: machine learning design science design artifact case study deep learning

Does Social Media Accelerate Product Recalls? Evidence from the Pharmaceutical Industry

2022 | Information Systems Research | Citations: 0

Authors: Gao, Yang; Duan, Wenjing; Rui, Huaxia

Abstract: Social media has become a vital platform for voicing product-related experiences ... Expand

Abstract: Social media has become a vital platform for voicing product-related experiences that may not only reveal product defects, but also impose pressure on firms to act more promptly than before. This study scrutinizes the rarely studied relationship between these voices and the speed of product recalls in the context of the pharmaceutical industry in which social media pharmacovigilance is becoming increasingly important for the detection of drug safety signals. Using Federal Drug Administration drug enforcement reports and social media data crawled from online forums and Twitter, we investigate whether social media can accelerate the product recall process in the context of drug recalls. Results based on discrete-time survival analyses suggest that more adverse drug reaction discussions on social media lead to a higher hazard rate of the drug being recalled and, thus, a shorter time to recall. To better understand the underlying mechanism, we propose the information effect, which captures how extracting information from social media helps detect more signals and mine signals faster to accelerate product recalls, and the publicity effect, which captures how firms and government agencies are pressured by public concerns to initiate speedy recalls. Estimation results from two mechanism tests support the existence of these conceptualized channels underlying the acceleration hypothesis of social media. This study offers new insights for firms and policymakers concerning the power of social media and its influence on product recalls. Collapse

Topics: social media Twitter anonymity Google Search database system

Methods: hazard function survival analysis econometric modeling support vector machine robustness check