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

ICT-Based Country-Level Determinants of Social Media Diffusion

2023 | Information Systems Frontiers | Citations: 0

Authors: Arayankalam, Jithesh; Krishnan, Satish

Abstract: There is a broad consensus that social media platforms have become critical tool ... Expand

Abstract: There is a broad consensus that social media platforms have become critical tools for policymakers in achieving various policy objectives in a country. Therefore, an in-depth understanding of the ICT-based country-level determinants of social media diffusion has become necessary. To this end, we propose a government-people-technology (GTP) framework for categorizing the ICT-based country-level determinants of social media diffusion into three dimensions. Using this framework and utilizing archival data from 107 countries, we examine the roles of ICT in the government's vision, ICT law maturity, Internet bandwidth, ICT cost, ICT skill, and capacity to own ICT in influencing social media diffusion. A quantile panel data regression results reveal that ICT in the government's vision, ICT law maturity, Internet bandwidth, and capacity to own ICT were positively associated with social media diffusion. In contrast, ICT cost was negatively associated with it. Thus, by offering a new framework, we contribute to the technology diffusion literature by emphasizing the role of government, people, and supportive technologies in driving social media diffusion at the country level. Collapse

Topics: social media IT investment internet technology IT skill technology diffusion

Methods: longitudinal research regression analysis method post-hoc analysis cross sectional research archival research

Theories: theory of everything

Counterfactual Explanations for Incorrect Predictions Made by AI Models

2023 | International Conference on Information Systems | Citations: 0

Authors: Asrzad, Amir; Li, Xiaobai

Abstract: Advanced AI models are powerful in making accurate predictions for complex prob ... Expand

Abstract: Advanced AI models are powerful in making accurate predictions for complex problems. However, these models often operate as black boxes. This lack of interpretability poses significant challenges, especially in high-stakes applications such as finance, healthcare, and criminal justice. Explainable AI seeks to address the challenges by developing methods that can provide meaningful explanations for humans to understand. When black box models are used for prediction, they inevitably produce errors. It is important to appropriately explain incorrect predictions. This problem, however, has not been addressed in the literature. In this study, we propose a novel method to provide explanations for misclassified cases made by black box models. The proposed method takes a counterfactual explanation approach. It builds a decision tree to find the best counterfactual examples for explanations. Incorrect predictions are rectified using a trust score measure. We validate the proposed method in an evaluation study using real-world data. Collapse

Topics: artificial intelligence explainable artificial intelligence local interpretable model agnostic explanation decision making enterprise information system

Methods: machine learning decision tree classification literature study random forest classification experiment

Decision support tool to define the optimal pool testing strategy for SARS-CoV-2

2023 | Decision Support Systems | Citations: 0

Authors: Barracosa, Bruno; Felício, João; Carvalho, Ana; Moreira, Leonilde M.; Mendes, Filipa; Verde, Sandra Cabo; Pinto-Varela, Tânia

Abstract: This work proposes a holistic decision support tool to be used by diverse stakeh ... Expand

Abstract: This work proposes a holistic decision support tool to be used by diverse stakeholders and decision-makers, integrating experimental data. Considering the characteristics of each region, the tool aids in defining the most suitable testing strategies per country or region: number of tests, time, cost, type of pool strategy based on prevalence rate interval, and the use of centralization vs. decentralization of testing strategies. The tool con­ tributes to the field of decision support systems theoretically by introducing a novel model for testing decisions in extreme situations within a specific region, and practically, by demonstrating how laboratory data can be applied to real-life decision-making in that same region. This tool has been validated with two real case studies (Portugal and France) and has applied, as input data, real experimental results. The results suggested tree- and matrixbased strategies for low and high prevalence rates. A decentralized testing strategy improves the financial and time gains introduced by pool testing; however, centralizing the decisions shows a more straightforward implementation and resource management of the pool testing system with a low-performance loss. Therefore, there might be a solution to consider overcoming operational restrictions. Collapse

Topics: decision support system pandemic

Methods: case study optimization model experiment simulation machine learning

Smart Mobility Meets Industry: Enhancing Energy Flexibility Potentials by Combining Industrial Production & Electric Vehicle Charging

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Bayer, David; Bollenbach, Jessica; Lersch, Jonathan; Rusche, Simon; Weibelzahl, Martin

Abstract: The increasing share of renewable energy sources poses the challenge of volatil ... Expand

Abstract: The increasing share of renewable energy sources poses the challenge of volatile energy generation, requiring demand side management (DSM) to manage such volatility. In view of the high industrial electricity demand and the increasing charging demand of electric vehicles (EVs), both industry and mobility represent relevant areas of DSM. However, the combination of EV charging and energy-intensive industrial processes still contains untapped synergy potentials. Thus, we present a mixed-integer linear program and quantify the economic and ecologic potential of combining energy flexibilities of industry and electric mobility within a case study. For our evaluation, we compare the results of our model to a benchmark case that separately manages industry and EV flexibilities. Our findings suggest that implementing our approach would yield both economic and ecological benefits, resulting in a reduction in anticipated costs and emissions, as well as a decrease in associated uncertainty. KeywordsEnergy-flexible Factory, Vehicle-to-Factory, Electric Vehicle Charging, Mixed-Integer Linear Programming Additionally, the increasing use of electric vehicles (EVs) raises concerns that the existing distribution networks may become strained beyond their capacity (Godina et al., 2016). These concerns are due to Smart Mobility Meets Industry Collapse

Topics: decision making manufacturing information system

Methods: simulation case study optimization model integer linear programming dynamic programming

Fast Generation of Heterogeneous Mental Models from Longitudinal Data by Combining Genetic Algorithms and Fuzzy Cognitive Maps

2023 | HICSS | Citations: 0

Authors: Bernard, D.; Cussat-Blanc, S.; Giabbanelli, P.

Abstract: Models that capture the heterogeneous perspectives of individuals are essential ... Expand

Abstract: Models that capture the heterogeneous perspectives of individuals are essential to test tailored interventions, such as behavior change interventions. Although Fuzzy Cognitive Maps (FCMs) have a rich history in depicting systems, they were either developed at an individual level through facilitated sessions, or created for an entire population through machine learning. The need to automatically create individual FCMs from data has started to be addressed, but the proposed solution was computationally prohibitive and thus could not be deployed over a large population. In this work, we use a state-of-the-art evolutionary algorithm (CMA-ES) to create individual FCMs by leveraging the growing availability of longitudinal data. We demonstrate on a real-world case study that our solution is both accurate and fast to compute. Our experiments on synthetic data also show that our approach can scale to a large number of measurements, but it cannot currently be applied to highly noisy datasets. Collapse

Topics: Python

Methods: computational algorithm case study simulation longitudinal research descriptive statistic

Pricing in Nonconvex Markets: How to Price Electricity in the Presence of Demand Response

2023 | Information Systems Research | Citations: 0

Authors: Bichler, Martin; Knörr, Johannes; Maldonado, Felipe

Abstract: A Walrasian competitive equilibrium defines a set of linear and anonymous prices ... Expand

Abstract: A Walrasian competitive equilibrium defines a set of linear and anonymous prices where no coalition of market participants wants to deviate. Walrasian prices do not exist in nonconvex markets in general, with electricity markets as an important real-world example. However, the availability of linear and anonymous prices is important for derivatives markets and as a signal for scarcity. Prior literature on electricity markets assumed price-inelastic demand and introduced numerous heuristics to compute linear and anonymous prices on electricity markets. At these prices, market participants often make a loss. As a result, market operators provide out-of-market side-payments (so-called make-whole payments) to cover these losses. Make-whole payments dilute public price signals and are a significant concern in electricity markets. Moreover, demand-side flexibility becomes increasingly important with growing levels of renewable energy sources. Demand response implies that different flexibility options come at different prices, and the proportion of price-sensitive demand that actively bids on power exchanges will further increase. We show that with price-inelastic demand there are simple pricing schemes that are individually rational (participants do not make a loss), clear the market, support an efficient solution, and do not require make-whole payments. With the advent of demand-side bids, budget balanced prices (no subsidies are necessary) cannot exist anymore, and we propose a pricing rule that minimizes make-whole payments. We describe design desiderata that different pricing schemes satisfy and report results of experiments that evaluate the level of subsidies required for linear and anonymous prices on electricity spot markets with price-sensitive demand.History: Xiaoquan (Michael) Zhang, Senior Editor; Pallab Sanyal, Associate Editor.Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Grant BI 1057/1-9].Supplemental Material: The online supplement is available at https://doi.org/10.1287/isre.2022.1139. Collapse

Topics: price management anonymity accounting

Methods: experiment optimization model linear optimization descriptive statistic case study

Selecting the Optimal Number of Crowd Workers for Forecasting Tasks

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Carvalho, Arthur; Karimi, Majid

Abstract: Crowdsourcing platforms enable requesters to elicit information from thousands ... Expand

Abstract: Crowdsourcing platforms enable requesters to elicit information from thousands of workers worldwide. A question that arises when outsourcing a task to a crowd is how many crowd workers a requester should hire. Focusing on forecasting tasks, we provide a methodological way of answering that question by formally describing how the number of crowd workers relates to the expected forecast error. Specifically, we discuss how an error curve associates a number of hired crowd workers with a fixed and a variable error. We then suggest an optimality concept that enables requesters to select an optimal number of crowd workers based on the tolerable variable error. In an illustrative study on Amazon Mechanical Turk, we demonstrate how highly prescriptive our approach is. In particular, we show how requesters can perform ex-ante and ex-post analyses by determining the optimal number of workers before running a task and by determining the fixed error after running the task and collecting forecasts. Collapse

Topics: crowdsourcing decision support system

Methods: computational algorithm post analysis optimization model experiment mathematical model

Data-Induced Rationality and Unitary Spaces in Interfirm Collaboration

2023 | Organization Science | Citations: 0

Authors: Cepa, Katharina; Schildt, Henri

Abstract: The real-time data transfer between collaborating companies allows them to repre ... Expand

Abstract: The real-time data transfer between collaborating companies allows them to represent and control activities across firm boundaries, providing new ways to organize collaborative efforts. We conducted an inductive multiple case study of five long-term relationships to examine the effects of data-intensive technologies on the organization and management of collaborative relationships in industrial companies. Our analysis shows how the delegation of digital activities into specialized digital units fostered data-driven mindsets and data-driven interactions that jointly formed a holistic data-induced rationality for managing the relationship. Together, the compartmentalization of digital collaboration and the data-induced rationalities turned these units into “unitary spaces,” organizational enclosures where structural tensions and competing demands were temporarily suspended to foster single-minded pursuit of collaborative short-term benefits for the partner company. Funding: The authors are grateful for the financial support from the Academy of Finland [Grant 274536]. Collapse

Topics: digital service interorganizational relationship big data problem solving analytical information system

Methods: qualitative interview computational algorithm theory development multiple case study qualitative coding

Theories: theory of affordance sociotechnical systems theory

Competing on price and guarantee compensation: Heeding cloud consumer's quality perception

2023 | Information & Management | Citations: 0

Authors: Chen, Fuzan; Lu, Aijun; Wu, Harris; Li, Minqiang; Feng, Haiyang

Abstract: As frequent service failures raise user concerns, guarantee compensation has bec ... Expand

Abstract: As frequent service failures raise user concerns, guarantee compensation has become a competitive instrument for cloud service providers (CSPs) in addition to price. This study proposes a game-theoretical model where two CSPs compete on both price and compensation. We consider two roles of compensation: (1) remedying user losses and (2) helping users to form quality perception. The results indicate that the higher-quality CSP should always guarantee higher compensation but may not always charge a higher price. We further investigate the signaling policies and find that the lower-quality CSP is more likely to signal a higher-than-actual quality. Collapse

Topics: IS economics consumer surplus service quality cloud computing privacy

Methods: game theoretical model optimization model cross sectional research theory development experiment

Theories: game theory