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

Process and Strategy Mining in Real-Time Strategy Games

2023 | International Conference on Business Informatics | Citations: 0

Authors: Elbert, Nico; Stein, Nikolai; Flath, Christoph M.

Abstract: In this study, we introduce a data-driven approach for process mining in real-t ... Expand

Abstract: In this study, we introduce a data-driven approach for process mining in real-time strategy (RTS) games, which serve as a proxy for corporate decision processes. By analyzing decision sequences in gaming environments, we uncover strategic behaviors impacting performance and match outcomes. Our methodology leverages game data, enriching process mining research and providing implications for strategic planning in business process management. Collapse

Topics: process mining strategic management business process management decision support decision making

Methods: cluster analysis Petri net modeling language XGBoost event log

The Role of Parents’ Mores on Their Intentions to Enroll Their Children in a School-based Telemedicine Program

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

Authors: Ghashami, Farnaz; Gantz, Christopher; Gefen, David

Abstract: Hospitals providing nurse-run school telemedicine programs can improve children ... Expand

Abstract: Hospitals providing nurse-run school telemedicine programs can improve children's access to healthcare services by delivering such services on school premises. Despite this potential, the enrollment in such programs by parents falls short of expectations because of parents' hesitation to allow their children to receive such telemedicine healthcare services. This study investigates some aspects of the role of parents' perception of contributing to a good cause, i.e., personal mores, on their intentions to enroll their children in such a telemedicine program. Surveying parents about such a new telemedicine service that is being deployed in their school district shows that parents' personal mores are associated with increased intentions over and above its expected time-flexibility advantages. Surprisingly, trust increased intentions was fully mediated by those mores. Practical and theoretical implications are discussed, especially that doing good to others may be another reason why people adopt telemedicine, suggesting another reason beyond the utilitarian, rational, and social processes that are portrayed in previous research. Collapse

Topics: behavioral intention electronic health technology adoption missing data healthcare data

Methods: survey qualitative interview SEM tool post-hoc analysis descriptive statistic

Unveiling and Unraveling Aggregation and Dispersion Fallacies in Group MCDM

2023 | Group Decision and Negotiation | Citations: 0

Authors: Mohammadi, Majid; Tamburri, Damian A.; Rezaei, Jafar

Abstract: Priorities in multi-criteria decision-making (MCDM) convey the relevance prefere ... Expand

Abstract: Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with solutions based on compositional data analysis to avoid misusing statistical operations. First, we use a compositional approach to aggregate the priorities of a group of DMs and show that the outcome of the compositional analysis is identical to the normalized geometric mean, meaning that the arithmetic mean should be avoided. Furthermore, a new aggregation method is developed, which is a robust surrogate for the geometric mean. We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions. Discussing the fallacies in computing the standard deviation, we provide a probabilistic criteria ranking by developing proper Bayesian tests, where we calculate the extent to which a criterion is more important than another. Finally, we explain the errors in computing the distance between priorities, and a clustering algorithm is specially tailored based on proper distance metrics. Collapse

Topics: decision making

Methods: descriptive statistic computational algorithm cluster analysis Wilcoxon signed-rank test nonparametric test

Theories: SERVQUAL

The Attraction Effect in Crowdfunding

2023 | Information Systems Research | Citations: 0

Authors: Weinmann, Markus; Mishra, Abhay Nath; Kaiser, Lena Franziska; vom Brocke, Jan

Abstract: At the core of reward-based crowdfunding (RBC) is the reward menu. Carefully con ... Expand

Abstract: At the core of reward-based crowdfunding (RBC) is the reward menu. Carefully constructed reward menus can impact fundraising success significantly. For example, an oft-discussed approach to influencing purchasing decisions is introducing an irrelevant option into a consumer’s choice set—a decoy option—making another, often more valuable, option more attractive. This phenomenon is also known as the attraction effect. Prior research, however, has found mixed evidence for its occurrence in applied settings because it has largely employed simplified attribute presentation—with only numeric attributes such as ratings—and hypothetical choices, which had no economic consequences. Hence, researchers questioned the practical significance of the attraction effect. In this paper, we examine whether the attraction effect will manifest in an applied setting such as the crowdfunding context, where product attributes are both numerical (e.g., price) and nonnumerical (e.g., content of a product), choices have economic consequences, and organizations offer several menu options to consumers. We draw upon the salience theory and propose four hypotheses, suggesting that introducing an irrelevant decoy option in choice sets, created digitally in reward menus, may lead backers to choose higher-priced options. We conducted seven online experiments and a field study on Kickstarter (in total, n = 3,998 participants), with increasing levels of similarity with RBC. We found that the attraction effect significantly shifted backers’ preferences from a low-priced to a high-priced reward by 18.8% to 28.2% in different settings, highlighting the substantial potential of properly designed digital reward menus to influence funders’ choices. Given that researchers have expressed skepticism about the attraction effect in applied settings, our results are particularly significant, suggesting that information systems design can influence choice behavior. This research makes targeted contributions to the literature on crowdfunding, the attraction effect, and digital design. History: Alessandro Acquisti, Senior Editor; David (Jingjun) Xu, Associate Editor. Funding: This work was supported by University of Cologne, Erasmus University, and University of Liechtenstein. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1152. Collapse

Topics: crowdsourcing crowdfunding electronic market systems design decision making

Methods: experiment field study experimental group mixed method Bayesian analysis

Theories: behavioral economics

Bayesian non-parametric method for decision support: Forecasting online product sales

2023 | Decision Support Systems | Citations: 0

Authors: Wu, Ziyue; Chen, Xi; Gao, Zhaoxing

Abstract: Forecasting online product sales is essential for retailers and e-commerce platf ... Expand

Abstract: Forecasting online product sales is essential for retailers and e-commerce platforms, but it can be challenging owing to the complex dynamics and mixed trends in sales data. Popular end-to-end approaches tend to capture spurious correlations in historical data, whereas two-stage approaches that decompose time series and make separate predictions often result in error accumulation. To address these issues, we propose PoissonGP, a novel Bayesian model that employs a non-homogeneous Poisson process with a Gaussian process prior for sales pre­ diction. PoissonGP can capture complex patterns in data with multiple trends and manage distribution shifts caused by changes in long-run sales. Additionally, it incorporates forecast uncertainty and provides interpret­ ability for building efficient and visual decision support systems. Experimental results on several synthetic and empirical datasets suggest that PoissonGP outperforms existing approaches, making it a promising tool for ecommerce platforms. Collapse

Topics: marketing management digital platform user behavior online advertising electronic commerce

Methods: longitudinal research time series analysis experiment Bayesian analysis simulation

Extracting Actionable Insights from Text Data: A Stable Topic Model Approach

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Yang, Yi; Subramanyam, Ramanath

Abstract: Topic models are becoming a frequently employed tool in the empirical methods r ... Expand

Abstract: Topic models are becoming a frequently employed tool in the empirical methods repertoire of information systems and management scholars. Given textual corpora, such as consumer reviews and online discussion forums, researchers and business practitioners often use topic modeling to either explore data in an unsupervised fashion or generate variables of interest for subsequent econometric analysis. However, one important concern stems from the fact that topic models can be notorious for their instability, i.e., the generated results could be inconsistent and irreproducible at different times, even on the same dataset. Therefore, researchers might arrive at potentially unreliable results regarding the theoretical relationships that they are testing or developing. In this paper, we attempt to highlight this problem and suggest a potential approach to addressing it. First, we empirically define and evaluate the stability problem of topic models using four textual datasets. Next, to alleviate the problem and with the goal of extracting actionable insights from textual data, we propose a new method, Stable LDA, which incorporates topical word clusters into the topic model to steer the model inference toward consistent results. We show that the proposed Stable LDA approach can significantly improve model stability while maintaining or even improving the topic model quality. Further, employing two case studies related to an online knowledge community and online consumer reviews, we demonstrate that the variables generated from Stable LDA can lead to more consistent estimations in econometric analyses. We believe that our work can further enhance management scholars' collective toolkit to analyze ever-growing textual data. Collapse

Topics: online review user-generated content open source usability social media

Methods: topic model experiment word embedding Gibbs sampling computational algorithm

Blazing the Trail: Considering Browsing Path Dependence in Online Service Response Strategy

2023 | Information Systems Frontiers | Citations: 0

Authors: Zuo, Meihua; Angelopoulos, Spyros; Liang, Zhouyang; Ou, Carol X. J.

Abstract: Competition on e-commerce platforms is becoming increasingly fierce, due to the ... Expand

Abstract: Competition on e-commerce platforms is becoming increasingly fierce, due to the ease of online searching for comparing products and services. We examine how the sequential browsing behavior of consumers can enable targeted marketing strategies on e-commerce platforms, by using clickstream data from one of the largest e-commerce platforms in Asia. We deploy duration analysis to i) explore how path dependence can better explain consumers’ sequential browsing behavior in different product categories, and ii) characterize the sequential browsing behavior of heterogeneous consumer groups. The findings of our work showcase i) the high accuracy of using sequential browsing path dependence to explain consumer behavior, ii) the patterns of their behavioral intentions and iii) the spell of the behavior of heterogeneous consumer groups. Our findings provide nuanced implications for strategically managing branding, marketing, and customer relations on e-commerce platforms. We discuss the implications of our findings for both research and practice, and we delineate an agenda for future research on the topic. Collapse

Topics: electronic commerce website logistics management marketing management electronic market

Methods: survival analysis hazard function robustness check archival research computational algorithm

Building GIS Platforms for Spatial Business: A Focus on the Science of Maximizing Location Intelligence Benefits Through Risk/Cost Management

2022 | HICSS | Citations: 0

Authors: Franklin, Christopher; Sreedharan, Jayashree

Abstract: A scientific decision model is simulated for rigorously aggregating weighted ris ... Expand

Abstract: A scientific decision model is simulated for rigorously aggregating weighted risks and costs for lower-order risk factors established by Tomlinson for planning GIS (Geographic Information System) implementations. The basic assumption of the paper is that generally, management practitioners do not estimate risk with the required quantitative rigor during the planning phases. Examples include: (a) lack of Johari window risk mapping and assessment (b) subjective versus Delphi method judgmental probabilities (c) violation of Lusser’s law (d) failure to utilize Galton’s estimation (e) incorrect weighting (if at all) (f) doubtful risk aggregation techniques for summarized likelihoods and (g) ad hoc decision making without Bayesian logic. For organizations competing in a global economy, strong decision models lead to management decision-making options in the creation of value from location awareness and proximity intelligence capabilities. Results are simulated and discussed with reference to future potential research. Collapse

Topics: decision making digital dashboard anonymity

Methods: delphi study Bayesian analysis survey

Predicting the Outcome of a Debt Collection Process Using Bayesian Networks

2022 | HICSS | Citations: 0

Authors: Köhler, Benedikt; Fromm, Hansjörg

Abstract: Many companies rely on professional debtcollection agencies to handle their outs ... Expand

Abstract: Many companies rely on professional debtcollection agencies to handle their outstanding debts. These agencies conduct a debt collection process consisting of successive, escalating actions with the aim of getting a debtor to settle an overdue claim. The sequence of actions is administered by agents who often have to make decisions on a case-by-case basis. This requires understanding of complex data and making decisions under uncertainty. This decision-making process has hardly been investigated so far. We are proposing Bayesian networks as the analytical basis for a decision support system. Bayesian networks are strong in dealing with uncertainties. They can be used for both predicting the success of a case and making recommendations on actions. The evaluation shows that Bayesian networks have a very good predictive performance which gets even better as the process evolves. With this instrument, the agents can make better-informed decisions in the debt collection process. Collapse

Topics: decision support system telephone analytical information system

Methods: Bayesian network computational algorithm machine learning sensitivity analysis naïve bayes