Perceived Algorithmic Control: Conceptualization and Scale Development

2023 | European Conference On Information Systems | Citations: 1

Authors: Alizadeh, Armin; Hirsch, Felix; Benlian, Alexander; Wiener, Martin; Cram, W.

Abstract: Organizations today leverage big data analytics combined with modern informatio ... Expand

Abstract: Organizations today leverage big data analytics combined with modern information and communication technology (ICT) to control workers in a largely automated manner. This phenomenon is commonly referred to as algorithmic control (AC). Research has shown that worker perceptions of AC applications can impact critical outcomes such as worker resistance, continuance intention, and well-being. Despite recent efforts to better understand these worker-level effects of AC, no systematic measures of workers' perceptions of AC exist to date. In this study, we develop and present a scale to empirically measure AC at the worker level. In particular, following established scale development guidelines, we conceptualize AC in terms of seven distinct forms, and derive, evaluate, and validate a final set of 33 measurement items. In doing so, our study paves the way for a much-needed, more systematic investigation of AC and its worker-level implications. Collapse

Topics: database system video conference social media

Methods: qualitative interview survey design psychometrics card sorting descriptive statistic

Does Analytics Help Resolve Equivocality in the Healthcare Context? Contrasting the Effects of Analyzability and Differentiation

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Adjerid, Idris; Angst, Corey; Devaraj, Sarv; Berente, Nicholas

Abstract: Organizations are increasingly using data analytics to help make decisions and ... Expand

Abstract: Organizations are increasingly using data analytics to help make decisions and drive positive outcomes. But organizational scholarship has warned us that the sort of information processing associated with analytic capabilities, while effective for uncertainty reduction, may be less effective in equivocal contexts. Equivocality is evident when tasks are not easily analyzable (task analyzability) or when organizational departments are highly differentiated (differentiation). We hypothesize that analytics will be less effective in driving positive outcomes when equivocality is high because of low task analyzability. However, when an organization is more differentiated, resulting in high equivocality, we anticipate that analytics will be more effective in driving positive outcomes. To test this theory, we studied how clinical healthcare analytics influenced experiential quality (akin to patient satisfaction) in over 3,000 hospitals across nine years. Our results show that analytics capabilities, on average, do improve outcomes in terms of patient experiential quality, suggesting that analytics can reduce uncertainty, but we also found evidence for the moderating role of equivocality. Specifically, as task analyzability decreases (i.e., increasing equivocality), clinical healthcare analytics becomes less effective in improving experiential quality. However, when equivocality is high because of differentiation, there is a positive relationship between clinical healthcare analytics and experiential quality but only in larger hospitals. From a managerial perspective, this study has implications for boundary conditions of data analytics in organizations. Collapse

Topics: healthcare analytics technology adoption electronic health record health information system artificial intelligence

Methods: theory development post-hoc analysis experimental group longitudinal research machine learning

Theories: organizational information processing theory

Investigating the Use of Augmented Reality and Machine Learning in Electrical Engineering Courses

2023 | International Conference on Business Informatics | Citations: 0

Authors: Al Mehdawi, Miles; Dreesbach, Tobias; Gösling, Henning; Brinkmann, Marcel

Abstract: The use of augmented reality (AR) in education and training is growing increasi ... Expand

Abstract: The use of augmented reality (AR) in education and training is growing increasingly. However, applications to integrate augmented reality learning content into training for basic electrical engineering courses are scarce. The individual learning objectives of trainees complicate the digitalization of learning content, particularly for the drawing of circuit diagrams. To increase trainees' learning outcome while simultaneously relieving instructors of classroom supervision, we designed and developed an AR-based prototype, to enhance hand-drawn circuit diagrams in vocational training. The context sensitivity is achieved by combining AR with image recognition. In an experiment with twelve participants, a positive impact of the prototype on trainees' learning outcomes was observed, in comparison to a control group that received instructions without the prototype. Collapse

Topics: augmented reality smart device artificial intelligence smart glass extended reality

Methods: design artifact experimental group qualitative interview survey parametric test

Influencing the Influencers? The Case of @CelebJets and the Role of Social Media in Empowering Citizens to Conduct Climate Justice Activism

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Berney, Manon; Ondrus, Jan; Holzer, Adrian

Abstract: Online climate change activism is growing. However, its effectiveness is not ye ... Expand

Abstract: Online climate change activism is growing. However, its effectiveness is not yet firmly established. One of the challenges is for citizens to make sense of the vast amount of data to understand the impact of specific behaviors. The aggregation of publicly available information in a comprehensible and playful manner could potentially trigger effective evidence-based activism. However, it is not yet clear if and how such an intervention works. In this paper, we make a preliminary analysis of a use case of such an approach by investigating the @CelebJets phenomena, which automatically tracks and shares the private jet flights of celebrities on Twitter. Our analysis demonstrates that this approach can elicit user engagement and provide pertinent material for the press, potentially leading to a shift in behavior, particularly among celebrities who are promoting sustainability. Collapse

Topics: Twitter application programming interface social media climate change Wikipedia

Methods: case study regression analysis method parametric test descriptive statistic field experiment

Two Sides of the Same Pillow: Unfolding the Relationship between Objective and Subjective Sleep Quality with Unsupervised Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Biedebach, Luka; Óskarsdóttir, María; Arnardottir, Erna; Islind, Anna Sigríður

Abstract: ... Expand

Abstract: Collapse

Topics: mobile system smart watch mobile application smart wearable device healthcare data

Methods: descriptive statistic cluster analysis parametric test ANOVA longitudinal research

Team Performance: Nature and Antecedents of Nonnormal Distributions

2023 | Organization Science | Citations: 0

Authors: Bradley, Kyle J.; Aguinis, Herman

Abstract: Team research typically assumes that team performance is normally distributed: t ... Expand

Abstract: Team research typically assumes that team performance is normally distributed: teams cluster around average performance, performance variability is not substantial, and few teams inhabit the upper range of the distribution. Ironically, although most team research and methodological practices rely on the normality assumption, many theories actually imply nonnormality (e.g., performance spirals, team composition, team learning, punctuated equilibrium). Accordingly, we investigated the nature and antecedents of team performance distributions by relying on 274 performance distributions including 200,825 teams (e.g., sports, politics, firefighters, information technology, customer service) and more than 500,000 workers. First, regarding their overall nature, only 11% of the distributions were normal, star teams are much more prevalent than predicted by normality, the power law with an exponential cutoff is the most dominant distribution among nonnormal distributions (i.e., 73%), and incremental differentiation (i.e., differential performance trajectories across teams) is the best explanation for the emergence of these distributions. Second, this conclusion remained unchanged after examining theory-based boundary conditions (i.e., tournament versus nontournament contexts, performance as aggregation of individual-level performance versus performance as a team-level construct, performance assessed with versus without a hard left-tail zero, and more versus less sample homogeneity). Third, we used the team learning curve literature as a conceptual framework to test hypotheses and found that authority differentiation and lower temporal stability are associated with distributions with larger performance variability (i.e., a greater proportion of star teams). We discuss implications for existing theory, future research directions, and methodological practices (e.g., need to check for nonnormality, Bayesian analysis, outlier management). Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1619. Collapse

Topics: team productivity customer service anonymity systems development virtual teamwork

Methods: meta analysis statistical hypothesis test Bayesian analysis descriptive statistic chi squared test

How synchronous participation affects the willingness to subscribe to social live streaming services: the role of co-Interactive behavior on Twitch

2023 | European Journal of Information Systems | Citations: 0

Authors: Bründl, Simon; Matt, Christian; Hess, Thomas; Engert, Simon

Abstract: Social computing has transformed the content industry. Content providers have ch ... Expand

Abstract: Social computing has transformed the content industry. Content providers have changed from providing merely content to offering social content experiences that allow users not only to passively consume content but also to actively participate. As the latest development, social live streaming services (SLSS) have emerged that enable so-called co-interactive behaviour, i.e., the live interplay between users while co-experiencing content. Yet, it remains unclear how co-interactive behaviour affects the monetisation of social live content. Based on a data set of 868,940 users from 59 gaming channels of the freemium-based SLSS Twitch, we empirically show that co-interactive behaviour has a stronger effect on users’ willingness to subscribe (WTS) than passive and active behaviour. Since we find that a user’s reciprocated interactions also directly increase other users’ WTS, our results call for dismantling the current dichotomy of user participation and social influences in IS research. We suggest a social-experiential perspective that integrates social influences as an inherent part of users’ participation experiences. This novel interactive second-person perspective contributes to research on user engagement and value capture on social content platforms, and it allows SLSS providers to obtain a more precise picture of user value to better develop or adapt their monetisation strategies. Collapse

Topics: participatory design social influence user behavior Twitch application programming interface

Methods: design methodology propensity score matching experimental group descriptive statistic logistic regression

Theories: general systems theory

Fighting for the status quo: Threat to tech self-esteem and opposition to competing smartphones

2023 | Information & Management | Citations: 0

Authors: Cano Bejar, Arturo Heyner; Ray, Soumya; Huang, Yu Hsuan

Abstract: Users of rival technology platforms, such as Android-versus-iOS, often harbor ho ... Expand

Abstract: Users of rival technology platforms, such as Android-versus-iOS, often harbor hostile opposition to each other. The dominant perspective of technology resistance, conceptualized as technology inertia, cannot explain such strong oppositional behavior. We propose a threat to tech self-esteem construct that captures distinct threat perceptions and offers a unified explanation for oppositional intentions. We conducted an empirical study of smartphone users, where we find that the threat to technology self-esteem arises from distinct antecedents and offers a singular explanation for opposition. Moreover, we show the unintended consequences of employing lockin strategies and provide advice on why and how to mitigate them. Collapse

Topics: smartphone switching cost self efficacy Android iOS

Methods: survey partial least squares path modeling structural equation modeling cross sectional research statistical hypothesis test

Theories: reactance theory

When Does the Pre-entry Experience of New Entrants Improve Their Performance? A Meta-Analytical Investigation of Critical Moderators

2023 | Organization Science | Citations: 0

Authors: Cao, Zhi; Posen, Hart E.

Abstract: Although pre-entry experience is widely regarded as a critical asset that positi ... Expand

Abstract: Although pre-entry experience is widely regarded as a critical asset that positively influences new entrant performance, empirical support is mixed. To address this inconsistency, we conduct a meta-analysis of the empirical findings in 272 papers. We draw theoretically on the organizational learning literature to argue that the pre-entry experience–new entrant performance relationship is contingent on the characteristics of pre-entry experience, the environmental context of the new entrant, and the interaction between the two. In particular, we examine the effects of two levels of pre-entry experience (firm and founder), four types of founder-level pre-entry experience (entrepreneurial, managerial, industry, and functional experience), and two types of environments (industry and institutional). The meta-analysis results show a significant and positive correlation between founder-level pre-entry experience and economic performance of 0.07. Likewise, the failure rates of spinouts and diversifying entrants are 11% lower than that of start-ups. The moderating analysis results show that the correlation of founder-level pre-entry experience and economic performance is lower in knowledge- or technology-intensive (KTI) industries and higher in low-KTI manufacturing and service industries. The correlation is also higher in institutional environments with high power distance and individualism. These findings provide compelling new evidence for the importance of pre-entry experience and advance our understanding of the boundary conditions on the pre-entry experience–new entrant performance relationship. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1589. Collapse

Topics: national culture organization learning entrepreneurship productivity research and development

Methods: descriptive statistic hazard function meta analysis literature sample chi squared test

Trust and deception with high stakes: Evidence from the friend or foe dataset

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Xunyu; Wang, Xinran; Spitzley, Lee; Nunamaker, Jay

Abstract: Many social interactions rely on the premise of mutual trust, but deception viol ... Expand

Abstract: Many social interactions rely on the premise of mutual trust, but deception violates trust and poses risk. Empirically examining trust and deception, particularly in high-stakes situations, is challenging but essential for improving the research realism and generalizability. To address this difficulty, we study trusting and deceptive behaviors in a high-stakes situation by using a novel dataset created from an American game show, Friend or Foe (FoF). In the show, a contestant's reward was determined through a trust game modified from the prisoner's dilemma. We explore how numerous human behaviors including facial expressions, gaze, head pose, body motion, language, and socio-demographic attributes, were related to a contestant's trusting or deceptive decision. Using a data-driven approach, we find that the deceivers' (contestants who chose Foe) behavior featured a neutralized face, negative facial emotions, enhanced upper body motion, and language with a lower sense of immediacy and agreeableness. The contestants who chose to trust (chose Friend) exhibited opposite behavioral patterns. Socio-demographic factors such as age, height, and facial attractiveness were also associated with a contestant's choice. Combining multimodal information, machine learning classifiers could predict the contes­ tant's choice with an accuracy about 25% greater than earlier reported human accuracy. We contribute to both trust and deception literature by examining the generalizability of trusting and deceptive behaviors to a new high-stakes scenario. We also add to the decision support literature by showing the superior predictive perfor­ mances of combining behavioral and socio-demographic features. Furthermore, we contribute to the academic community by introducing the FoF dataset. Collapse

Topics: decision support website YouTube Python decision making

Methods: meta analysis support vector machine correlation analysis descriptive statistic random forest classification