How to react to a shock? Effects of Airbnb hosts’ choices and market segmentation at the time of Covid-19

2023 | Information & Management | Citations: 0

Authors: Buzzacchi, Luigi; Milone, Francesco Luigi; Paolucci, Emilio; Raguseo, Elisabetta

Abstract: We investigate the way service providers who operate on an online peer-to-peer ( ... Expand

Abstract: We investigate the way service providers who operate on an online peer-to-peer (P2P) platform readapted their marketing choices to face the Covid-19 pandemic. Through an empirical investigation on a large dataset of Airbnb properties in Rome, observed from January 2018 to December 2020, we provide a threefold contribution by investigating how Airbnb hosts reacted to the Covid-19 pandemic shock, in terms of marketing choices, such as price adjustments and flexible cancellation policies; the direct effects of these choices on their economic returns; and how service providers on Airbnb reacted to address the new needs of their customers during the Covid-19 pandemic. The findings provide useful insights for researchers and practitioners and show that the adoption of combined marketing choices led to more than proportional effects on performances as it allowed Airbnb hosts to exploit profitable market segmentation mechanisms. Collapse

Topics: pandemic Airbnb marketing management price management peer-to-peer model

Methods: econometric modeling descriptive statistic instrumental variables estimation robustness check longitudinal research

Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Yang, Yi; Qin, Yu; Fan, Yangyang; Zhang, Zhongju

Abstract: Unstructured multimedia data (text and audio) provides unprecedented opportunit ... Expand

Abstract: Unstructured multimedia data (text and audio) provides unprecedented opportunities to derive actionable decision-making in the financial industry, in areas such as portfolio and risk management. However, due to formidable methodological challenges, the promise of business value from unstructured multimedia data has not materialized. In this study, we use a design science approach to develop DeepVoice, a novel nonverbal predictive analysis system for financial risk prediction, in the setting of quarterly earnings conference calls. DeepVoice forecasts financial risk by leveraging not only what managers say (verbal linguistic cues) but also how managers say it (vocal cues) during the earnings conference calls. The design of DeepVoice addresses several challenges associated with the analysis of nonverbal communication. We also propose a two-stage deep learning model to effectively integrate managers' sequential vocal and verbal cues. Using a unique dataset of 6,047 earnings call samples (audio recordings and textual transcripts) of S&P 500 firms across four years, we show that DeepVoice yields remarkably lower risk forecast errors than that achieved by previous efforts. The improvement can also translate into nontrivial economic gains in options trading. The theoretical and practical implications of analyzing vocal cues are discussed.Challenge 1: Time variation. Managers' vocal features can vary substantially during conference calls. For example, during the presentation section, when the manager is reading a forward-looking statement, the voice pitch may be quite stable. However, individuals' vocal cues may change as a result of being nervous, uncertain, or excited (Scherer et al., 1991). As such, in the question-answer section, when the manager is being scrutinized by analysts, the voice pitch may change. Mayew et al. (2020) found that the voice pitch of managers during dialogues with analysts is lower by about 4 Hz, on average, compared to their voice pitch during presentation sessions. Therefore, the time-varying patterns of managers' vocal cues may contain useful information. However, prior studies have ignored the time-varying pattern of managers' vocal cues in earnings conference calls (Mayew et al., 2020;, and it is unclear how a predictive model could effectively summarize time-varying vocal sequences.Challenge 2: Vocal-verbal integration. According to nonverbal communication theory (Mehrabian, 1972), vocal cues play a key role in nonverbal communication, as they can either affirm or discredit a message. For example, if a manager claims the company has strong future growth but uses a pessimistic tone of voice, market participants listening to the conference call may spot the irregularity and interpret the verbal message differently. This implies that to accurately gauge the information contained in managers' communication, one should integrate verbal language with the accompanying vocal cues. However, existing computational models (Poria et al., 2017) are limited in terms of integrating these two different communication channels (i.e., vocal and verbal).Challenge 3: Vocal complexity. Identifying the specific acoustic constructs from voice is a complex process, and the literature is still evolving without any consensus on an appropriate model for measuring affective states, emotions, and trust (Mayew & Venkatachalam, 2013). There is also little agreement about how a speaker's vocal cues should be combined to identify emotional states (Schuller, 2010)-e.g., which vocal cues to investigate and measure and how much weight to place on each vocal cue. As a result, attempts to develop constructs from vocal cues may suffer from high measurement errors. Collapse

Topics: database system enterprise information system website accounting social media

Methods: deep learning experiment meta design machine learning design artifact

Theories: transaction cost economics communication theory

Combining analytics and simulation methods to assess the impact of shared, autonomous electric vehicles on sustainable urban mobility

2022 | Information & Management | Citations: 5

Authors: Dlugosch, Oliver; Brandt, Tobias; Neumann, Dirk

Abstract: Urban mobility is currently undergoing three fundamental transformations with th ... Expand

Abstract: Urban mobility is currently undergoing three fundamental transformations with the sharing economy, electri­ fication, and autonomous vehicles changing how people and goods move across cities. In this paper, we demonstrate the valuable contribution of decision support systems that combine data-driven analytics and simulation techniques in understanding complex systems such as urban transportation. Using the city of Berlin as a case study, we show that shared, autonomous electric vehicles can substantially reduce resource investments while keeping service levels stable. Our findings inform stakeholders on the trade-off between economic and sustainability-related considerations when fostering the transition to sustainable urban mobility. Collapse

Topics: autonomous driving system ride sharing platform decision support system decision making sharing platform

Methods: simulation sensitivity analysis computational algorithm agent based simulation longitudinal research

The Sequence Effect in Panel Decisions: Evidence from the Evaluation of Research and Development Projects

2021 | Organization Science | Citations: 0

Authors: Criscuolo, Paola; Dahlander, Linus; Grohsjean, Thorsten; Salter, Ammon

Abstract: We examine how groups fall prey to the sequence effect when they make choices ba ... Expand

Abstract: We examine how groups fall prey to the sequence effect when they make choices based on informed assessments of complex situations, for example, when evaluating research and development (R&D) projects. The core argument is that the temporal sequence of selection matters because projects that appear in a sequence following a funded project are themselves less likely to receive funding. Building on the idea that selecting R&D projects is a demanding process that drains participants’ mental and emotional resources, we further theorize the moderating effect of the influence of the timing of the panel meeting on the sequence effect. We test these conjectures using a randomization in sequence order from several rounds of R&D project selection at a leading professional service firm. We find robust support for the existence of a sequence effect in R&D as well as for the moderating effect. We further explore different explanations for the sequence effect and how it passes from the individual to the panel. These findings have broader implications for the literature on innovation and search in general and on group decision making for R&D, specifically, as they suggest that a previously overlooked dimension affects selection outcomes. Collapse

Topics: research and development decision making innovation management anonymity decision support system

Methods: autocorrelation analysis longitudinal research simulation case study qualitative interview

Theories: behavioral theory of the firm

Presenting Cloud Business Performance for Manufacturing Organizations

2020 | Information Systems Frontiers | Citations: 2

Authors: Chang, Victor

Abstract: This paper presents a new concept, Cloud Business Performance (CBP) and describe ... Expand

Abstract: This paper presents a new concept, Cloud Business Performance (CBP) and describes the method of measurement, data analysis, impacts to manufacturing and case studies about CPB. Three methods can be used for CBP with two case studies illustrated. The first case study presents a small and medium manufacturing enterprise that has adopted backup services for all manufacturing transactions and records. The second case study shows a software manufacturing organization’s forecasting on their business performance and risk. Methods used, results and analysis have been fully justifiable to support the case of CBP for manufacturing organizations. We demonstrate that the use of CBP calculation and prediction analysis is useful for manufacturing organizations that adopt Cloud Computing. Collapse

Topics: organizational productivity cloud computing backup new product development project management

Methods: case study simulation literature study descriptive statistic longitudinal research

Entering a Golden Age of Sustained Superiority: Entrepreneurial Creation or Discovery?

2020 | Organization Science | Citations: 0

Authors: Henderson, Andrew D.; Graebner, Melissa E.

Abstract: To better understand the origins of sustained superior firm performance, we cons ... Expand

Abstract: To better understand the origins of sustained superior firm performance, we consider processes of entrepreneurial creation and discovery. Discovering new opportunities requires speed and flexibility, and although many discoveries are easy to implement, this makes them easy to imitate. By comparison, creation is more complex, iterative, and cumulative, making these opportunities slower to implement yet harder to copy. In formal simulation modeling and analyses of the U.S. Compustat population from 1966 to 2015, we found a bimodal pattern of entry into golden eras of sustained superiority. One peak consisted of an initial spike as firms went public, and evidence suggests this was driven by entrepreneurial discovery. The other peak involved a long-term inverted U-shaped trajectory that reached its high point about 17 years later. Evidence suggests this was driven by entrepreneurial creation, which first enabled then later constrained durable superiority. Our results have implications for research on sustained superiority, entrepreneurial opportunities, and strategic renewal. Collapse

Topics: entrepreneurship accounting organizational productivity economies of scale strategic management

Methods: simulation Compustat experiment computational algorithm hazard function

Theories: resource based view of the firm

If Higher Pay Is Profitable, Why Is It So Rare? Modeling Competing Strategies in Mass Market Services

2020 | Organization Science | Citations: 1

Authors: Rahmandad, Hazhir; Ton, Zeynep

Abstract: Several case studies suggest that firms targeting mass market services can align ... Expand

Abstract: Several case studies suggest that firms targeting mass market services can align profitability with jobs offering a living wage, stable schedules, and engaging work. Yet, few do. To understand this puzzle, we draw on theories of firms as systems of interdependent choices. Building on a few cases, we map the processes connecting managerial choice to performance and formalize the resulting performance landscape. In a strategy space defined by two dimensions—task richness and compensation—two local profitability peaks emerge: one with low compensation and low task richness and one with high compensation and high task richness. The bimodal landscape results from complementarity among choices and is robust when the strategy space is expanded from two to six dimensions and under many alternative parameterizations. Exploring how firms discover, move to, and remain at the high-compensation–high-task richness peak, we find three challenges to this strategy: (a) contextuality—adoption, imitation, and replication are harder for strategies that rely on interdependences among components and thus, require significant customization for each context; (b) temporal complexity—strategies depending on long-term and synergistic investments and slow-moving reinforcing feedbacks are hard to learn owing to misleading performance feedback; and (c) variable demand with no inventory buffers—efforts to adjust labor supply to highly variable demand in services often lead to unstable schedules given with short notice that drive quality employees away and compromise the strategy. These mechanisms can undermine promising strategies even if the actual performance landscape includes a small number of local peaks. Collapse

Topics: human resource management IT personell turnover business-to-business decision making customer service

Methods: simulation experiment case study theory development sensitivity analysis

Theories: management theory

Assessing the Gap between Technology and the Environmental Sustainability of European Cities

2019 | Information Systems Frontiers | Citations: 6

Authors: Akande, Adeoluwa; Cabral, Pedro; Casteleyn, Sven

Abstract: The growth of cities’ population increased the interest in the opportunities and ... Expand

Abstract: The growth of cities’ population increased the interest in the opportunities and challenges that Information and Communication Technology (ICT) have on carbon footprint reduction, which fosters their environmental sustainability. Using Principal Component Analysis (PCA), six ICT-related variables from European Union (EU) cities were combined into a single two-dimensional ICT index. Then, through cluster analysis, cities were clustered into four groups based on the ICT index and Carbon dioxide (CO2) emissions. Using ICT as an indicator of smartness and CO2 emissions as an indicator of sustainability, we show that it is possible for a city to be smart but not sustainable and vice versa. Results also indicate that there is a gap between cities in northern Europe, which are the top performers in both categories, and cities in south-eastern Europe, which do not perform as well. The need for a common strategy for achieving integrated smart, sustainable and inclusive growth at a European level is demonstrated. Collapse

Topics: systems development environmental sustainability information technology infrastructure climate change smart city

Methods: data transformation principal component analysis cluster analysis hierarchical clustering computational algorithm

RISK MITIGATION CAPABILITY OF FLEXIBILITY PERFORMANCE CONTRACTS FOR DEMAND RESPONSE IN ELECTRICITY SYSTEMS

2019 | European Conference On Information Systems | Citations: 0

Authors: Jã, Florian

Abstract: The transition of the energy system increases the urgency to cope with the inter ... Expand

Abstract: The transition of the energy system increases the urgency to cope with the intermittency of renewable energy sources to keep the electricity network balanced. Demand Response (DR) measures are a promising approach to align the electricity consumption, especially of industrial consumers, with current electricity supply. While adequate information systems (IS) are already in place to dynamically adapt electricity consumption patterns, industrial consumers are still reluctant to implement DR measures due to uncertainty of their financial performance. Nevertheless, studies on risk transfer instruments related to DR investments are still scarce. To contribute to the closure of this research gap, we examine the risk transfer capability of Flexibility Performance Contracts (FPC). We derive cash flow structures for representative FPC designs, calculate risk premiums and enable the comparison of corresponding risk profiles. Presented FPCs are evaluated based on a real-world industrial use case. Thereby, the financial performance is modelled stochastically, taking electricity price fluctuation, industrial process characteristics and IS-backed decisions into account. Our results reveal that FPCs represent well-suited risk transfer instruments for DR measures. Thus, FPCs have the potential to accelerate the application of DR measures and therefore to complement existing capabilities of IS in the context of electricity networks. Collapse

Topics: energy information system financial performance decision support price management moral hazard

Methods: simulation generalized autoregressive conditional heteroskedasticity model longitudinal research autoregressive moving average model case study

Theories: transaction cost economics

Discovering Discontinuity in Big Financial Transaction Data

2018 | ACM Transactions on Management Information Systems | Citations: 1

Authors: Tuarob, Suppawong; Strong, Ray; Chandra, Anca; Tucker, Conrad S.

Abstract: Business transactions are typically recorded in the company ledger. The primary ... Expand

Abstract: Business transactions are typically recorded in the company ledger. The primary purpose of such financial information is to accompany a monthly or quarterly report for executives to make sound business decisions and strategies for the next business period. These business strategies often result in transitions that cause underlying infrastructures and components to change, including alteration in the nomenclature system of the business components. As a result, a transaction stream of an affected component would be replaced by another stream with a different component name, resulting in discontinuity of a financial stream of the same component. Recently, advancement in large-scale data mining technologies has enabled a set of critical applications to utilize knowledge extracted from a vast amount of existing data that would otherwise have been unused or underutilized. In financial and services computing domains, recent studies have illustrated that historical financial data could be used to predict future revenues and profits, optimizing costs, among other potential applications. These prediction models rely on long-term availability of the historical data that traces back for multiple years. However, the discontinuity of the financial transaction stream associated with a business component has limited the learning capability of the prediction models. In this article, we propose a set of machine learning–based algorithms to automatically discover component name replacements, using information available in general ledger databases. The algorithms are designed to be scalable for handling massive data points, especially in large companies. Furthermore, the proposed algorithms are generalizable to other domains whose data is time series and shares the same nature as the financial data available in business ledgers. A case study of real-world IBM service delivery retrieved from four different geographical regions is used to validate the efficacy of the proposed methodology. Collapse

Topics: outsourcing database system

Methods: computational algorithm longitudinal research machine learning time series analysis classification method