Hungarian abstract: : A kriptovalutak nepszerűsege vilagszerte novekszik. Hasznaljak őket fizetesre, befektetesre, kincskepzesre, annak ellenere, hogy nem minősulnek fizetőeszkoznek, ertekpapirnak, vagy vagyontargynak, bar ketsegkivul minden kategorianak a tulajdonsagaibol rendelkeznek nehannyal. Egy magyar allasfoglalas szerint a krioptovalutakat egyeb kovetelesnek kell tekinteni, mig a nemzetkozi szamviteli sztenderdek ajanlasa szerint a kriptovalutakat vagy az immaterialis javak, vagy a keszletek kozott kell bemutatni. A cikk elemzi a kriptovalutak tulajdonsagait, es bemutatja, hogy milyen tarsasagi adozasi kovetkezmenyei vannak annak, ha a vallalkozas a magyar allasfoglalas ajanlasat koveti, es hogyan vezet a helytelen besorolas fals adozasi eredmenyekhez. A szerző az allasfoglalas visszavonasat, es uj szamviteli szabalyok es ertelmezes megalkotasat javasolja – akar egy uj eszkozkategoria megalkotasaval a kriptovalutak szamara. English abstract: Cryptocurrencies are gaining in popularity worldwide. They are used as if they were currencies, securities, debt or equity instruments, or property. They are neither of those things although they certainly show characteristics of each categories. A Hungarian non-binding ruling classifies them as claims or accounts receivable, while the international accounting standards recommend to present cryptocurrencies either as intangible assets or as inventories. The article analyses the characteristics of cryptocurrencies, and the corporate income tax consequences of following the recommendations of the Hungarian non-binding ruling. It shows why the wrong classification of cryptocurrencies leads to false tax results. The author recommends the withdrawal of the tax ruling and the establishment of new accounting rules and interpretations - possibly also the introduction of a new accounting category for cryptocurrencies.
{"title":"Bitcoin, a lejárat nélküli követelés? (Bitcoin: Receivable Without Due Date?)","authors":"Gabriella Erdős","doi":"10.2139/ssrn.3676977","DOIUrl":"https://doi.org/10.2139/ssrn.3676977","url":null,"abstract":"Hungarian abstract: : A kriptovalutak nepszerűsege vilagszerte novekszik. Hasznaljak őket fizetesre, befektetesre, kincskepzesre, annak ellenere, hogy nem minősulnek fizetőeszkoznek, ertekpapirnak, vagy vagyontargynak, bar ketsegkivul minden kategorianak a tulajdonsagaibol rendelkeznek nehannyal. Egy magyar allasfoglalas szerint a krioptovalutakat egyeb kovetelesnek kell tekinteni, mig a nemzetkozi szamviteli sztenderdek ajanlasa szerint a kriptovalutakat vagy az immaterialis javak, vagy a keszletek kozott kell bemutatni. A cikk elemzi a kriptovalutak tulajdonsagait, es bemutatja, hogy milyen tarsasagi adozasi kovetkezmenyei vannak annak, ha a vallalkozas a magyar allasfoglalas ajanlasat koveti, es hogyan vezet a helytelen besorolas fals adozasi eredmenyekhez. A szerző az allasfoglalas visszavonasat, es uj szamviteli szabalyok es ertelmezes megalkotasat javasolja – akar egy uj eszkozkategoria megalkotasaval a kriptovalutak szamara. \u0000 \u0000English abstract: Cryptocurrencies are gaining in popularity worldwide. They are used as if they were currencies, securities, debt or equity instruments, or property. They are neither of those things although they certainly show characteristics of each categories. A Hungarian non-binding ruling classifies them as claims or accounts receivable, while the international accounting standards recommend to present cryptocurrencies either as intangible assets or as inventories. The article analyses the characteristics of cryptocurrencies, and the corporate income tax consequences of following the recommendations of the Hungarian non-binding ruling. It shows why the wrong classification of cryptocurrencies leads to false tax results. The author recommends the withdrawal of the tax ruling and the establishment of new accounting rules and interpretations - possibly also the introduction of a new accounting category for cryptocurrencies.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82816972","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}
Pub Date : 2020-05-24DOI: 10.1007/978-3-030-54252-8_12
Stéphane Goutte, Benjamin Keddad
{"title":"A Non-Linear Approach to Measure the Dependencies Between Bitcoin and Other Commodity Markets.","authors":"Stéphane Goutte, Benjamin Keddad","doi":"10.1007/978-3-030-54252-8_12","DOIUrl":"https://doi.org/10.1007/978-3-030-54252-8_12","url":null,"abstract":"","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74465657","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}
A significant part of the fast-moving dynamics in the high-tech industries is due to the high rate of new entry in the form of entrepreneurial ventures. Leveraging a novel measure of new entry threats using text mining, we test the conjecture that threats from new entry lead to deterioration in operational performance. In addition, we hypothesize that the relationship is moderated by corporate board capital, a proxy of the board’s ability to leverage outside resources. We distinguish board capital breadth, measuring the diversity or heterogeneity of the board’s social ties, from board capital depth, or the embeddedness of the board in the focal firm’s industry. Our evidences show that a higher level of new entry threats indeed leads to an incumbent’s performance deterioration. Interestingly, we find that the moderating effect of board capital depends on its nature: particularly, board capital breadth mitigates the negative impact of new entry threats, while board capital depth strengthens the negative relationship between new entry threats and firm performance. We discuss the implications for research and practices.
{"title":"Is Board Capital a Double-Edged Sword? New Entry Threats and Firm Performance: On Examining the Moderating Role of Board Capital","authors":"Yang Pan, Peng Huang, A. Gopal","doi":"10.2139/ssrn.3606875","DOIUrl":"https://doi.org/10.2139/ssrn.3606875","url":null,"abstract":"A significant part of the fast-moving dynamics in the high-tech industries is due to the high rate of new entry in the form of entrepreneurial ventures. Leveraging a novel measure of new entry threats using text mining, we test the conjecture that threats from new entry lead to deterioration in operational performance. In addition, we hypothesize that the relationship is moderated by corporate board capital, a proxy of the board’s ability to leverage outside resources. We distinguish board capital breadth, measuring the diversity or heterogeneity of the board’s social ties, from board capital depth, or the embeddedness of the board in the focal firm’s industry. Our evidences show that a higher level of new entry threats indeed leads to an incumbent’s performance deterioration. Interestingly, we find that the moderating effect of board capital depends on its nature: particularly, board capital breadth mitigates the negative impact of new entry threats, while board capital depth strengthens the negative relationship between new entry threats and firm performance. We discuss the implications for research and practices.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83879276","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 study proposes a novel supervised deep topic modeling approach for effective text analysis. This approach leverages the auxiliary data associated with text, such as ratings in consumer reviews or categories of posts in online forums, to enhance the discovery of latent topics in text. The proposed approach can effectively improve topic modeling performance in several ways. First, the learned latent topics are more meaningful and distinguishable, which helps text data exploration. Second, the latent topics discovered by the novel supervised deep topic model are more accurate, which improves the performance of downstream econometrics and predictive analytics that utilize latent topics as inputs. Given the prevalence of auxiliary data in real-world text analysis tasks and the wide adoption of topic modeling in business research and practice, the study offers an effective solution for extracting insights from text data.
{"title":"sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics","authors":"Yi Yang, Kunpeng Zhang, Yangyang Fan","doi":"10.2139/ssrn.3612168","DOIUrl":"https://doi.org/10.2139/ssrn.3612168","url":null,"abstract":"This study proposes a novel supervised deep topic modeling approach for effective text analysis. This approach leverages the auxiliary data associated with text, such as ratings in consumer reviews or categories of posts in online forums, to enhance the discovery of latent topics in text. The proposed approach can effectively improve topic modeling performance in several ways. First, the learned latent topics are more meaningful and distinguishable, which helps text data exploration. Second, the latent topics discovered by the novel supervised deep topic model are more accurate, which improves the performance of downstream econometrics and predictive analytics that utilize latent topics as inputs. Given the prevalence of auxiliary data in real-world text analysis tasks and the wide adoption of topic modeling in business research and practice, the study offers an effective solution for extracting insights from text data.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78800741","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}
Despite the popularity of ridesharing, there is limited empirical evidence on how ridesharing activities differ across regions with different levels of accessibility and the implication for consumers. In this paper, we study the market for rides across New York City neighborhoods. We construct a novel data set that contains massive API queries on route-specific estimates of pricing, wait time, and travel time of Uber, Lyft, and the public transit. After linking this data with actual trip records of taxis, Uber, and Lyft, we document a strong pattern that ridesharing has a larger market share relative to taxis in neighborhoods with lower accessibility, defined either in terms of geographic distance to Midtown Manhattan or "economic distance" to job opportunities. Next, we estimate a discrete-choice model of demand for rides and interpret the geography of ridesharing through the lens of the model. We find that consumer surplus from ridesharing varies drastically across geography: passengers that are 5 to 15 miles (resp. more than 15 miles) from Midtown experience a 60% (resp. 19%) larger consumer surplus relative to passengers that are within 5 miles from Midtown. Additionally, over half of these gains comes from reduced wait time. We discuss the implications of the distributional results for policy makers.
{"title":"The Geography of Ridesharing: A Case Study of New York City","authors":"Chungsang Lam, Meng Liu, Xiang Hui","doi":"10.2139/ssrn.2997190","DOIUrl":"https://doi.org/10.2139/ssrn.2997190","url":null,"abstract":"Despite the popularity of ridesharing, there is limited empirical evidence on how ridesharing activities differ across regions with different levels of accessibility and the implication for consumers. In this paper, we study the market for rides across New York City neighborhoods. We construct a novel data set that contains massive API queries on route-specific estimates of pricing, wait time, and travel time of Uber, Lyft, and the public transit. After linking this data with actual trip records of taxis, Uber, and Lyft, we document a strong pattern that ridesharing has a larger market share relative to taxis in neighborhoods with lower accessibility, defined either in terms of geographic distance to Midtown Manhattan or \"economic distance\" to job opportunities. Next, we estimate a discrete-choice model of demand for rides and interpret the geography of ridesharing through the lens of the model. We find that consumer surplus from ridesharing varies drastically across geography: passengers that are 5 to 15 miles (resp. more than 15 miles) from Midtown experience a 60% (resp. 19%) larger consumer surplus relative to passengers that are within 5 miles from Midtown. Additionally, over half of these gains comes from reduced wait time. We discuss the implications of the distributional results for policy makers.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81479303","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}
Problem definition: We consider appointment scheduling workflows for providing access to new patients seeking appointments at specialty group-practice departments and offer a novel demand management mechanism that enables the best use of care capacity through prioritization of requests. Relevance: Considering the increasing demand for healthcare, it is imperative that capacity-constrained specialty departments intentionally manage demand, particularly during the new patient intake process. Methodology: The novel Time Windows Access Protocol (TWAP) prioritizes appointment requests considering the corresponding patient population’s sensitivity to access delays. Under TWAP, scheduling agents are provided, for each patient priority class, a distinct contiguous portion of the booking horizon (i.e., time window) that they can use to search for an available appointment upon a request from Priority n. High priority classes are incentivized to book and attend appointments with low wait offers, while sufficient dilution of requests from lower priority classes are induced with higher (but still, medically safe) levels of wait. Results: We develop a closed-form, computationally efficient model to determine the optimal set of time windows to use for given delay-dependent appointment realization probabilities. We demonstrate the calculation and use of TWAP under strict prioritization and compromised prioritization objectives for a number of experimental settings and a real-life case study. Managerial Implications: TWAP allows direct control of access delays for different priority classes. Due to patients’ sensitivity to wait (characterizable through available data) this is equivalent to controlling fill rates of different priority classes, allowing us to manage patient demand and match it to care capacity. Under TWAP, slot utilization or overbooks are observed, and can be used as signals to self-correct. Other reservation-type prioritization mechanisms are generally effective, but they are hard to compute. They are also hard to implement since they attempt to control or protect capacity and observe access delays or fill rates.
{"title":"Patient Demand Management through Prioritized Access with Time Windows Access Protocol","authors":"Derya Kilinc, E. Gel, M. Sir, K. Pasupathy","doi":"10.2139/ssrn.3602222","DOIUrl":"https://doi.org/10.2139/ssrn.3602222","url":null,"abstract":"Problem definition: We consider appointment scheduling workflows for providing access to new patients seeking appointments at specialty group-practice departments and offer a novel demand management mechanism that enables the best use of care capacity through prioritization of requests. \u0000 \u0000Relevance: Considering the increasing demand for healthcare, it is imperative that capacity-constrained specialty departments intentionally manage demand, particularly during the new patient intake process. \u0000 \u0000Methodology: The novel Time Windows Access Protocol (TWAP) prioritizes appointment requests considering the corresponding patient population’s sensitivity to access delays. Under TWAP, scheduling agents are provided, for each patient priority class, a distinct contiguous portion of the booking horizon (i.e., time window) that they can use to search for an available appointment upon a request from Priority n. High priority classes are incentivized to book and attend appointments with low wait offers, while sufficient dilution of requests from lower priority classes are induced with higher (but still, medically safe) levels of wait. \u0000 \u0000Results: We develop a closed-form, computationally efficient model to determine the optimal set of time windows to use for given delay-dependent appointment realization probabilities. We demonstrate the calculation and use of TWAP under strict prioritization and compromised prioritization objectives for a number of experimental settings and a real-life case study. Managerial Implications: TWAP allows direct control of access delays for different priority classes. Due to patients’ sensitivity to wait (characterizable through available data) this is equivalent to controlling fill rates of different priority classes, allowing us to manage patient demand and match it to care capacity. Under TWAP, slot utilization or overbooks are observed, and can be used as signals to self-correct. Other reservation-type prioritization mechanisms are generally effective, but they are hard to compute. They are also hard to implement since they attempt to control or protect capacity and observe access delays or fill rates.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82543458","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}
Micro-Videos are playing a vital role in almost every field on the Internet like social-media, advertisement, education, entertainment, et cetera. It gives a way to brands and makers to achieve a large number of the individuals in only a moment. Users need to spend a lot of time in search of their desired ones and producer finds difficulty in getting the content liked by users. So, Micro-Video market needs a better and effective recommendation system. This paper provides a survey report on the various methodologies used for Micro-Video Recommendation System along with their advantages and drawbacks which will assist in the further advancement and the better implementation of the system.
{"title":"Various Methodologies for Micro-Video Recommendation System: A Survey","authors":"J. Raj, Amirul Hoque, Dr Ashim Saha","doi":"10.2139/ssrn.3598885","DOIUrl":"https://doi.org/10.2139/ssrn.3598885","url":null,"abstract":"Micro-Videos are playing a vital role in almost every field on the Internet like social-media, advertisement, education, entertainment, et cetera. It gives a way to brands and makers to achieve a large number of the individuals in only a moment. Users need to spend a lot of time in search of their desired ones and producer finds difficulty in getting the content liked by users. So, Micro-Video market needs a better and effective recommendation system. This paper provides a survey report on the various methodologies used for Micro-Video Recommendation System along with their advantages and drawbacks which will assist in the further advancement and the better implementation of the system.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86869783","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}
The fields of the history and methodology of economics have recently experienced a quantitative turn. Among the quantitative tools and methods recently used, text mining has received less attention. In this article, we apply a “text as data” approach to the study of Adam Smith’s The Wealth of Nations and The Theory of Moral Sentiments. We first study these two classics through the lenses of readability and lexical richness indicators and sentiment analysis. We then determine the most relevant words and topics in the two books and, as case studies, we examine the specific topics of opulence, poverty, prosperity, sympathy, tax, war, wealth, and woman. Our results confirm some of the well-known differences between the two books (thus supporting the argument that text mining is a powerful and generally reliable tool for exploratory research on unknown documents), but also provide new insights about them.
{"title":"Toward a 'Text as Data' Approach in the History and Methodology of Economics: An Application to Adam Smith’s Classics","authors":"Matthieu Ballandonne, I. Cersosimo","doi":"10.2139/ssrn.3595120","DOIUrl":"https://doi.org/10.2139/ssrn.3595120","url":null,"abstract":"The fields of the history and methodology of economics have recently experienced a quantitative turn. Among the quantitative tools and methods recently used, text mining has received less attention. In this article, we apply a “text as data” approach to the study of Adam Smith’s The Wealth of Nations and The Theory of Moral Sentiments. We first study these two classics through the lenses of readability and lexical richness indicators and sentiment analysis. We then determine the most relevant words and topics in the two books and, as case studies, we examine the specific topics of opulence, poverty, prosperity, sympathy, tax, war, wealth, and woman. Our results confirm some of the well-known differences between the two books (thus supporting the argument that text mining is a powerful and generally reliable tool for exploratory research on unknown documents), but also provide new insights about them.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82339276","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}
Pub Date : 2020-05-01DOI: 10.26595/eamr.2014.6.2.4
Josep M. Rosanas
In the last few decades, there has been some concern about forms becoming less and less “human”. Or, in other words, becoming a place where human beings cannot self- actualize, where they are only pieces of a puzzle or “adjuncts to machines”. This paper intends to review briefly the previous situation in the two decades after WW II, and then show how dehumanization of the firm and of the economic context of the firms begun after the crisis of the 70’s.
Next, the paper goes to Management Control Systems, revising the “classical” systems and analyzing the Balanced Scorecard, which has been possible the main tool used for management control in the last forty years, showing how it is in fact unbalanced and dehumanizing. After illustrating this through a case example, it is concluded that indicators used for diagnostic for self-control only, with no implication in hierarchical control and/or incentives, may be very useful tools for management.
{"title":"The Dehumanization and Demoralization of Management Control Systems: Can We Possibly Re-Humanize and Re- Moralize Them?","authors":"Josep M. Rosanas","doi":"10.26595/eamr.2014.6.2.4","DOIUrl":"https://doi.org/10.26595/eamr.2014.6.2.4","url":null,"abstract":"In the last few decades, there has been some concern about forms becoming less and less “human”. Or, in other words, becoming a place where human beings cannot self- actualize, where they are only pieces of a puzzle or “adjuncts to machines”. This paper intends to review briefly the previous situation in the two decades after WW II, and then show how dehumanization of the firm and of the economic context of the firms begun after the crisis of the 70’s.<br><br>Next, the paper goes to Management Control Systems, revising the “classical” systems and analyzing the Balanced Scorecard, which has been possible the main tool used for management control in the last forty years, showing how it is in fact unbalanced and dehumanizing. After illustrating this through a case example, it is concluded that indicators used for diagnostic for self-control only, with no implication in hierarchical control and/or incentives, may be very useful tools for management.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86741854","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}
Jiankun Sun, Dennis J. Zhang, Haoyuan Hu, J. V. Mieghem
Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box) focus on box volume utilization yet tend to overlook human behavioral deviations. We observe that packing workers at the warehouses of the Alibaba Group deviate from algorithmic prescriptions for 5.8% of packages, and these deviations increase packing time and reduce operational efficiency. We posit two mechanisms and demonstrate that they result in two types of deviations: (1) information deviations stem from workers having more information and in turn better solutions than the algorithm; and (2) complexity deviations result from workers’ aversion, inability, or discretion to precisely implement algorithmic prescriptions. We propose a new “human-centric bin packing algorithm” that anticipates and incorporates human deviations to reduce deviations and improve performance. It predicts when workers are more likely to switch to larger boxes using machine learning techniques and then proactively adjusts the algorithmic prescriptions of those “targeted packages.” We conducted a large-scale randomized field experiment with the Alibaba Group. Orders were randomly assigned to either the new algorithm (treatment group) or Alibaba’s original algorithm (control group). Our field experiment results show that our new algorithm lowers the rate of switching to larger boxes from 29.5% to 23.8% for targeted packages and reduces the average packing time of targeted packages by 4.5%. This idea of incorporating human deviations to improve optimization algorithms could also be generalized to other processes in logistics and operations. This paper was accepted by Charles Corbett, operations management.
{"title":"Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations","authors":"Jiankun Sun, Dennis J. Zhang, Haoyuan Hu, J. V. Mieghem","doi":"10.2139/ssrn.3355114","DOIUrl":"https://doi.org/10.2139/ssrn.3355114","url":null,"abstract":"Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box) focus on box volume utilization yet tend to overlook human behavioral deviations. We observe that packing workers at the warehouses of the Alibaba Group deviate from algorithmic prescriptions for 5.8% of packages, and these deviations increase packing time and reduce operational efficiency. We posit two mechanisms and demonstrate that they result in two types of deviations: (1) information deviations stem from workers having more information and in turn better solutions than the algorithm; and (2) complexity deviations result from workers’ aversion, inability, or discretion to precisely implement algorithmic prescriptions. We propose a new “human-centric bin packing algorithm” that anticipates and incorporates human deviations to reduce deviations and improve performance. It predicts when workers are more likely to switch to larger boxes using machine learning techniques and then proactively adjusts the algorithmic prescriptions of those “targeted packages.” We conducted a large-scale randomized field experiment with the Alibaba Group. Orders were randomly assigned to either the new algorithm (treatment group) or Alibaba’s original algorithm (control group). Our field experiment results show that our new algorithm lowers the rate of switching to larger boxes from 29.5% to 23.8% for targeted packages and reduces the average packing time of targeted packages by 4.5%. This idea of incorporating human deviations to improve optimization algorithms could also be generalized to other processes in logistics and operations. This paper was accepted by Charles Corbett, operations management.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":"104 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78776782","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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