Masato Sakata, Zeynep Yücel, K. Shinozawa, N. Hagita, M. Imai, M. Furutani, R. Matsuoka
Common periodical health check-ups include several clinical test items with affordable cost. However, these standard tests do not directly indicate signs of most lifestyle diseases. In order to detect such diseases, a number of additional specific clinical tests are required, which increase the cost of the health check-up. This study aims to enrich our understanding of the common health check-ups and proposes a way to estimate the signs of several lifestyle diseases based on the standard tests in common examinations without performing any additional specific tests. In this manner, we enable a diagnostic process, where the physician may prefer to perform or avoid a costly test according to the estimation carried out through a set of common affordable tests. To that end, the relation between standard and specific test results is modeled with a multivariate kernel density estimate. The condition of the patient regarding a specific test is assessed following a Bayesian framework. Our results indicate that the proposed method achieves an overall estimation accuracy of 84%. In addition, an outstanding estimation accuracy is achieved for a subset of high-cost tests. Moreover, comparison with standard artificial intelligence methods suggests that our algorithm outperforms the conventional methods. Our contributions are as follows: (i) promotion of affordable health check-ups, (ii) high estimation accuracy in certain tests, (iii) generalization capability due to ease of implementation on different platforms and institutions, (iv) flexibility to apply to various tests and potential to improve early detection rates.
{"title":"An Inference Engine for Estimating Outside States of Clinical Test Items","authors":"Masato Sakata, Zeynep Yücel, K. Shinozawa, N. Hagita, M. Imai, M. Furutani, R. Matsuoka","doi":"10.1145/2517084","DOIUrl":"https://doi.org/10.1145/2517084","url":null,"abstract":"Common periodical health check-ups include several clinical test items with affordable cost. However, these standard tests do not directly indicate signs of most lifestyle diseases. In order to detect such diseases, a number of additional specific clinical tests are required, which increase the cost of the health check-up. This study aims to enrich our understanding of the common health check-ups and proposes a way to estimate the signs of several lifestyle diseases based on the standard tests in common examinations without performing any additional specific tests. In this manner, we enable a diagnostic process, where the physician may prefer to perform or avoid a costly test according to the estimation carried out through a set of common affordable tests. To that end, the relation between standard and specific test results is modeled with a multivariate kernel density estimate. The condition of the patient regarding a specific test is assessed following a Bayesian framework. Our results indicate that the proposed method achieves an overall estimation accuracy of 84%. In addition, an outstanding estimation accuracy is achieved for a subset of high-cost tests. Moreover, comparison with standard artificial intelligence methods suggests that our algorithm outperforms the conventional methods.\u0000 Our contributions are as follows: (i) promotion of affordable health check-ups, (ii) high estimation accuracy in certain tests, (iii) generalization capability due to ease of implementation on different platforms and institutions, (iv) flexibility to apply to various tests and potential to improve early detection rates.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122432828","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}
Automated monitoring algorithms operating on live video streamed from a home can effectively aid in several assistive monitoring goals, such as detecting falls or estimating daily energy expenditure. Use of video raises obvious privacy concerns. Several privacy enhancements have been proposed such as modifying a person in video by introducing blur, silhouette, or bounding-box. Person extraction is fundamental in video-based assistive monitoring and degraded in the presence of privacy enhancements; however, privacy enhancements have characteristics that can opportunistically be adapted to. We propose two adaptive algorithms for improving assistive monitoring goal performance with privacy-enhanced video: specific-color hunter and edge-void filler. A nonadaptive algorithm, foregrounding, is used as the default algorithm for the adaptive algorithms. We compare nonadaptive and adaptive algorithms with 5 common privacy enhancements on the effectiveness of 8 automated monitoring goals. The nonadaptive algorithm performance on privacy-enhanced video is degraded from raw video. However, adaptive algorithms can compensate for the degradation. Energy estimation accuracy in our tests degraded from 90.9% to 83.9%, but the adaptive algorithms significantly compensated by bringing the accuracy up to 87.1%. Similarly, fall detection accuracy degraded from 1.0 sensitivity to 0.86 and from 1.0 specificity to 0.79, but the adaptive algorithms compensated accuracy back to 0.92 sensitivity and 0.90 specificity. Additionally, the adaptive algorithms were computationally more efficient than the nonadaptive algorithm, averaging 1.7% more frames processed per second.
{"title":"Accurate and Efficient Algorithms that Adapt to Privacy-Enhanced Video for Improved Assistive Monitoring","authors":"Alex D. Edgcomb, F. Vahid","doi":"10.1145/2523025.2523026","DOIUrl":"https://doi.org/10.1145/2523025.2523026","url":null,"abstract":"Automated monitoring algorithms operating on live video streamed from a home can effectively aid in several assistive monitoring goals, such as detecting falls or estimating daily energy expenditure. Use of video raises obvious privacy concerns. Several privacy enhancements have been proposed such as modifying a person in video by introducing blur, silhouette, or bounding-box. Person extraction is fundamental in video-based assistive monitoring and degraded in the presence of privacy enhancements; however, privacy enhancements have characteristics that can opportunistically be adapted to. We propose two adaptive algorithms for improving assistive monitoring goal performance with privacy-enhanced video: specific-color hunter and edge-void filler. A nonadaptive algorithm, foregrounding, is used as the default algorithm for the adaptive algorithms. We compare nonadaptive and adaptive algorithms with 5 common privacy enhancements on the effectiveness of 8 automated monitoring goals. The nonadaptive algorithm performance on privacy-enhanced video is degraded from raw video. However, adaptive algorithms can compensate for the degradation. Energy estimation accuracy in our tests degraded from 90.9% to 83.9%, but the adaptive algorithms significantly compensated by bringing the accuracy up to 87.1%. Similarly, fall detection accuracy degraded from 1.0 sensitivity to 0.86 and from 1.0 specificity to 0.79, but the adaptive algorithms compensated accuracy back to 0.92 sensitivity and 0.90 specificity. Additionally, the adaptive algorithms were computationally more efficient than the nonadaptive algorithm, averaging 1.7% more frames processed per second.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"686 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132003015","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}
Suprateek Sarker, Suranjan Chakraborty, P. Tansuhaj, M. Mulder, Kivilcim Dogerlioglu-Demir
Information technology (IT) is often an enabler in bringing people together. In the context of this study, IT helps connect matchmaking service providers with those looking for love, particularly when a male seeks to meet and possibly marry a female from another country: a process which results in over 16,500 such ‘mail-order-bride’ (MOB) marriages a year in the United States alone. Past research in business disciplines has been largely silent about the way in which this process unfolds, the perspectives of the participants at different points of time, and the role of IT underlying the MOB matchmaking service. Adopting an interpretivist stance, and utilizing some of the methodological guidelines associated with the Grounded Theory Methodology (GTM), we develop a process model which highlights: a) the key states of the process through which the relationship between the MOB seeker (the man) and the MOB (the woman) unfolds, b) the transitions between states, and c) the triggering conditions for the transitions from one state to another. This study also highlights key motivations of the individuals participating in the MOB process, the effect of power and the role it plays in the dynamics of the relationships, the status of women and how their status evolves during the MOB process, and the unique affordance provided by IT as the relationships evolve.
{"title":"The “Mail-Order-Bride” (MOB) Phenomenon in the Cyberworld: An Interpretive Investigation","authors":"Suprateek Sarker, Suranjan Chakraborty, P. Tansuhaj, M. Mulder, Kivilcim Dogerlioglu-Demir","doi":"10.1145/2524263","DOIUrl":"https://doi.org/10.1145/2524263","url":null,"abstract":"Information technology (IT) is often an enabler in bringing people together. In the context of this study, IT helps connect matchmaking service providers with those looking for love, particularly when a male seeks to meet and possibly marry a female from another country: a process which results in over 16,500 such ‘mail-order-bride’ (MOB) marriages a year in the United States alone. Past research in business disciplines has been largely silent about the way in which this process unfolds, the perspectives of the participants at different points of time, and the role of IT underlying the MOB matchmaking service. Adopting an interpretivist stance, and utilizing some of the methodological guidelines associated with the Grounded Theory Methodology (GTM), we develop a process model which highlights: a) the key states of the process through which the relationship between the MOB seeker (the man) and the MOB (the woman) unfolds, b) the transitions between states, and c) the triggering conditions for the transitions from one state to another. This study also highlights key motivations of the individuals participating in the MOB process, the effect of power and the role it plays in the dynamics of the relationships, the status of women and how their status evolves during the MOB process, and the unique affordance provided by IT as the relationships evolve.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124247396","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}
When a medical practitioner encounters a patient with rare symptoms that translates to rare occurrences in the local database, it is quite valuable to draw conclusions collectively from such occurrences in other hospitals. However, for such rare conditions, there will be a huge imbalance in classes among the relevant base population. Due to regulations and privacy concerns, collecting data from other hospitals will be problematic. Consequently, distributed decision support systems that can use just the statistics of data from multiple hospitals are valuable. We present a system that can collectively build a distributed classification model dynamically without the need of patient data from each site in the case of imbalanced data. The system uses a voting ensemble of experts for the decision model. The imbalance condition and number of experts can be determined by the system. Since only statistics of the data and no raw data are required by the system, patient privacy issues are addressed. We demonstrate the outlined principles using the Nationwide Inpatient Sample (NIS) database. Results of experiments conducted on 7,810,762 patients from 1050 hospitals show improvement of 13.68% to 24.46% in balanced prediction accuracy using our model over the baseline model, illustrating the effectiveness of the proposed methodology.
{"title":"Distributed Privacy-Preserving Decision Support System for Highly Imbalanced Clinical Data","authors":"George Mathew, Z. Obradovic","doi":"10.1145/2517310","DOIUrl":"https://doi.org/10.1145/2517310","url":null,"abstract":"When a medical practitioner encounters a patient with rare symptoms that translates to rare occurrences in the local database, it is quite valuable to draw conclusions collectively from such occurrences in other hospitals. However, for such rare conditions, there will be a huge imbalance in classes among the relevant base population. Due to regulations and privacy concerns, collecting data from other hospitals will be problematic. Consequently, distributed decision support systems that can use just the statistics of data from multiple hospitals are valuable. We present a system that can collectively build a distributed classification model dynamically without the need of patient data from each site in the case of imbalanced data. The system uses a voting ensemble of experts for the decision model. The imbalance condition and number of experts can be determined by the system. Since only statistics of the data and no raw data are required by the system, patient privacy issues are addressed. We demonstrate the outlined principles using the Nationwide Inpatient Sample (NIS) database. Results of experiments conducted on 7,810,762 patients from 1050 hospitals show improvement of 13.68% to 24.46% in balanced prediction accuracy using our model over the baseline model, illustrating the effectiveness of the proposed methodology.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116167542","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}
Information technology is inseparable from contemporary knowledge management (KM). Although anecdotal evidence and individual case studies suggest that effective knowledge management initiatives contribute to superior firm performance, other kinds of empirical investigations are scarce, and more to the point, most of them are based on perceptions of survey participants embedded in the firms being studied. Moreover, studies analyzing the question of whether superior KM performance can predict superior market-based valuation appear to be virtually nonexistent. Findings of such studies would be of value to those who champion and direct a firm’s KM efforts, and to the firm’s strategists, planners, and operational managers. Here, we empirically examine the relationship between KM performance and firm valuation; the former is assessed by international panels of independent KM experts and the latter is evaluated in terms of market-based measures. Based on data spanning eight years, the results show that superior KM performance has a statistically significant positive association with firm valuation in terms of Tobin’s q, price-to-book ratio, and price-to-sales ratio. This study contributes to the management literature by using independent expert judges and archival data to substantiate the notion that KM competencies are an important ingredient in a firm’s performance as indicated by market-based valuation.
{"title":"Does Knowledge Management Matter? The Empirical Evidence from Market-Based Valuation","authors":"Jiming Wu, C. Holsapple","doi":"10.1145/2500750","DOIUrl":"https://doi.org/10.1145/2500750","url":null,"abstract":"Information technology is inseparable from contemporary knowledge management (KM). Although anecdotal evidence and individual case studies suggest that effective knowledge management initiatives contribute to superior firm performance, other kinds of empirical investigations are scarce, and more to the point, most of them are based on perceptions of survey participants embedded in the firms being studied. Moreover, studies analyzing the question of whether superior KM performance can predict superior market-based valuation appear to be virtually nonexistent. Findings of such studies would be of value to those who champion and direct a firm’s KM efforts, and to the firm’s strategists, planners, and operational managers. Here, we empirically examine the relationship between KM performance and firm valuation; the former is assessed by international panels of independent KM experts and the latter is evaluated in terms of market-based measures. Based on data spanning eight years, the results show that superior KM performance has a statistically significant positive association with firm valuation in terms of Tobin’s q, price-to-book ratio, and price-to-sales ratio. This study contributes to the management literature by using independent expert judges and archival data to substantiate the notion that KM competencies are an important ingredient in a firm’s performance as indicated by market-based valuation.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128419073","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}
D. Derrick, Thomas O. Meservy, Jeffrey L. Jenkins, J. Burgoon, J. Nunamaker
Computer-mediated deception is prevalent and may have serious consequences for individuals, organizations, and society. This article investigates several metrics as predictors of deception in synchronous chat-based environments, where participants must often spontaneously formulate deceptive responses. Based on cognitive load theory, we hypothesize that deception influences response time, word count, lexical diversity, and the number of times a chat message is edited. Using a custom chatbot to conduct interviews in an experiment, we collected 1,572 deceitful and 1,590 truthful chat-based responses. The results of the experiment confirm that deception is positively correlated with response time and the number of edits and negatively correlated to word count. Contrary to our prediction, we found that deception is not significantly correlated with lexical diversity. Furthermore, the age of the participant moderates the influence of deception on response time. Our results have implications for understanding deceit in chat-based communication and building deception-detection decision aids in chat-based systems.
{"title":"Detecting Deceptive Chat-Based Communication Using Typing Behavior and Message Cues","authors":"D. Derrick, Thomas O. Meservy, Jeffrey L. Jenkins, J. Burgoon, J. Nunamaker","doi":"10.1145/2499962.2499967","DOIUrl":"https://doi.org/10.1145/2499962.2499967","url":null,"abstract":"Computer-mediated deception is prevalent and may have serious consequences for individuals, organizations, and society. This article investigates several metrics as predictors of deception in synchronous chat-based environments, where participants must often spontaneously formulate deceptive responses. Based on cognitive load theory, we hypothesize that deception influences response time, word count, lexical diversity, and the number of times a chat message is edited. Using a custom chatbot to conduct interviews in an experiment, we collected 1,572 deceitful and 1,590 truthful chat-based responses. The results of the experiment confirm that deception is positively correlated with response time and the number of edits and negatively correlated to word count. Contrary to our prediction, we found that deception is not significantly correlated with lexical diversity. Furthermore, the age of the participant moderates the influence of deception on response time. Our results have implications for understanding deceit in chat-based communication and building deception-detection decision aids in chat-based systems.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114545740","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}
Trust has emerged as a major impediment to the success of electronic markets and communities where interaction with the strangers is the norm. Social Networks and Online Communities enable interaction with complete strangers, and open up new commercial, political, and social possibilities. But those promises are rarely achieved because it is difficult to trust the online contacts. A common approach to remedy this problem is to compute trust values for the new contacts from the existing trust values in the network. There are two main methods: aggregation and transitivity. Yet, neither method provides satisfactory results because trust networks are sparse and transitivity may not hold. This article develops a Bayesian formulation of the problem, where trust is defined as a conditional probability, and a Bayesian Network analysis is employed to compute the unknown trust values in terms of the known trust values. The algorithms used to propagate conditional probabilities through the network are theoretically sound and based on a long-standing literature on probability propagation in Bayesian networks. Moreover, the context information that is typically ignored in trust literature is included here as a major factor in computing new trust values. These changes have led to significant improvements over existing approaches in the accuracy of computed trust, and with some modifications to the algorithm, in its reach. Real data acquired from Advogato network is used to do extensive testing, and the results confirm the practical value of a theoretically sound Bayesian approach.
{"title":"Bayesian Inference in Trust Networks","authors":"L. Orman","doi":"10.1145/2489790","DOIUrl":"https://doi.org/10.1145/2489790","url":null,"abstract":"Trust has emerged as a major impediment to the success of electronic markets and communities where interaction with the strangers is the norm. Social Networks and Online Communities enable interaction with complete strangers, and open up new commercial, political, and social possibilities. But those promises are rarely achieved because it is difficult to trust the online contacts. A common approach to remedy this problem is to compute trust values for the new contacts from the existing trust values in the network. There are two main methods: aggregation and transitivity. Yet, neither method provides satisfactory results because trust networks are sparse and transitivity may not hold. This article develops a Bayesian formulation of the problem, where trust is defined as a conditional probability, and a Bayesian Network analysis is employed to compute the unknown trust values in terms of the known trust values. The algorithms used to propagate conditional probabilities through the network are theoretically sound and based on a long-standing literature on probability propagation in Bayesian networks. Moreover, the context information that is typically ignored in trust literature is included here as a major factor in computing new trust values. These changes have led to significant improvements over existing approaches in the accuracy of computed trust, and with some modifications to the algorithm, in its reach. Real data acquired from Advogato network is used to do extensive testing, and the results confirm the practical value of a theoretically sound Bayesian approach.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116689844","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}
Zhu Zhang, D. Zeng, A. Abbasi, Jing Peng, Xiaolong Zheng
Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users’ preferences on an item similarity graph and spreading items’ influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation.
{"title":"A Random Walk Model for Item Recommendation in Social Tagging Systems","authors":"Zhu Zhang, D. Zeng, A. Abbasi, Jing Peng, Xiaolong Zheng","doi":"10.1145/2490860","DOIUrl":"https://doi.org/10.1145/2490860","url":null,"abstract":"Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users’ preferences on an item similarity graph and spreading items’ influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114850543","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}
With firms facing intense rivalry, globalization, and time-to-market pressures, the need for organizational agility assumes greater importance. One of the primary vehicles for achieving organizational agility is the use of agile information systems [IS] and the close alignment of information technologies [IT] with business. However, IS is often viewed as an impediment to organization agility. Recently, service-oriented architecture [SOA] has emerged as a prominent IS agility-enhancing technology. The fundamental question of how SOA can enhance organization agility and foster closer alignment between IT and business has not been adequately addressed. The dynamic interaction among external business environmental factors, organizational agility, and IS architecture makes the process of keeping IT and business aligned more complex. This study uses a design science approach to build a system dynamics model to examine the effect of employing alternative SOA implementation strategies in various organizational and external business environments on the IT business alignment and IS cost. The results provide insights into the shaping of IT-business alignment. Additionally, the system dynamics model serves as a tool for supporting managerial decisions related to SOA implementation.
{"title":"The Impact of SOA Implementation on IT-Business Alignment: A System Dynamics Approach","authors":"Jae Choi, D. Nazareth, H. Jain","doi":"10.1145/2445560.2445563","DOIUrl":"https://doi.org/10.1145/2445560.2445563","url":null,"abstract":"With firms facing intense rivalry, globalization, and time-to-market pressures, the need for organizational agility assumes greater importance. One of the primary vehicles for achieving organizational agility is the use of agile information systems [IS] and the close alignment of information technologies [IT] with business. However, IS is often viewed as an impediment to organization agility. Recently, service-oriented architecture [SOA] has emerged as a prominent IS agility-enhancing technology. The fundamental question of how SOA can enhance organization agility and foster closer alignment between IT and business has not been adequately addressed. The dynamic interaction among external business environmental factors, organizational agility, and IS architecture makes the process of keeping IT and business aligned more complex. This study uses a design science approach to build a system dynamics model to examine the effect of employing alternative SOA implementation strategies in various organizational and external business environments on the IT business alignment and IS cost. The results provide insights into the shaping of IT-business alignment. Additionally, the system dynamics model serves as a tool for supporting managerial decisions related to SOA implementation.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126488226","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}
Niam Yaraghi, Anna Ye Du, R. Sharman, R. Gopal, R. Ramesh
The importance of the Healthcare Information Exchange (HIE) in increasing healthcare quality and reducing risks and costs has led to greater interest in identifying factors that enhance adoption and meaningful use of HIE by healthcare providers. In this research we study the interlinked network effects between two different groups of physicians -- primary care physicians and specialists -- as significant factors in increasing the growth of each group in an exchange. An analytical model of interlinked and intragroup influences on adoption is developed using the Bass diffusion model as a basis. Adoption data on 1,060 different primary and secondary care physicians over 32 consecutive months was used to test the model. The results indicate not only the presence of interlinked effects, but also that their influence is stronger than that of the intragroup. Further, the influence of primary care physicians on specialists is stronger than that of specialists on primary care physicians. We also provide statistical evidence that the new model performs better than the conventional Bass model, and the assumptions of diffusion symmetry in the market are statistically valid. Together, the findings provide important guidelines on triggers that enhance the overall growth of HIE and potential marketing strategies for HIE services.
{"title":"Network Effects in Health Information Exchange Growth","authors":"Niam Yaraghi, Anna Ye Du, R. Sharman, R. Gopal, R. Ramesh","doi":"10.1145/2445560.2445561","DOIUrl":"https://doi.org/10.1145/2445560.2445561","url":null,"abstract":"The importance of the Healthcare Information Exchange (HIE) in increasing healthcare quality and reducing risks and costs has led to greater interest in identifying factors that enhance adoption and meaningful use of HIE by healthcare providers. In this research we study the interlinked network effects between two different groups of physicians -- primary care physicians and specialists -- as significant factors in increasing the growth of each group in an exchange. An analytical model of interlinked and intragroup influences on adoption is developed using the Bass diffusion model as a basis. Adoption data on 1,060 different primary and secondary care physicians over 32 consecutive months was used to test the model. The results indicate not only the presence of interlinked effects, but also that their influence is stronger than that of the intragroup. Further, the influence of primary care physicians on specialists is stronger than that of specialists on primary care physicians. We also provide statistical evidence that the new model performs better than the conventional Bass model, and the assumptions of diffusion symmetry in the market are statistically valid. Together, the findings provide important guidelines on triggers that enhance the overall growth of HIE and potential marketing strategies for HIE services.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116237681","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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