Journal articles: 'Information-measurement systems'

1

DeLone, William H., and Ephraim R. McLean. "Information Systems Success Measurement." Foundations and Trends® in Information Systems 2, no. 1 (2016): 1–116. http://dx.doi.org/10.1561/2900000005.

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Brace, G., A. L. Dowd, and P. N. Johnson. "Measurement of Information Systems Integration." IFAC Proceedings Volumes 23, no. 3 (September 1990): 97–102. http://dx.doi.org/10.1016/s1474-6670(17)52541-0.

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Володарський, Є. Т., М. В. Добролюбова, and Л. О. Кошева. "INFORMATION AND MEASUREMENT SYSTEMS AND UNCERTAINTY." Ukrainian Metrological Journal, no. 3A (November 30, 2020): 30–35. http://dx.doi.org/10.24027/2306-7039.3a.2020.217653.

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4

Scott, Judy E. "The measurement of information systems effectiveness." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 26, no. 1 (February 1995): 43–61. http://dx.doi.org/10.1145/206476.206484.

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Kesharwani, Ankit, Venkatesh Mani, Jighyasu Gaur, Jighyasu Gaur, Samuel Fosso Wamba, and Sachin S. Kamble. "Service Quality Measurement in Information Systems." Journal of Global Information Management 29, no. 6 (November 2021): 1–19. http://dx.doi.org/10.4018/jgim.20211101.oa30.

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Traditionally measurements of service quality have followed the expectation-disconfirmation approach. Further, previous studies have shown that negative disconfirmation is more influential than positive disconfirmation. In this research, we hypothesized information systems(IS) service quality scales based on the dimensionality of the expectation-disconfirmation (ED) and desire-disconfirmation (DD) approach. Using the SERVQUAL+ instrument and data collected from 321 IS users, we developed ED and DD based IS service quality scales using contemporary methods, such as LISREL-based CFA. We have proposed and empirically validated the following two new IS service quality constructs: Service Adequacy (difference of expected service and perceived service) and Service Superiority (difference of desired service and perceived service). Our results indicate that both measures have shown better predictive power than earlier scales like SERVQUAL+ and the IS ZOT scales. We have outlined several implications of ED and DD scales to practice and research.

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Zevelev, Yu A., E. A. Aksenov, and O. B. Lipovskii. "Electronic systems for processing measurement information using unified systems." Measurement Techniques 30, no. 11 (November 1987): 1052–54. http://dx.doi.org/10.1007/bf00865052.

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Chen, Mocker, Preston, and Teubner. "Information Systems Strategy: Reconceptualization, Measurement, and Implications." MIS Quarterly 34, no. 2 (2010): 233. http://dx.doi.org/10.2307/20721426.

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Malykhina, G. F., A. N. Vasilyev, and D. A. Tarkhov. "Information measurement systems in the digital society." Journal of Physics: Conference Series 1379 (November 2019): 012037. http://dx.doi.org/10.1088/1742-6596/1379/1/012037.

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Krivotsyuk, V. I., A. A. Matveev, and V. N. Popov. "Adaptive digital filters in information measurement systems." Measurement Techniques 30, no. 1 (January 1987): 5–8. http://dx.doi.org/10.1007/bf00865713.

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Dai, Jianhua, Wentao Wang, and Ju-Sheng Mi. "Uncertainty measurement for interval-valued information systems." Information Sciences 251 (December 2013): 63–78. http://dx.doi.org/10.1016/j.ins.2013.06.047.

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Webler, Forrest, and Marilyne Andersen. "Measurement in the Age of Information." Information 13, no. 3 (February 25, 2022): 111. http://dx.doi.org/10.3390/info13030111.

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Information is the resolution of uncertainty and manifests itself as patterns. Although complex, most observable phenomena are not random and instead are associated with deterministic, chaotic systems. The underlying patterns and symmetries expressed from these phenomena determine their information content and compressibility. While some patterns, such as the existence of Fourier modes, are easy to extract, advances in machine learning have enabled more comprehensive methods in feature extraction, most notably in their ability to elicit non-linear relationships. Herein we review methods concerned with the encoding and reconstruction of natural signals and how they might inform the discovery of useful transform bases. Additionally, we illustrate the efficacy of data-driven bases over generic ones in encoding information whilst discussing these developments in the context of “fourth paradigm” metrology. Toward this end, we propose that existing metrological standards and norms may need to be redefined within the context of a data-rich world.

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Cenfetelli and Bassellier. "Interpretation of Formative Measurement in Information Systems Research." MIS Quarterly 33, no. 4 (2009): 689. http://dx.doi.org/10.2307/20650323.

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Saitta, Sandro, Benny Raphael, and Ian F. C. Smith. "Rational Design of Measurement Systems using Information Science." IABSE Symposium Report 92, no. 25 (January 1, 2006): 37–44. http://dx.doi.org/10.2749/222137806796169245.

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Melnyk, H. "MODEL OF INFORMATION RISK MEASUREMENT IN CORPORATE SYSTEMS." Bulletin of Taras Shevchenko National University of Kyiv Economics, no. 171 (2015): 48–54. http://dx.doi.org/10.17721/1728-2667.2015/171-6/9.

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15

Bogatenkov, Sergei. "Organizational improvement under implementation of information measurement systems." Energy-Safety and Energy-Economy 4 (August 2017): 58–60. http://dx.doi.org/10.18635/2071-2219-2017-4-58-60.

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Wakid, Shukri A., and Shirley M. Radack. "Measurement-based standards for future information technology systems." StandardView 5, no. 1 (March 1997): 36–40. http://dx.doi.org/10.1145/253452.253479.

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17

Akma Mohd Salleh, Noor, Ruzita Jusoh, and Che Ruhana Isa. "Relationship between information systems sophistication and performance measurement." Industrial Management & Data Systems 110, no. 7 (August 24, 2010): 993–1017. http://dx.doi.org/10.1108/02635571011069077.

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18

Elg, Mattias, and Anders Fundin. "Quality assurance of information in performance measurement systems." International Journal of Productivity and Quality Management 10, no. 4 (2012): 514. http://dx.doi.org/10.1504/ijpqm.2012.049637.

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19

Petrin, A. A. "Virtual sensors for use in information measurement systems." Measurement Techniques 53, no. 9 (November 27, 2010): 964–69. http://dx.doi.org/10.1007/s11018-010-9605-8.

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20

Udovichenko, E. T., and A. D. Pinchevskii. "Methodology of metrological support of information measurement systems." Measurement Techniques 32, no. 11 (November 1989): 1035–37. http://dx.doi.org/10.1007/bf02159452.

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21

Sethi, Vijay, and William R. King. "Construct Measurement in Information Systems Research: An Illustration in Strategic Systems." Decision Sciences 22, no. 3 (July 1991): 455–72. http://dx.doi.org/10.1111/j.1540-5915.1991.tb01274.x.

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22

Ermishin, S. M. "Determination of the computational component of measurement error in measurement information systems." Measurement Techniques 39, no. 12 (December 1996): 1170–73. http://dx.doi.org/10.1007/bf02375398.

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23

Wu, Qihui, Lizhen Chen, Zheng Wang, Guoru Ding, Linyuan Zhang, and Xiaofei Zhang. "Information Measurement of Cognitive Communication Systems: The Introduction of Negative Cognitive Information." IEEE Access 6 (2018): 34288–95. http://dx.doi.org/10.1109/access.2018.2842245.

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24

Tarkivsky, V. E., N. V. Trubitsyn, and E. S. Voronin. "Software for Measurement Information Systems for Agricultural Machinery Testing." Machinery and Equipment for Rural Area, no. 9 (September 25, 2019): 12–15. http://dx.doi.org/10.33267/2072-9642-2019-9-12-15.

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25

Bogatenkov, Sergei. "Information and measurement systems implementation. Planning individual career trajectories." Energy Safety and Energy Economy 1 (February 2018): 39–42. http://dx.doi.org/10.18635/2071-2219-2018-1-39-42.

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26

Farr, William H. "Getting Acquainted with the Measurement Information Systems Tool (MIST)." INSIGHT 1, no. 4 (December 1998): 18–23. http://dx.doi.org/10.1002/inst.19981418.

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27

Dominick, Wayne D. "A performance measurement and evaluation environment for information systems." Information Processing & Management 23, no. 1 (January 1987): 7–15. http://dx.doi.org/10.1016/0306-4573(87)90034-3.

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28

Ding, Feng. "Hierarchical estimation algorithms for multivariable systems using measurement information." Information Sciences 277 (September 2014): 396–405. http://dx.doi.org/10.1016/j.ins.2014.02.103.

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29

Veselov, O. V., N. A. Kononykhina, and V. M. Perepelkin. "An Information Measurement System for Diagnostics of Electromechanical Systems." Measurement Techniques 60, no. 6 (September 2017): 538–45. http://dx.doi.org/10.1007/s11018-017-1231-2.

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30

Udovichenko, E. T. "Metrological support for measurement information systems and process control." Measurement Techniques 39, no. 9 (September 1996): 914–19. http://dx.doi.org/10.1007/bf02369840.

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31

Belen'kii, Ya E., A. M. Krichevets, and A. L. Semenyuk. "Metrological characteristics of computing programs of information measurement systems." Measurement Techniques 32, no. 11 (November 1989): 1038–40. http://dx.doi.org/10.1007/bf02159453.

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32

Delen, G. P. A. J., and D. B. B. Rijsenbrij. "The specification, engineering, and measurement of information systems quality." Journal of Systems and Software 17, no. 3 (March 1992): 205–17. http://dx.doi.org/10.1016/0164-1212(92)90109-w.

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33

Yesmagambetov, B. B. S. "NOISE IMMUNITY IN DATA COMPRESSION INFORMATION AND MEASUREMENT SYSTEMS." Bulletin Series of Physics & Mathematical Sciences 75, no. 3 (September 15, 2021): 100–107. http://dx.doi.org/10.51889/2021-3.1728-7901.12.

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When processing huge data streams in information systems, individual measurements or whole groups of measurements can be distorted or lost due to various reasons. Recovery of compressed data during transmission on communication channels is accompanied by errors related to distortion of information and service parts of messages due to presence of interference in transmission channel. To these errors are added errors caused by quantization of the transmitted implementations by level and time sampling. Research on methods of increasing noise immunity both during transmission and during recovery of measured data is an urgent task in the design of information and measurement systems. The article considers non-parametric methods of estimating probabilistic characteristics of random processes. A distinctive feature of non-parametric methods is the ranking of data measured at the observation interval. It is shown that ranking of data on transmitting side of information-measuring system enables correction of errors and failures based on strict monotony of ranked number of codes. Also, the error of recovery of continuous implementations taking into account distortions of compressed data in the communication channel was investigated. The obtained results indicate that the use of complex compression algorithms is impractical, since the difference in the error in the restoration of non-stationary messages between the simplest algorithm and the rather difficult one becomes negligible. The article presents the results of estimating recovery errors for various data compression methods.

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34

Bartoněk, Dalibor, and Irena Opatřilová. "The Management of Global Navigation Satellite Systems Measurement via Geographic Information Systems." Advanced Science Letters 21, no. 1 (January 1, 2015): 57–61. http://dx.doi.org/10.1166/asl.2015.5745.

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35

Wang, Hong Wei, Yu Liu, Man Zhou, and Shao Yi He. "DEA-Based Measurement of Information System Performance." Key Engineering Materials 467-469 (February 2011): 597–602. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.597.

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This paper selects YMK-DEA and C2GS2-ISS models for the evaluation of performance of complex information systems. Referring the balanced scorecard, evaluation indexes for IS performance are presented from four perspectives, including finance, customer, internal processes and learning and growth. Taking the information systems from 30 steel sale enterprises as examples, this paper uses YMK-DEA and C2GS2-ISS models to evaluate IS performance from the angles of both technical efficiency and scale efficiency, and compares the results with those from C2R and C2GS2 models. Finally, some suggestions for IS improvement are given.

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36

Moffat, Alistair, Falk Scholer, and Ziying Yang. "Estimating Measurement Uncertainty for Information Retrieval Effectiveness Metrics." Journal of Data and Information Quality 10, no. 3 (October 13, 2018): 1–22. http://dx.doi.org/10.1145/3239572.

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37

Jatmiko, Udik. "Management Accounting Information Systems, Performance Measurement Methods and Reward Systems in Influencing Managerial Performance." International Journal on Social Science, Economics and Art 11, no. 4 (February 27, 2022): 180–89. http://dx.doi.org/10.35335/ijosea.v11i4.67.

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The problem that is the indication of management accounting information system policies, performance measurement methods, and reward systems that are less effective, so that they are felt to affect managerial performance. The number of samples obtained from the total population in as many as 32 people. Data analysis techniques using validity and reliability tests, multiple linear regression tests, t tests and F tests. The results of the research carried out obtained that partially the application of management accounting information systems to managerial performance. The performance measurement method has a positive and significant effect on managerial performance. The reward system has a positive and significant effect on managerial performance. Simultaneously the application of management accounting information systems, performance measurement methods and reward systems have an effect on managerial performance with a significant value of F 0.001 < sig. 0.05. Management should always pay attention to the development of the systems implemented, including the application of management accounting information systems, performance measurement methods and reward systems in order to achieve the effectiveness of managerial performance.

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38

Ragu-Nathan, Bhanu, T. S. Ragu-Nathan, Qiang Tu, and Zhengzhong Shi. "Information management (IM) strategy: the construct and its measurement." Journal of Strategic Information Systems 10, no. 4 (December 2001): 265–89. http://dx.doi.org/10.1016/s0963-8687(01)00053-1.

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39

Kuznetsova, T. A., and P. V. Repp. "THE ROBUST ON-BOARD INFORMATION AND MEASUREMENT CONTROL SYSTEMS’ DEVELOPMENT." Scientific and Technical Volga region Bulletin 6, no. 3 (June 2016): 120–22. http://dx.doi.org/10.24153/2079-5920-2016-6-3-120-122.

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40

Babak, S. "Amorphous Materials for Elastic Sensitive Elements of Information-Measurement Systems." Advanced Science Journal 2015, no. 5 (October 1, 2015): 41–44. http://dx.doi.org/10.15550/asj.2015.05.041.

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41

Argenti, Fiorella, Vieri Benci, Paola Cerrai, Alessandro Cordelli, Stefano Galatolo, and Giulia Menconi. "Information and dynamical systems: a concrete measurement on sporadic dynamics." Chaos, Solitons & Fractals 13, no. 3 (March 2002): 461–69. http://dx.doi.org/10.1016/s0960-0779(01)00028-5.

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42

Taufiq, Muhammad, Nandhini Hudha Anggarasari, and Mirawati. "Measurement of Performance Value for New Student Admission Information Systems." Journal of Physics: Conference Series 1179 (July 2019): 012037. http://dx.doi.org/10.1088/1742-6596/1179/1/012037.

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43

Wang, J. W., F. Gao, and W. H. Ip. "Measurement of resilience and its application to enterprise information systems." Enterprise Information Systems 4, no. 2 (May 2010): 215–23. http://dx.doi.org/10.1080/17517571003754561.

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44

XIA, WEIDONG, and GWANHOO LEE. "Complexity of Information Systems Development Projects: Conceptualization and Measurement Development." Journal of Management Information Systems 22, no. 1 (April 2005): 45–83. http://dx.doi.org/10.1080/07421222.2003.11045831.

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45

Kettinger, William J., and Choong C. Lee. "Pragmatic Perspectives on the Measurement of Information Systems Service Quality." MIS Quarterly 21, no. 2 (June 1997): 223. http://dx.doi.org/10.2307/249421.

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46

Sedera, Darshana, and Sharmistha Dey. "User expertise in contemporary information systems: Conceptualization, measurement and application." Information & Management 50, no. 8 (December 2013): 621–37. http://dx.doi.org/10.1016/j.im.2013.07.004.

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47

Liang, Yi-Hui. "Performance measurement of interorganizational information systems in the supply chain." International Journal of Production Research 53, no. 18 (April 7, 2015): 5484–99. http://dx.doi.org/10.1080/00207543.2015.1026614.

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48

Karabutov, N. N. "Influence of measurement information on the properties of adaptive systems." Measurement Techniques 53, no. 9 (November 25, 2010): 956–63. http://dx.doi.org/10.1007/s11018-010-9604-9.

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49

Kozochkin, M. P., A. R. Maslov, and A. N. Porvatov. "Information-Measurement and Control Systems for Force and Vibroacoustic Parameters." Measurement Techniques 58, no. 8 (November 2015): 839–44. http://dx.doi.org/10.1007/s11018-015-0804-1.

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50

Erokhin, I. V. "Automated information measurement systems in the housing and utilities sector." Measurement Techniques 50, no. 7 (July 2007): 788–94. http://dx.doi.org/10.1007/s11018-007-0150-z.

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Journal articles on the topic 'Information-measurement systems'

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