S. Schuetz, P. Lowry, Daniel A. Pienta, J. Thatcher
A substantial amount of previous research has examined the efficacy of fear appeals to elicit security-enhancing behaviors from users. However, despite more than a decade of research on fear appeals in security contexts, researchers have yet to understand which factors drive users’ responses to fear appeals. Instead, the literature is riddled with inconsistent findings on the antecedents that predict fear-appeal outcomes, fueling controversy and inhibiting progress on the problem. This research addresses the inconsistent findings by using construal level theory (CLT) to explain how temporal distance and argument nature affect fear-appeal appraisal. Based on two online experiments, we report evidence showing that temporal distance determines which antecedents drive fear-appeal outcomes, which helps explain inconsistent results found in prior literature. Moreover, we found that depending on the temporal distance condition, argument nature (i.e., “how” or “why” arguments) can impact the effectiveness of fear appeals. Overall, our findings refine the understanding of when certain factors influence users’ responses to fear appeals and provide guidance for future research on how to create more effective fear appeals.
{"title":"Improving the Design of Information Security Messages by Leveraging the Effects of Temporal Distance and Argument Nature","authors":"S. Schuetz, P. Lowry, Daniel A. Pienta, J. Thatcher","doi":"10.2139/ssrn.3718606","DOIUrl":"https://doi.org/10.2139/ssrn.3718606","url":null,"abstract":"A substantial amount of previous research has examined the efficacy of fear appeals to elicit security-enhancing behaviors from users. However, despite more than a decade of research on fear appeals in security contexts, researchers have yet to understand which factors drive users’ responses to fear appeals. Instead, the literature is riddled with inconsistent findings on the antecedents that predict fear-appeal outcomes, fueling controversy and inhibiting progress on the problem. This research addresses the inconsistent findings by using construal level theory (CLT) to explain how temporal distance and argument nature affect fear-appeal appraisal. Based on two online experiments, we report evidence showing that temporal distance determines which antecedents drive fear-appeal outcomes, which helps explain inconsistent results found in prior literature. Moreover, we found that depending on the temporal distance condition, argument nature (i.e., “how” or “why” arguments) can impact the effectiveness of fear appeals. Overall, our findings refine the understanding of when certain factors influence users’ responses to fear appeals and provide guidance for future research on how to create more effective fear appeals.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80609063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dereck Barr‐Pulliam, Joseph F. Brazel, Jennifer McCallen, Kimberly Walker
We investigate if varying rates of false positives impact auditor skepticism toward red flags identified by data analytic tools. We also examine the extent to which consistent rewards for skepticism can improve the application of skepticism on audits employing data analytics. Using an experiment with practicing auditors we observe that, when false positive rates are higher, skepticism levels are low. Importantly, both our lower and higher false positive conditions reflect well calibrated analytic tests. We also find that consistent rewards for skepticism significantly improve the skepticism of our auditors. However, the positive effect of rewards is limited, in that we do not see improvements in skepticism when the false positive rate is higher. Our findings highlight the importance of improving the calibration of analytic tests to reduce false positives and the need for a culture change where appropriate skepticism is consistently rewarded in order for audit firms to effectively use analytic tools to enhance audit quality.
{"title":"Data Analytics and Skeptical Actions: The Countervailing Effects of False Positives and Consistent Rewards for Skepticism","authors":"Dereck Barr‐Pulliam, Joseph F. Brazel, Jennifer McCallen, Kimberly Walker","doi":"10.2139/ssrn.3537180","DOIUrl":"https://doi.org/10.2139/ssrn.3537180","url":null,"abstract":"We investigate if varying rates of false positives impact auditor skepticism toward red flags identified by data analytic tools. We also examine the extent to which consistent rewards for skepticism can improve the application of skepticism on audits employing data analytics. Using an experiment with practicing auditors we observe that, when false positive rates are higher, skepticism levels are low. Importantly, both our lower and higher false positive conditions reflect well calibrated analytic tests. We also find that consistent rewards for skepticism significantly improve the skepticism of our auditors. However, the positive effect of rewards is limited, in that we do not see improvements in skepticism when the false positive rate is higher. Our findings highlight the importance of improving the calibration of analytic tests to reduce false positives and the need for a culture change where appropriate skepticism is consistently rewarded in order for audit firms to effectively use analytic tools to enhance audit quality.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76427858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While the digital economy has contributed substantially to productivity growth and the emergence of new forms of civic discourse and connectivity, it has also exposed consumers and businesses to various risks associated with use of digital technologies. A chorus of global policy elites regularly sounds the alarm about the growing magnitude of these risks and the urgent need for businesses and governments to respond. And yet, despite the drumbeat of warnings, policy elites regularly express deep dissatisfaction with the status quo. What explains this apparent discrepancy between elite’s demands for more cyber resilience and their political systems’ apparent failure to deliver that resilience? We believe that part of the answer may have to do with the perceptions of digital risks by ordinary individuals. Researchers who have examined cyber risk from an economic perspective have emphasized how information asymmetries and especially externalities contribute to underinvestment in cybersecurity. Our findings in this study add an additional dimension to this work: consumers underestimate risk even when the distribution of costs affects them more directly, as is the case with fraud.
{"title":"Perception of Digital Risks: Evidence from 54 Countries","authors":"AJ Grotto, C. Makridis","doi":"10.2139/ssrn.3711862","DOIUrl":"https://doi.org/10.2139/ssrn.3711862","url":null,"abstract":"While the digital economy has contributed substantially to productivity growth and the emergence of new forms of civic discourse and connectivity, it has also exposed consumers and businesses to various risks associated with use of digital technologies. A chorus of global policy elites regularly sounds the alarm about the growing magnitude of these risks and the urgent need for businesses and governments to respond. And yet, despite the drumbeat of warnings, policy elites regularly express deep dissatisfaction with the status quo. What explains this apparent discrepancy between elite’s demands for more cyber resilience and their political systems’ apparent failure to deliver that resilience? We believe that part of the answer may have to do with the perceptions of digital risks by ordinary individuals. Researchers who have examined cyber risk from an economic perspective have emphasized how information asymmetries and especially externalities contribute to underinvestment in cybersecurity. Our findings in this study add an additional dimension to this work: consumers underestimate risk even when the distribution of costs affects them more directly, as is the case with fraud.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81524465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Financial news has been identified as an important alternative information source for modeling market dynamics in recent years. While most of the attention goes to stock markets, the foreign exchange (Forex) market, in contrast, is much less studied. Most of the existing text mining research for the Forex market combine news sentiment with other text features, making the contribution of each factor unclear. To this end, we want to study the role of news sentiment exclusively. In particular, we propose a FinBERT-based model to extract high-frequency news sentiment as a 4-dimensional time series. We examine the efficacy of this news sentiment for Forex market prediction without involving any other semantic feature. Experiments show that our model outperforms alternative sentiment analysis approaches and confirm that news sentiment alone may have predictive power for Forex price movements. The sentiment analysis method seems to have a big potential to improve despite that the current predictive power is still weak. The results deepen our understanding of financial text processing systems.
{"title":"High-Frequency News Sentiment and Its Application to Forex Market Prediction","authors":"Frank Xing, D. Hoang, Dinh-Vinh Vo","doi":"10.24251/HICSS.2021.191","DOIUrl":"https://doi.org/10.24251/HICSS.2021.191","url":null,"abstract":"Financial news has been identified as an important alternative information source for modeling market dynamics in recent years. While most of the attention goes to stock markets, the foreign exchange (Forex) market, in contrast, is much less studied. Most of the existing text mining research for the Forex market combine news sentiment with other text features, making the contribution of each factor unclear. To this end, we want to study the role of news sentiment exclusively. In particular, we propose a FinBERT-based model to extract high-frequency news sentiment as a 4-dimensional time series. We examine the efficacy of this news sentiment for Forex market prediction without involving any other semantic feature. Experiments show that our model outperforms alternative sentiment analysis approaches and confirm that news sentiment alone may have predictive power for Forex price movements. The sentiment analysis method seems to have a big potential to improve despite that the current predictive power is still weak. The results deepen our understanding of financial text processing systems.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86272382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract A key benefit of using car sharing services (relative to car ownership) is that they are more cost effective. Car sharing firms offer a menu of pricing plans to make this happen. The two most common plans are flat-rate and pay-per-use pricing. However, little is known about how consumers choose among these pricing plans. In this study, we analyze consumers' choices between pay-per-use and flat-rate pricing using data from a car sharing provider in a large European city. In contrast to previous research, we find a prevalent and time-persistent pay-per-use bias. Specifically, depending on the definition of the bias, 21% to 32% of customers exhibit this bias. This bias also persists over time within customer. We propose three potential explanations for the existence and persistence of this bias. First, we suggest that customers underestimate their usage. Second, we propose that customers have a preference for flexibility, leading them to pay more. Finally, we show that the physical context, such as weather, increases the likelihood of a pay-per-use bias. Our findings suggest that more research into consumer response to pricing in the Sharing Economy is needed.
{"title":"The Existence and Persistence of the Pay-Per-Use Bias in Car Sharing Services","authors":"Katharina Dowling, Puneet Manchanda, Martin Spann","doi":"10.2139/ssrn.3204233","DOIUrl":"https://doi.org/10.2139/ssrn.3204233","url":null,"abstract":"Abstract A key benefit of using car sharing services (relative to car ownership) is that they are more cost effective. Car sharing firms offer a menu of pricing plans to make this happen. The two most common plans are flat-rate and pay-per-use pricing. However, little is known about how consumers choose among these pricing plans. In this study, we analyze consumers' choices between pay-per-use and flat-rate pricing using data from a car sharing provider in a large European city. In contrast to previous research, we find a prevalent and time-persistent pay-per-use bias. Specifically, depending on the definition of the bias, 21% to 32% of customers exhibit this bias. This bias also persists over time within customer. We propose three potential explanations for the existence and persistence of this bias. First, we suggest that customers underestimate their usage. Second, we propose that customers have a preference for flexibility, leading them to pay more. Finally, we show that the physical context, such as weather, increases the likelihood of a pay-per-use bias. Our findings suggest that more research into consumer response to pricing in the Sharing Economy is needed.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74605632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Numerous ML pricing models (Zillow’s Zestimate, Redfin Estimate) have been deployed to make house sale price predictions. They appears to be independent and unbiased signal to resolve pricing friction in the housing market. These ML models – learn from live sale prices and influence the same sales simultaneously. This creates a Feedback Loop where the ML model is confounded by its own previous version. We theoretically show how this Feedback Loop creates a self fulfilling prophecy where ML over estimates its own prediction accuracy and market participants over rely on ML predictions. We use data from Zillow’s Zestimate to establish necessary primitives for the theoretical Feedback Loop phenomenon. We also structurally estimate seller payoffs under current and counterfactual ML regimes. We show that ML pricing, instead of alleviating, may widen payoff disparity in favor of sellers with greatest ability to price. This happens because ML lowers pricing Disagreement but adds pricing Bias, with both effects amplified under strong Feedback and high capacity ML.
{"title":"Does Machine Learning Amplify Pricing Errors in Housing Market? : Economics of ML Feedback Loops","authors":"Nikhil Malik","doi":"10.2139/ssrn.3694922","DOIUrl":"https://doi.org/10.2139/ssrn.3694922","url":null,"abstract":"Numerous ML pricing models (Zillow’s Zestimate, Redfin Estimate) have been deployed to make house sale price predictions. They appears to be independent and unbiased signal to resolve pricing friction in the housing market. These ML models – learn from live sale prices and influence the same sales simultaneously. This creates a Feedback Loop where the ML model is confounded by its own previous version. We theoretically show how this Feedback Loop creates a self fulfilling prophecy where ML over estimates its own prediction accuracy and market participants over rely on ML predictions. We use data from Zillow’s Zestimate to establish necessary primitives for the theoretical Feedback Loop phenomenon. We also structurally estimate seller payoffs under current and counterfactual ML regimes. We show that ML pricing, instead of alleviating, may widen payoff disparity in favor of sellers with greatest ability to price. This happens because ML lowers pricing Disagreement but adds pricing Bias, with both effects amplified under strong Feedback and high capacity ML.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87507227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the promise of 3D printing, also known as additive manufacturing, some innovative consumer goods companies have started to experiment with such a technology for on-demand production. However, the potential impact of 3D printing on retail and supply chain operations is not well understood. In this paper, we consider two adoption cases of 3D printing in a dual-channel (i.e., online and in-store) retail setting, and evaluate its impact on a firm's product offering, prices for the two channels, as well as inventory decisions. Our analysis uncovers the following effects of 3D printing. First, 3D printing at the factory has the substitution effect of technological innovation for online demands, as 3D printing replaces the traditional mode of production. Such technology substitution not only leads to increased product variety offered online, which allows the firm to charge a price premium for online customers, but also induces the firm to offer a smaller product variety and a reduced price in-store. Second, when 3D printing is used in-store as well, in additional to the substitution effect, the firm also achieves a structural effect due to the fundamental change in the supply chain structure. Since the in-store demand is served in a build to order fashion, the firm achieves postponement benefits in inventory management. Moreover, using 3D printing in-store will require a new supplier-retailer relationship. We find that cost-sharing contracts can coordinate the supply chains where 3D printing is used in-store and the supplier controls the raw material inventory.
{"title":"Retailing with 3D Printing","authors":"Li Chen, Yao Cui, Hau L. Lee","doi":"10.2139/ssrn.3031566","DOIUrl":"https://doi.org/10.2139/ssrn.3031566","url":null,"abstract":"Given the promise of 3D printing, also known as additive manufacturing, some innovative consumer goods companies have started to experiment with such a technology for on-demand production. However, the potential impact of 3D printing on retail and supply chain operations is not well understood. In this paper, we consider two adoption cases of 3D printing in a dual-channel (i.e., online and in-store) retail setting, and evaluate its impact on a firm's product offering, prices for the two channels, as well as inventory decisions. Our analysis uncovers the following effects of 3D printing. First, 3D printing at the factory has the substitution effect of technological innovation for online demands, as 3D printing replaces the traditional mode of production. Such technology substitution not only leads to increased product variety offered online, which allows the firm to charge a price premium for online customers, but also induces the firm to offer a smaller product variety and a reduced price in-store. Second, when 3D printing is used in-store as well, in additional to the substitution effect, the firm also achieves a structural effect due to the fundamental change in the supply chain structure. Since the in-store demand is served in a build to order fashion, the firm achieves postponement benefits in inventory management. Moreover, using 3D printing in-store will require a new supplier-retailer relationship. We find that cost-sharing contracts can coordinate the supply chains where 3D printing is used in-store and the supplier controls the raw material inventory.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86875045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper involves a pioneering study that delves into the relationship between the acquisitions of UAE based companies and the diversification of the UAE economy. More specifically, it studies the acquisition of Dubai based ride-hailing company Careem by ride-hailing giant Uber Technologies, Inc. and the Dubai based e-commerce company Souq.com by e-commerce giant Amazon.com, Inc.
This paper uses a qualitative method of research and employs the use of secondary sources from economic journals and books as well as news reports and articles. This study deploys case study research design in order to understand the focused efforts of the two giants and how the acquisitions have led to much growth in GDP and an overall increase in the diversification of the UAE economy. The findings of this study reveal that the said acquisitions are likely to lead to efficiency and add competition to domestic companies in the e-commerce and ride-hailing sectors in the market in the UAE. They are also expected to help meet the growing demands of customers owing to their greater global capabilities bringing in international commodities and services. These acquisitions can also become a motivation for innovation in the region, especially with the increased human and physical capital.
This study is preliminary in nature, as the acquisitions are very recent developments, in finalization stages. The information available on the same was very limited at this point in time. There is certainly scope for further research on the subject both as a quantitative and qualitative assessment.
{"title":"Souq-Amazon and Careem-Uber Acquisition Deals: An Analytical Study of the Two Merging Giants in the UAE","authors":"Salma Al-Omari, M. Bishnoi, Mukund Jakhiya","doi":"10.2139/ssrn.3713825","DOIUrl":"https://doi.org/10.2139/ssrn.3713825","url":null,"abstract":"This paper involves a pioneering study that delves into the relationship between the acquisitions of UAE based companies and the diversification of the UAE economy. More specifically, it studies the acquisition of Dubai based ride-hailing company Careem by ride-hailing giant Uber Technologies, Inc. and the Dubai based e-commerce company Souq.com by e-commerce giant Amazon.com, Inc.<br><br>This paper uses a qualitative method of research and employs the use of secondary sources from economic journals and books as well as news reports and articles. This study deploys case study research design in order to understand the focused efforts of the two giants and how the acquisitions have led to much growth in GDP and an overall increase in the diversification of the UAE economy. The findings of this study reveal that the said acquisitions are likely to lead to efficiency and add competition to domestic companies in the e-commerce and ride-hailing sectors in the market in the UAE. They are also expected to help meet the growing demands of customers owing to their greater global capabilities bringing in international commodities and services. These acquisitions can also become a motivation for innovation in the region, especially with the increased human and physical capital.<br><br>This study is preliminary in nature, as the acquisitions are very recent developments, in finalization stages. The information available on the same was very limited at this point in time. There is certainly scope for further research on the subject both as a quantitative and qualitative assessment.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84782512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sai Yeshwanth Chaganti, Ipseeta Nanda, Prajwal Ainapur
Drowsiness is one of the key contributing factors responsible for large number of accidents. As Doctors say Prevention is better than Cure, the same formula is applied in our Solution to the above problem, which can save many Human lives from many accidents and consequently save Medication Bills etc. Mainly Drowsiness is caused due to Sleep Disorders as a result of which Human Sleep gets affected which in turn makes our eyes closed. Recent Studies have proven that Bright Light Exposure on eyes can reduce the fatigue Condition. Based on this phenomenon, A Device like Buzzer with its buzzing sound can also help the driver in increasing his alertness. With the increasing trends in Technology, Microelectronics paved a way in designing many solutions to different problems in the field of Electronic Devices. Challenges in driving mainly include driving time conditions and weather conditions. Summing up everything, Project Idea is to develop a purely hardware-based system so that it increases the feasibility and accuracy of the device as a result it detects the state of eyes of the driver and warn him with the help of Buzzer.
{"title":"Accident Prevention System based on Drowsiness Driver Detector","authors":"Sai Yeshwanth Chaganti, Ipseeta Nanda, Prajwal Ainapur","doi":"10.2139/ssrn.3691548","DOIUrl":"https://doi.org/10.2139/ssrn.3691548","url":null,"abstract":"Drowsiness is one of the key contributing factors responsible for large number of accidents. As Doctors say Prevention is better than Cure, the same formula is applied in our Solution to the above problem, which can save many Human lives from many accidents and consequently save Medication Bills etc. Mainly Drowsiness is caused due to Sleep Disorders as a result of which Human Sleep gets affected which in turn makes our eyes closed. Recent Studies have proven that Bright Light Exposure on eyes can reduce the fatigue Condition. Based on this phenomenon, A Device like Buzzer with its buzzing sound can also help the driver in increasing his alertness. With the increasing trends in Technology, Microelectronics paved a way in designing many solutions to different problems in the field of Electronic Devices. Challenges in driving mainly include driving time conditions and weather conditions. Summing up everything, Project Idea is to develop a purely hardware-based system so that it increases the feasibility and accuracy of the device as a result it detects the state of eyes of the driver and warn him with the help of Buzzer.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90888265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matthias Soppert, Claudius Steinhardt, C. Müller, Jochen Gönsch
Over the last decades, shared mobility systems have become an integral part of the inner-city mobility offer – a prominent example is car sharing. In fact, this work has been motivated by the insights we gained in close collaboration with Share Now, Europe's largest car sharing provider. In car sharing as well as in shared mobility systems in general, pricing optimization has turned out to be a promising means of controlling the complex interactions between demand and supply in order to increase profitability. Practice mostly applies static price differentiation according to a rental's spatial origin and the time of day. In research, however, such approaches have not been considered in detail yet. In this paper, we consider the static origin-based, profit-maximizing pricing problem for shared mobility systems. The problem is characterized by the determination of spatially and temporally differentiated minute prices, by the prevalence of spatio-temporal network effects, and by other practice-relevant aspects, such as a limited fleet size. Based on a deterministic network flow model, we formulate the problem as a mixed-integer linear program and prove it to be NP-hard. We propose a scalable heuristic solution approach that combines the computational benefits of problem decomposition in a rolling horizon fashion with a value function approximation technique adapted from approximate dynamic programming in order to incorporate future spatio-temporal network effects. An extensive computational study demonstrates the benefits of capturing such effects in pricing in general, as well as our value function approximation's ability to anticipate them precisely. Moreover, in a case study based on Share Now data from Florence in Italy, we demonstrate potential profit increases of around 9% compared to the de facto industry standard of constant uniform minute prices.
{"title":"Static Pricing Optimization in Shared Mobility Systems Under the Consideration of Network Effects","authors":"Matthias Soppert, Claudius Steinhardt, C. Müller, Jochen Gönsch","doi":"10.2139/ssrn.3745001","DOIUrl":"https://doi.org/10.2139/ssrn.3745001","url":null,"abstract":"Over the last decades, shared mobility systems have become an integral part of the inner-city mobility offer – a prominent example is car sharing. In fact, this work has been motivated by the insights we gained in close collaboration with Share Now, Europe's largest car sharing provider. In car sharing as well as in shared mobility systems in general, pricing optimization has turned out to be a promising means of controlling the complex interactions between demand and supply in order to increase profitability. Practice mostly applies static price differentiation according to a rental's spatial origin and the time of day. In research, however, such approaches have not been considered in detail yet. \u0000 \u0000In this paper, we consider the static origin-based, profit-maximizing pricing problem for shared mobility systems. The problem is characterized by the determination of spatially and temporally differentiated minute prices, by the prevalence of spatio-temporal network effects, and by other practice-relevant aspects, such as a limited fleet size. Based on a deterministic network flow model, we formulate the problem as a mixed-integer linear program and prove it to be NP-hard. We propose a scalable heuristic solution approach that combines the computational benefits of problem decomposition in a rolling horizon fashion with a value function approximation technique adapted from approximate dynamic programming in order to incorporate future spatio-temporal network effects. An extensive computational study demonstrates the benefits of capturing such effects in pricing in general, as well as our value function approximation's ability to anticipate them precisely. Moreover, in a case study based on Share Now data from Florence in Italy, we demonstrate potential profit increases of around 9% compared to the de facto industry standard of constant uniform minute prices.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83854147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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