P2P lending and Natural Disasters: Is Altruistic Behavior conditional?

2023 | International Conference on Information Systems | Citations: 0

Authors: Agrawal, Lavlin; Sharman, Raj; Tiu, Christian; Joenväärä, Juha

Abstract: In the aftermath of natural disasters, borrowers seeking credit from traditiona ... Expand

Abstract: In the aftermath of natural disasters, borrowers seeking credit from traditional sources, such as banks, may encounter higher interest rates than in pre-disaster periods. We examine the lending behavior of peer-topeer (P2P) platforms in the wake of disasters. Since P2P platforms involve individual investors in making lending decisions, they accommodate both profit motives and empathetic responses. Empirical evidence from psychology suggests that empathy can lead to prosocial behavior. Consistent with this assertion, we find that loans affected by natural disasters have lower interest rates than comparable loans not issued in the wake of disasters. Our study finds that lenders are involved in a prosocial behavior and are ready to give away some part of their interest income to help the communities in need. Collapse

Topics: crowdsourcing natural hazard peer-to-peer lending peer-to-peer model LendingClub

Methods: instrumental variables estimation experimental group sensitivity analysis ordinary least square robustness check

When Reflection Hurts: The Effect of Cognitive Processing Types on Organizational Adaptation to Discontinuous Change

2023 | Organization Science | Citations: 1

Authors: Alves, Marlon Fernandes Rodrigues; Vastola, Vincenzo; Vasconcelos Ribeiro Galina, Simone; Zollo, Maurizio

Abstract: Technological breakthroughs, institutional disruptions, and natural disasters of ... Expand

Abstract: Technological breakthroughs, institutional disruptions, and natural disasters often alter the course of organizations and entire industries. Such discontinuous changes threaten organizations’ survival by affecting the value of the knowledge accumulated in routines and capabilities. Although it is widely acknowledged that managerial cognition is a critical antecedent of organizational responses to discontinuous change, the role of type 1 (intuitive) and type 2 (reflective) processing in the adaptation of shared patterns of behavior, that is, routines, remains understudied. Drawing on dual-process theory, we propose that particular features of type 1 processing render this approach superior to type 2 processing, especially in highly ambiguous environments in which information is limited and difficult to verify. We tested our hypotheses in a longitudinal experiment linking individual-level factors with organizational-level practices of routine adaptation. Experienced managers, paired in 80 groups, developed routines in a first round of a simulation game; in a second round, we then introduced a discontinuous change making previous routines obsolete in order to observe how they adapted. The data show that priming type 1 processing facilitates organizational adaptation more than type 2 processing by providing faster, more routinized, efficiently coordinated, and optimal responses. In addition, type 1 appears to be more functional in highly ambiguous environments, whereas type 1 and type 2 processes yield similar levels of performance under low levels of ambiguity. Overall, our study advances the understanding of the nondeliberative dimension of organizational adaptation to discontinuous change.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001).Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1517. Collapse

Topics: decision making strategic management innovation management problem solving task productivity

Methods: experiment longitudinal research parametric test ANOVA statistical hypothesis test

Theories: dual-system theory dynamic capabilities theory

Human versus automated agents: how user preferences affect future mobility systems

2023 | European Conference On Information Systems | Citations: 0

Authors: Ahadi, Ramin; Taudien, Anna; Ketter, Wolfgang

Abstract: Along with rapid advancements in digital, and physical technologies, shared aut ... Expand

Abstract: Along with rapid advancements in digital, and physical technologies, shared autonomous electric vehicles are forecasted to gradually complement and replace traditional human-based mobility systems. Information systems play a key role in such a deep socio-technical system to pave the path toward a more sustainable future. This study investigates a hybrid ride-hailing platform of automated and human-driven vehicles. Our focus lies on the demand side where we evaluate the influence of user behaviors on economic and environmental system performance. For this, we employ a data-driven agent-based simulation modeling heterogeneous vehicle and user agents calibrated by rental data of a leading vehicle-sharing company. Our findings declare that diverse customer responses to the introduction of shared autonomous electric vehicles yield significantly differing fleet performance and ecological costs. We also observe that the status quo customer communication design of ride-hailing platforms needs adjustments to maximize the potentials of future hybrid shared mobility systems. Collapse

Topics: ride sharing platform user behavior service quality decision making artificial intelligence

Methods: simulation agent based simulation experiment literature study sensitivity analysis

Theories: technology acceptance model general systems theory

Strategic Investments for Platform Launch and Ecosystem Growth: A Dynamic Analysis

2023 | Journal of Management Information Systems | Citations: 0

Authors: Anderson Jr., Edward G.; Parker, Geoffrey G.; Tan, Burcu

Abstract: Multi-sided platforms must make decisions on both pricing and engineering invest ... Expand

Abstract: Multi-sided platforms must make decisions on both pricing and engineering investment and must continually adjust them as the platform scales over its lifecycle. Engineering investments can be allocated to features that improve a platform's standalone value, social features to take advantage of same-side network effects, or integration tools and boundary resources to facilitate third-party content creation. Guidance in the academic or practitioner literature is not granular. Moreover, relevant normative economic models that consider externalities are rarely dynamic. Hence, there is a gap in knowledge about how to best balance tradeoffs between different strategic decisions throughout the entire platform lifecycle. To begin to address this gap, we explore normative strategies for coordinating pricing and engineering investment decisions on a continuous basis under different ecosystem conditions. We build a simulation model informed by economics and marketing theory and perform extensive sensitivity analyses on key parameters in different ecosystem scenarios over a multi-period lifecycle. We find that pricing and investment strategies must continuously change to perform optimally. In particular, strategies that are most effective at launch often differ from those that are most effective during scaling as well as those most effective at maturity. We also find that the optimal strategy depends strongly on the monetization model and market aversion to price changes. Lastly, we specifically examine four different industry segments: mobile platforms, social media, the sharing economy, and business-to-business. The results provide evidence that the trajectory of platform pricing and investment strategies should greatly differ depending on industrial context. Collapse

Topics: price management digital platform social media decision making business-to-business

Methods: simulation sensitivity analysis system dynamics optimization model conjoint analysis

Theories: control theory marketing theory

Employing Machine Learning to Advance Agent-based Modeling in Information Systems Research

2023 | International Conference on Information Systems | Citations: 0

Authors: Bashardoust, Amirsiavosh; Safaei, Negin; Haki, Kazem; Shrestha, Yash Raj

Abstract: ... Expand

Abstract: Collapse

Topics: sensemaking Python organizational context innovation management business-to-business

Methods: sensitivity analysis theory development simulation robustness check artificial neural network

Theories: sociotechnical systems theory

Influencer is the New Recommender: insights for Theorising Social Recommender Systems

2023 | Information Systems Frontiers | Citations: 0

Authors: Bawack, Ransome Epie; Bonhoure, Emilie

Abstract: Designing theory-driven social recommender systems (SRSs) has been a significant ... Expand

Abstract: Designing theory-driven social recommender systems (SRSs) has been a significant research challenge for over a decade. This study aims to identify behavioural factors that could improve the persuasiveness and quality of recommendations made by SRSs. Given both research streams’ striking similarity, it uses the recent yet rich research on social media influencers (SMI) to inform SRS research. Drawing on 72 publications, we classified 52 independent variables into 12 categories regrouped into three broad categories that characterise the relationships between the consumer and the (i) recommender system, (ii) product or brand, and (iii) and advert. The metanalysis results determined the relative importance of each category in predicting purchase intentions, placing recommender credibility and attitude towards the recommended product or brand at the top of the charts. Our findings are expected to facilitate more refined theory-building efforts and theory-driven designs in SRS research and practice. Collapse

Topics: advertising management database system Twitter TikTok IS discipline

Methods: literature study theory development computational algorithm descriptive statistic sensitivity analysis

Theories: social learning theory behavioral theory theory of planned behavior

COVIDAL: A Machine Learning Classifier for Digital COVID-19 Diagnosis in German Hospitals

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Bartenschlager, Christina C.; Ebel, Stefanie S.; Kling, Sebastian; Vehreschild, Janne; Zabel, Lutz T.; Spinner, Christoph D.; Schuler, Andreas; Heller, Axel R.; Borgmann, Stefan; Hoffmann, Reinhard; Rieg, Siegbert; Messmann, Helmut; Hower, Martin; Brunner, Jens O.; Hanses, Frank; Römmele, Christoph

Abstract: For the fight against the COVID-19 pandemic, it is particularly important to map ... Expand

Abstract: For the fight against the COVID-19 pandemic, it is particularly important to map the course of infection, in terms of patients who have currently tested SARS-CoV-2 positive, as accurately as possible. In hospitals, this is even more important because resources have become scarce. Although polymerase chain reaction (PCR) and point of care (POC) antigen testing capacities have been massively expanded, they are often very time-consuming and cost-intensive and, in some cases, lack appropriate performance. To meet these challenges, we propose the COVIDAL classifier for AI-based diagnosis of symptomatic COVID-19 subjects in hospitals based on laboratory parameters. We evaluate the algorithm's performance by unique multicenter data with approximately 4,000 patients and an extraordinary high ratio of SARS-CoV-2-positive patients. We analyze the influence of data preparation, flexibility in optimization targets, as well as the selection of the test set on the COVIDAL outcome. The algorithm is compared with standard AI, PCR, POC antigen testing and manual classifications of seven physicians by a decision theoretic scoring model including performance metrics, turnaround times and cost. Thereby, we define health care settings in which a certain classifier for COVID-19 diagnosis is to be applied. We find sensitivities, specificities, and accuracies of the COVIDAL algorithm of up to 90 percent. Our scoring model suggests using PCR testing for a focus on performance metrics. For turnaround times, POC antigen testing should be used. If balancing performance, turnaround times, and cost is of interest, as, for example, in the emergency department, COVIDAL is superior based on the scoring model. Collapse

Topics: artificial intelligence mobile application pandemic logistics management data integration

Methods: computational algorithm machine learning normalization data transformation support vector machine artificial neural network

Navigating the Paradox of IT Novelty and Strategic Conformity: The Moderating Role of Industry Dynamism

2023 | International Conference on Information Systems | Citations: 0

Authors: Chaturvedi, Devina; Kathuria, Abhishek; Andrade, Mariana; Saldanha, Terence

Abstract: Although achieving strategic distinctiveness is critical for firms in dynamic i ... Expand

Abstract: Although achieving strategic distinctiveness is critical for firms in dynamic industries, where the competitive landscape and market conditions frequently change, there is a paucity of research on the impact of information technology (IT) on a firm's strategic conformity (or distinctiveness). Motivated by the importance of strategic conformity and emergence of novel technologies, we draw on institutional theory and hypothesize that IT Novelty positively impacts strategic conformity. Second, drawing on resource-based view, we hypothesize that market dynamism and competitive dynamism negatively moderate the effect of IT Novelty on strategic conformity. Third, we hypothesize that the moderating impact is stronger for market dynamism than competitive dynamism. We find support for our hypotheses using a fifteen-year dataset from over 300 U.S. manufacturing firms. Our study contributes to theory and managerial practice by combining institutional and resource-based view perspectives to examine how IT Novelty shapes a firm's strategic conformity, particularly in dynamic industry conditions. Collapse

Topics: database system decision making artificial intelligence internet of things mobile system

Methods: longitudinal research autocorrelation analysis hierarchical linear modeling robustness check regression analysis method

Theories: resource based view of the firm institutional theory

A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction

2023 | Information Systems Research | Citations: 0

Authors: Chen, Gang; Xiao, Shuaiyong; Zhang, Chenghong; Zhao, Huimin

Abstract: As artificial intelligence and digitalization technologies are flourishing real- ... Expand

Abstract: As artificial intelligence and digitalization technologies are flourishing real-time, online interaction–based commercial modes, exploiting customers’ purchase intention implied in online interaction processes may foster huge business opportunities. In this study, we target the task of voice chat–based customer response prediction in an emerging online interaction–based commercial mode, the invite-online-and-experience-in-store mode. Prior research shows that satisfaction, which can be revealed by the discrepancy between prior expectation and actual experience, is a key factor to disentangle customers’ purchase intention, whereas black-box deep learning methods empirically promise us advantageous capabilities in dealing with complex voice data, for example, text and audio information incorporated in voice chat. To this end, we propose a theory-driven deep learning method that enables us to (1) learn customers’ personalized product preferences and dynamic satisfaction in the absence of their profile information, (2) model customers’ actual experiences based on multiview voice chat information in an interlaced way, and (3) enhance the customer response prediction performance of a black-box deep learning model with theory-driven dynamic satisfaction. Empirical evaluation results demonstrate the advantageous prediction performance of our proposed method over state-of-the-art deep learning alternatives. Investigation of cumulative satisfaction reveals the collaborative predictive roles of theory-driven dynamic satisfaction and deep representation features for customer response prediction. Explanatory analysis further renders insights into customers’ personalized preferences and dynamic satisfaction for key product attributes. History: Yong Tan, Senior Editor; Jingjing Zhang, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971067 and 72271059] and the China Postdoctoral Science Foundation [Grant 2022M722394]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1196. Collapse

Topics: customer satisfaction willingness to pay decision making marketing information system customer service

Methods: deep learning experiment machine learning BERT theory development

Appreciating the Performance of Neuroscience Mining in NeuroIS Research: A Case Study on Consumer's Product Perceptions in the Two UI Modes-Dark UI vs. Light UI

2023 | HICSS | Citations: 0

Authors: Costello, F.; Kim, M.; Lee, K.

Abstract: The goal of the current study was to explore the potential of neuroscience mini ... Expand

Abstract: The goal of the current study was to explore the potential of neuroscience mining (NSM) for comprehending NeuroIS paradigms. NSM is an interdisciplinary field that combines neuroscience and business mining, which is the application of big data analytics, computational social science, and other areas to business problems. NSM makes it possible to apply predictive models to NeuroIS datasets, such as machine learning and deep learning, to find intricate patterns hidden by conventional regression-based analysis. First, we predicted 28 individual EEG power spectra separated brainwave data using a Random Forest (RF) model. Next, we used NSM to precisely predict how consumers would perceive a product online, depending on whether a light or dark user interface (UI) mode was used. The model was then used to extract more precise results that could not be obtained using more conventional linear-based analytical models using sensitivity analysis. The benefits of using NSM in NeuroIS research are as follows: (1) it can relieve the burden of the threehorned dilemma described by Runkel and McGrath; (2) it can enable more temporal data to be directly analyzed on the target variables; and (3) sensitivity analysis can be performed on a condition/individual basis, strengthening the rigor of findings by reducing sample bias that can be lost in grand averaging of data when analyzed with methods like GLM. Collapse

Topics: user interface big data usability tablet artificial intelligence

Methods: electroencephalography NeuroIS computational algorithm sensitivity analysis machine learning