Examining the Impact of Algorithmic Control on Uber Drivers' Technostress

2022 | Journal of Management Information Systems | Citations: 29

Authors: Cram, W. Alec; Wiener, Martin; Tarafdar, Monideepa; Benlian, Alexander

Abstract: This study examines how the use of algorithmic control within gig economy platfo ... Expand

Abstract: This study examines how the use of algorithmic control within gig economy platforms relates to the well-being and behavior of workers. Specifically, we explore how two different forms of algorithmic control—gatekeeping and guiding—correspond with (positive) challenge technostressors and (negative) threat technostressors experienced by Uber drivers. We also examine the moderating impact of algorithmic control transparency on these relationships, as well as the outcomes of technostressors in terms of continuance intentions and workaround use. Based on a survey of 621 U.S.-based Uber drivers, we find that gatekeeping and guiding algorithmic control positively relate to both challenge and threat technostressors. The study bridges the literature on control and technostress by conceptualizing algorithmic control as a condition that puts workers under stress. This stress is found to contribute to important behavioral consequences pertaining to both continuance intentions and workaround use. Findings from our work suggest that gig economy organizations can use algorithmic control to enhance challenge technostressors for their workers, thereby contributing to the cultivation of a more committed workforce. Furthermore, we find evidence disputing the assumption that algorithmic control transparency can mitigate the negative effects of threat technostressors. Collapse

Topics: Uber technostress platform worker ride sharing platform mobile application

Methods: computational algorithm survey SEM tool structural equation modeling survey design

Fighting money laundering with technology: A case study of Bank X in the UK

2018 | Decision Support Systems | Citations: 0

Authors: Demetis, Dionysios S.

Abstract: This paper presents a longitudinal interpretive case study of a UK bank's effort ... Expand

Abstract: This paper presents a longitudinal interpretive case study of a UK bank's efforts to combat Money Laundering (ML) by expanding the scope of its profiling of ML behaviour. The concept of structural coupling, taken from systems theory, is used to reflect on the bank's approach to theorize about the nature of ML-profiling. The paper offers a practical contribution by laying a path towards the improvement of money laundering detection in an organizational context while a set of evaluation measures is extracted from the case study. Generalizing from the case of the bank, the paper presents a systems-oriented conceptual framework for ML monitoring. Collapse

Topics: marketing management organizational context electronic mail Facebook information privacy concern

Methods: case study longitudinal research qualitative interview conceptual modelling experiment

Theories: general systems theory

Social Media Operationalized for GIS: The Prequel

2017 | Americas Conference on Information Systems | Citations: 0

Authors: Corso, Anthony; Alsudais, Abdulkareem

Abstract: With social media a de facto global communication channel used to disseminate ne ... Expand

Abstract: With social media a de facto global communication channel used to disseminate news, entertainment, and one’s self-revelations, the latter contains double-talk, peculiar insight, and contextual observation about real-world events. The primary objective is to propose a novel pipeline to classify a tweet as either “useful” or “not useful” by using widely-accepted Natural Language Processing (NLP) techniques, and measure the effect of such method based on the change in performance of a Geographical Information System (GIS) artifact. A 1,000 tweet sample is manually tagged and compared to an innovative social media grammar applied by a rule-based social media NLP pipeline. Evaluation underpins answering, prior to content analysis of a tweet, does a method exist to support identifying a tweet as “useful” for subsequent processing? Indeed, “useful” tweet identification via NLP returned precision of 0.9256, recall of 0.6590, and F-measure of 0.7699; consequently GIS social media processing increased 0.2194 over baseline. Collapse

Topics: Twitter social media criminality information retrieval

Methods: natural language processing experiment machine learning design artifact qualitativ observational study

A Big Data Analytics Method for Tourist Behaviour Analysis

2017 | Information & Management | Citations: 12

Authors: Miah, Shah Jahan; Vu, Huy Quan; Gammack, John; McGrath, Michael

Abstract: Big data generated across social media sites have created numerous opportunities ... Expand

Abstract: Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed. Collapse

Topics: big data decision making social media Flickr decision support

Methods: design artifact time series analysis longitudinal research experiment computational algorithm

Principles of Goal Discovery in IT Governance Policy Documents

2016 | Americas Conference on Information Systems | Citations: 0

Authors: Vlas, Radu; Lee, Jong Seok

Abstract: It is important that organizations comply with IT governance policies enforced b ... Expand

Abstract: It is important that organizations comply with IT governance policies enforced by IT governance bodies. A challenge exists as organizations often struggle with understanding IT governance policies written in natural language. Reading and analyzing IT policy documents is often time consuming and inefficient. Motivated by this challenge, we develop principles of goal discovery, apply them to the analysis of IT governance policy documents and develop taxonomy for IT policy documents. Furthermore, we automate the goal discovery process by using a keyword-based approach. Our goal discovery principles along with the new taxonomy help organizations better understand IT governance policies and aid in resolving compliance issues in managing various IT assets. This research contributes to the body of design science research by developing goal mining heuristics for analyzing IT governance policy documents. It also contributes to practice by proposing an efficient way to analyze and understand IT governance policies. Collapse

Topics: IT governance IT policy systems analysis website interorganizational system

Methods: machine learning natural language processing GATE design science computational algorithm

Big Social Data and GIS: Visualize Predictive Crime

2016 | Americas Conference on Information Systems | Citations: 1

Authors: Corso, Anthony; Alsudais, Kareem; Hilton, Brian

Abstract: Social media is a desirable Big Data source used to examine the relationship bet ... Expand

Abstract: Social media is a desirable Big Data source used to examine the relationship between crime and social behavior. Observation of this connection is enriched within a geographic information system (GIS) rooted in environmental criminology theory, and produces several different results to substantiate such a claim. This paper presents the construction and implementation of a GIS artifact producing visualization and statistical outcomes to develop evidence that supports predictive crime analysis. An information system research prototype guides inquiry and uses crime as the dependent variable and a social media tweet corpus, operationalized via natural language processing, as the independent variable. This inescapable realization of social media as a predictive crime variable is prudent; researchers and practitioners will better appreciate its capability. Inclusive visual and statistical results are novel, represent state-of-the-art predictive analysis, increase the baseline R2 value by 7.26%, and support future predictive crime-based research when frontrun with real-time social media. Collapse

Topics: Twitter criminality social media website geographic information system

Methods: natural language processing experiment longitudinal research autocorrelation analysis ordinary least square

Semantics-based event log aggregation for process mining and analytics

2015 | Information Systems Frontiers | Citations: 0

Authors: Deokar, Amit V.; Tao, Jie

Abstract: In highly complex and flexible environments, event logs tend to exhibit high lev ... Expand

Abstract: In highly complex and flexible environments, event logs tend to exhibit high levels of heterogeneity, and clustering-based methods are candidate techniques for simplifying the mined process models from the process observations. To compensate for the information loss occurring during clustering, semantic information from event logs may be extracted and organized in the form of knowledge structures such as process ontologies using methods of ontology learning. In this article, we propose an overall computational framework for event log pre-processing, and then focus on a specific component of the framework, namely event log aggregation. We develop a detailed system architecture for this component, along with an implemented and evaluated research prototype SemAgg. We use phrase-based semantic similarity between normalized event names to aggregate event logs in a hierarchical form. We discuss the practical implications of this work for learning lower level process ontology classes as well as performing further process mining and analytics. Collapse

Topics: knowledge representation process mining Java enterprise architecture business process management

Methods: event log experiment design artifact GATE computational algorithm

Text Mining for Studying Management’s Confidence in IPO Prospectuses and IPO Valuations

2015 | Americas Conference on Information Systems | Citations: 0

Authors: Tao, Jie; Deokar, Amit V

Abstract: Understanding pricing strategies in the context of the Initial Public Offering ( ... Expand

Abstract: Understanding pricing strategies in the context of the Initial Public Offering (IPO) process has been receiving much attention. Most prior studies have however focused on information sources from post issuance periods, and understanding such strategies from the management’s perspective during the IPO process is still an open research issue. Form 424 variants, as finalized IPO prospectus approved by Security Exchange Committee (SEC), contain rich and genuine information about the issuing firms. In this study, we analyze the inter-relationships between the management’s confidence (through the proxy of sentiments expressed in textual contents in the Management’s Discussion & Analysis (MD&A) sections in the prospectus) and the pre-/post-IPO valuations. We develop an analytical framework namely FOCAS-IE (Feature-Oriented, Context-Aware, Systematic Information Extraction) to derive sentiments from the MD&A sections. Further, we construct predictive models using information extracted using FOCAS-IE to predict IPO pricings. The results have shown to outperform results from prior related studies. Collapse

Topics: knowledge representation price management artificial intelligence organizational productivity web crawling

Methods: machine learning ontological modelling natural language processing conceptual modelling information extraction

Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis

2014 | Decision Support Systems | Citations: 0

Authors: Lau, Raymond Y. K.; Li, Chunping; Liao, Stephen S. Y.

Abstract: In the era of Web 2.0, there has been an explosive growth of consumer-contribute ... Expand

Abstract: In the era of Web 2.0, there has been an explosive growth of consumer-contributed comments at social media and electronic commerce Web sites. Applying state-of-the-art social analytics methodology to analyze the sentiments embedded in these consumer comments facilitates both firms' product design strategies and individual consumers' comparison shopping. However, existing social analytics methods often adopt coarse-grained and context-free sentiment analysis approaches. Consequently, these methods may not be effective enough to support firms and consumers' demands of fine-grained extraction of market intelligence from social media. Guided by the design science research methodology, the main contribution of our research is the design of a novel social analytics methodology that can leverage the sheer volume of consumer reviews archived at social media sites to perform a fine-grained extraction of market intelligence. More specifically, the proposed social analytics methodology is underpinned by a novel semi-supervised fuzzy product ontology mining algorithm. Evaluated based on real-world social media data, our prototype system shows remarkable performance improvement over a baseline ontology learning system and a context-free sentiment analysis system. The managerial implication of our research is that firms can apply the proposed social analytics methodology to tap into the collective social intelligence on the Web, and hence improve their product design and marketing strategies. Collapse

Topics: knowledge representation social media site online learning system computer hardware laptop computer

Methods: ontological modelling sentiment analysis computational algorithm experiment design artifact

Theories: function-behaviour-structure model

Web 2.0 Environmental Scanning and Adaptive Decision Support for Business Mergers and Acquisitions

2012 | Management Information Systems Quarterly | Citations: 0

Authors: Lau, Raymond Y. K.; Liao, Stephen S. Y.; Wong, K. F.; Chiu, Dickson K. W.

Abstract: Globalization has triggered a rapid increase in cross-border mergers and acquisi ... Expand

Abstract: Globalization has triggered a rapid increase in cross-border mergers and acquisitions (M&As). However, research shows that only 17 percent of cross-border M&As create shareholder value. One of the main reasons for this poor track record is top management’s lack of attention to nonfinancial aspects (e.g., sociocultural aspects) of M&As. With the rapid growth of Web 2.0 applications, online environmental scanning provides top executives with unprecedented opportunities to tap into collective web intelligence to develop better insights about the sociocultural and political–economic factors that cross-border M&As face. Grounded in Porter’s five forces model, one major contribution of our research is the design of a novel due diligence scorecard model that leverages collective web intelligence to enhance M&A decision making. Another important contribution of our work is the design and development of an adaptive business intelligence (BI) 2.0 system underpinned by an evolutionary learning approach, domain-specific sentiment analysis, and business relation mining to operationalize the aforementioned scorecard model for adaptive M&A decision support. With Chinese companies’ cross-border M&As as the business context, our experimental results confirm that the proposed adaptive BI 2.0 system can significantly aid decision makers under different M&A scenarios. The managerial implication of our findings is that firms can apply the proposed BI 2.0 technology to enhance their strategic decision making, particularly when making cross-border investments in targeted markets for which private information may not be readily available. Collapse

Topics: decision making BASIC decision support usability blog

Methods: experiment sentiment analysis computational algorithm machine learning experimental group

Theories: co-evolution theory