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Author
Anwar Muhammad Fahad (School of Management, Universiti Sains Malaysia, Penang, Malaysia), Wong Wai Peng (School of Information Technology, Monash University, Malaysia Campus, Subang Jaya, Malaysia), Saad Nor Hasliza (School of Management, Universiti Sains Malaysia, Penang, Malaysia), Mushtaq Naveed (Noon Business School, University of Sargodha, Pakistan.)
Title
Data Analytics and Global Logistics Performance: An Exploratory Study of Informatization in the Logistics Sector
Source
LogForum, 2022, vol. 18, nr 2, s. 137-160, rys., tab., bibliogr. 104 poz.
Keyword
Analiza danych, Logistyka, Informatyzacja
Data analysis, Logistics, Informatization
Note
summ.
Abstract
Background: Informatization has enabled global logistics and supply chains (LSC) to capitalize on data-driven analytics to improve logistics performance. At the country level, logistics performance is gauged through the logistics performance index (LPI), where globally 61.25% or 98 countries perform below the mean LPI score. Previous studies focused on logistics informatization in high and moderate LPI rank economies. The paper aims to conduct an exploratory case study in a low LPI performing country to assess the informatization practices of logistics entities and develop a logistics informatization continuum to unlock data analytics for other countries. Methods: The study implements qualitative methods to develop strategic recommendations to reduce global logistics imbalance. We employ a two-layer methodology consisting of thematic analysis and a novel strategic choice approach (SCA) to involve stakeholders for recommendations on obstruction. For thematic analysis, 16 semi-structured interviews were conducted from logistics companies, also onboard 10 trade associations and government representatives for the SCA analysis. Results: We observed many obstructions in informatization; low willingness on informatization, fear of information leakage by humans, low-reciprocity for collaboration, the myth of information and communication technologies (ICT) as an expensive tool, self-interest, and opportunistic behavior. Conclusion: Information-centric and integrated LSC enables data-driven technologies for real-time decision making, vigilance, and data analytics to distinguished the success of a country's logistics performance. Originality: This study explores the informatization conformity in the logistics sector to connect data analytics. We introduced a novel strategic choice approach in the technology domain for problem structuring. The paper further contributes by suggesting a logistics informatization continuum for low LPI countries to straighten digitalization in the logistics sector.(original abstract)
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Bibliography
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  1. Altuntaş Vural, C., Roso, V., Halldórsson, Á., Ståhle, G., & Yaruta, M. (2020). Can digitalization mitigate barriers to intermodal transport? An exploratory study. Research in Transportation Business & Management, 37, 100525. https://doi.org/10.1016/j.rtbm.2020.100525
  2. Bag, S., Gupta, S., & Luo, Z. (2020). Examining the role of logistics 4.0 enabled dynamic capabilities on firm performance. International Journal of Logistics Management, 31(3), 607-628. https://doi.org/10.1108/IJLM-11-2019-0311
  3. Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 implications in logistics: an overview. Procedia Manufacturing, 13, 1245-1252. https://doi.org/10.1016/j.promfg.2017.09.045
  4. Benamati, J. H., Ozdemir, Z. D., & Smith, H. J. (2021). Information Privacy, Cultural Values, and Regulatory Preferences. Journal of Global Information Management, 29(3), 131-164. https://doi.org/10.4018/JGIM.2021050106
  5. Bexelius, A., Carlberg, E. B., & Löwing, K. (2018). Quality of goal setting in pediatric rehabilitation-A SMART approach. Child: Care, Health and Development, 44(6), 850-856. https://doi.org/10.1111/cch.12609
  6. Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member Checking: A Tool to Enhance Trustworthiness or Merely a Nod to Validation? Qualitative Health Research, 26(13), 1802-1811. https://doi.org/10.1177/1049732316654870
  7. Boddy, C. R. (2016). Sample size for qualitative research. Qualitative Market Research: An International Journal, 19(4), 426-432. https://doi.org/10.1108/QMR-06-2016-0053
  8. Bryman, A., & Bell, E. (2009). Business Research Methods (2nd ed). Oxford University Press.
  9. Çelebi, D. (2019). The role of logistics performance in promoting trade. Maritime Economics & Logistics, 21(3), 307-323. https://doi.org/10.1057/s41278-017-0094-4
  10. Chaudhuri, A., Dukovska-Popovska, I., Subramanian, N., Chan, H. K., & Bai, R. (2018). Decision-making in cold chain logistics using data analytics: a literature review. In International Journal of Logistics Management 29(3), 839-861. https://doi.org/10.1108/IJLM-03-2017-0059
  11. Chen, Y.-T., Sun, E. W., Chang, M.-F., & Lin, Y.-B. (2021). Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0. International Journal of Production Economics, 238, 108157. https://doi.org/10.1016/j.ijpe.2021.108157
  12. Creswell, J. (2009). Research design: qualitative, quantitative, and mixed methods approaches (3rd edn). Sage, Thousand Oaks, CA.
  13. Cruz-Jesus, F., Oliveira, T., & Bacao, F. (2018). The Global Digital Divide. Journal of Global Information Management, 26(2), 1-26. https://doi.org/10.4018/JGIM.2018040101
  14. de Sousa Pereira, L., & Costa Morais, D. (2020). The strategic choice approach to the maintenance management of a water distribution system. Urban Water Journal, 17(1), 23-31. https://doi.org/10.1080/1573062X.2020.1734945
  15. Dunke, F., & Nickel, S. (2020). Improving company-wide logistics through collaborative track and trace IT services. International Journal of Logistics Systems and Management, 35(3), 329-353. https://doi.org/10.1504/IJLSM.2020.105916
  16. Dworkin, S. L. (2012). Sample Size Policy for Qualitative Studies Using In-Depth Interviews. Archives of Sexual Behavior, 41(6), 1319-1320. https://doi.org/10.1007/s10508-012-0016-6
  17. Fang, D., & Ren, Q. (2019). Optimal decision in a dual-channel supply chain under potential information leakage. Symmetry, 11(3), 308. https://doi.org/10.3390/sym11030308
  18. Friend, J. (1992). New directions in software for strategic choice. European Journal of Operational Research, 61(1-2), 154-164. https://doi.org/10.1016/0377-2217(92)90277-G
  19. Friend, J. (2011). The Strategic Choice Approach. In Wiley Encyclopedia of Operations Research and Management Science (pp. 121-158). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470400531.eorms0971
  20. Friend, J. K., Norris, M. E., & Stringer, J. (1988). The Institute for Operational Research: An Initiative to Extend the Scope of OR. Journal of the Operational Research Society, 39(8), 705-713. https://doi.org/10.1057/jors.1988.125
  21. Gani, A. (2017). The Logistics Performance Effect in International Trade. Asian Journal of Shipping and Logistics, 33(4), 279-288. https://doi.org/10.1016/j.ajsl.2017.12.012
  22. Gezikol, B., Tunahan, H., & Özsoy, S. (2020). Determinants of Freight Volume and Efficiency in Transportation and Storage Sector. Logforum, 16(3), 385-396. https://doi.org/10.17270/J.LOG.2020.453
  23. Guangwen Kong, Sampath Rajagopalan, Hao Zhang, (2012) Revenue Sharing and Information Leakage in a Supply Chain. Management Science 59(3):556-572. https://doi.org/10.1287/mnsc.1120.1627
  24. Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317. https://doi.org/10.1016/j.jbusres.2016.08.004
  25. Gunes, B., Kayisoglu, G., & Bolat, P. (2021). Cyber security risk assessment for seaports: A case study of a container port. Computers and Security, 103. https://doi.org/10.1016/j.cose.2021.102196
  26. Halaszovich, T. F., & Kinra, A. (2020). The impact of distance, national transportation systems and logistics performance on FDI and international trade patterns: Results from Asian global value chains. Transport Policy, 98, 35-47. https://doi.org/10.1016/j.tranpol.2018.09.003
  27. Hanna, N. K., & Qiang, C. Z. W. (2010). China's Emerging Informatization Strategy. Journal of the Knowledge Economy, 1(2), 128-164. https://doi.org/10.1007/s13132-009-0001-z
  28. Hausman, W. H., Lee, H. L., & Subramanian, U. (2013). The impact of logistics performance on trade. Production and Operations Management, 22(2), 236-252. https://doi.org/10.1111/j.1937-5956.2011.01312.x
  29. Heeks, R., & Renken, J. (2018). Data justice for development: What would it mean? Information Development, 34(1), 90-102. https://doi.org/10.1177/0266666916678282
  30. Hopkins, J., & Hawking, P. (2018). Big Data Analytics and IoT in logistics: a case study. International Journal of Logistics Management, 29(2), 575-591. https://doi.org/10.1108/IJLM-05-2017-0109
  31. Imam Yudhistyra, W., Marta Risal, E., Raungratanaamporn, I., & Ratanavaraha, V. (2020). Exploring Big Data Research: A Review of Published Articles from 2010 to 2018 Related to Logistics and Supply Chains. Operations and Supply Chain Management, 13(2), 134-149. http://doi.org/10.31387/oscm0410258
  32. Jarvenpaa, S. L., & Staples, D. S. (2000). The use of collaborative electronic media for information sharing: an exploratory study of determinants. The Journal of Strategic Information Systems, 9(2-3), 129-154. https://doi.org/10.1016/S0963-8687(00)00042-1
  33. Kabak, Ö., Önsel Ekici, Ş., & Ülengin, F. (2020). Analyzing two-way interaction between the competitiveness and logistics performance of countries. Transport Policy, 98, 238-246. https://doi.org/10.1016/j.tranpol.2019.10.007
  34. Kapkaeva, N., Gurzhiy, A., Maydanova, S., & Levina, A. (2021). Digital Platform for Maritime Port Ecosystem: Port of Hamburg Case. Transportation Research Procedia, 54(2020), 909-917. https://doi.org/10.1016/j.trpro.2021.02.146
  35. Keith, J. E., Lee, D.-J., & Leem, R. G. (2004). The Effect of Relational Exchange Between the Service Provider and the Customer on the Customer's Perception of Value. Journal of Relationship Marketing, 3(1), 3-33. https://doi.org/10.1300/J366v03n01_02
  36. Kembro, J., Näslund, D., & Olhager, J. (2017). Information sharing across multiple supply chain tiers: A Delphi study on antecedents. International Journal of Production Economics, 193, 77-86. https://doi.org/10.1016/j.ijpe.2017.06.032
  37. Kinra, A., Hald, K. S., Mukkamala, R. R., & Vatrapu, R. (2020). An unstructured big data approach for country logistics performance assessment in global supply chains. International Journal of Operations and Production Management, 40(4), 439-458. https://doi.org/10.1108/IJOPM-07-2019-0544
  38. Kirono, I., Armanu, A., Hadiwidjojo, D., & Solimun, S. (2019). Logistics performance collaboration strategy and information sharing with logistics capability as mediator variable (study in Gafeksi East Java Indonesia). International Journal of Quality & Reliability Management, 36(8), 1301-1317. https://doi.org/10.1108/IJQRM-11-2017-0246
  39. Kourtit, K., & Nijkamp, P. (2011). Strategic choice analysis by expert panels for migration impact assessment. International Journal of Business and Globalisation, 7(2), 166. https://doi.org/10.1504/IJBG.2011.041831
  40. Lechler, S., Canzaniello, A., Roßmann, B., von der Gracht, H. A., & Hartmann, E. (2019). Real-time data processing in supply chain management: revealing the uncertainty dilemma. International Journal of Physical Distribution & Logistics Management, 49(10), 1003-1019. https://doi.org/10.1108/IJPDLM-12-2017-0398
  41. Li, W., Ardichvili, A., Maurer, M., Wentling, T., & Stuedemann, R. (2007). Impact of Chinese Culture Values on Knowledge Sharing Through Online Communities of Practice. International Journal of Knowledge Management, 3(3), 46-59. https://doi.org/10.4018/jkm.2007070103
  42. Liu, C., Feng, Y., Lin, D., Wu, L., & Guo, M. (2020). Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques. International Journal of Production Research, 58(17), 5113-5131. https://doi.org/10.1080/00207543.2019.1677961
  43. Liu, H., Jiang, W., Feng, G., & Chin, K. S. (2020). Information leakage and supply chain contracts. Omega, 90, 101994. https://doi.org/10.1016/j.omega.2018.11.003
  44. Liu, C., Zhou, Y., Cen, Y., & Lin, D. (2019). Integrated application in intelligent production and logistics management: technical architectures concepts and business model analyses for the customised facial masks manufacturing. International Journal of Computer Integrated Manufacturing, 32(4-5), 522-532. https://doi.org/10.1080/0951192X.2019.1599434
  45. Long, T., & Johnson, M. (2000). Rigour, reliability and validity in qualitative research. Clinical Effectiveness in Nursing, 4(1), 30-37. https://doi.org/10.1054/cein.2000.0106
  46. Lu, Q., Liu, B., & Song, H. (2020). How can SMEs acquire supply chain financing: the capabilities and information perspective. Industrial Management and Data Systems, 120(4), 784-809. https://doi.org/10.1108/IMDS-02-2019-0072
  47. Lui, S. S., Wong, Y. Y., & Liu, W. (2009). Asset specificity roles in interfirm cooperation: Reducing opportunistic behavior or increasing cooperative behavior?. Journal of Business research, 62(11), 1214-1219. https://doi.org/10.1016/j.jbusres.2008.08.003
  48. Luttermann, S., Kotzab, H., & Halaszovich, T. (2020). The impact of logistics performance on exports, imports and foreign direct investment. World Review of Intermodal Transportation Research, 9(1), 27. https://doi.org/10.1504/WRITR.2020.106444
  49. Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research 59(6), 1875-1900. https://doi.org/10.1080/00207543.2020.1793011
  50. Mangina, E., Narasimhan, P. K., Saffari, M., & Vlachos, I. (2020). Data analytics for sustainable global supply chains. Journal of Cleaner Production, 255, 120300. https://doi.org/10.1016/j.jclepro.2020.120300
  51. Merriam, S. B. (2009). Qualitative Research: A Guide to Design and Implementation.
  52. Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2), 81-97. https://doi.org/10.1037/h0043158
  53. Ministry of Finance. (2020). Transport and Communication. In Economic Survey of Pakistan 2019-20.
  54. Mirzabeiki, V., Roso, V., & Sjöholm, P. (2016). Collaborative tracking and tracing applied on dry ports. International Journal of Logistics Systems and Management, 25(3), 425-440. https://doi.org/10.1504/IJLSM.2016.079834
  55. Moldabekova, A., Philipp, R., Reimers, H. E., & Alikozhayev, B. (2021). Digital Technologies for Improving Logistics Performance of Countries. Transport and Telecommunication, 22(2), 207-216. https://doi.org/10.2478/ttj-2021-0016
  56. Najjar, M. S., Dahabiyeh, L., & Nawayseh, M. (2019). Share if you care: The impact of information sharing and information quality on humanitarian supply chain performance - a social capital perspective. Information Development, 35(3), 467-481. https://doi.org/10.1177/0266666918755427
  57. Önsel Ekici, Ş., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81, 197-207. https://doi.org/10.1016/j.tranpol.2019.06.014
  58. Park, Y.-H., & Jeong, Y.-S. (2016). An empirical analysis on the performance of the third-party logistics in the Korean exporter. Journal of Korea Trade, 20(1), 97-114. https://doi.org/10.1108/JKT-03-2016-006
  59. Peltokorpi, V. (2006). Knowledge sharing in a cross-cultural context: Nordic expatriates in Japan. Knowledge Management Research & Practice, 4(2), 138-148. https://doi.org/10.1057/palgrave.kmrp.8500095
  60. Pfleeger, S. L., & Caputo, D. D. (2012). Leveraging behavioral science to mitigate cyber security risk. Computers and Security, 31(4), 597-611. https://doi.org/10.1016/j.cose.2011.12.010
  61. Pomegbe, W. W. K., Li, W., Dogbe, C. S. K., & Otoo, C. O. A. (2021). Closeness or opportunistic behavior? Mediating the business ecosystem governance mechanisms and coordination relationship. Cross Cultural & Strategic Management 28(3), 530-552. https://doi.org/10.1108/CCSM-01-2020-0013
  62. Rahimi, Y., Matyshenko, I., Kapitan, R., & Pronchakov, Y. (2020). Organization the information support of full logistic supply chains within the industry 4.0. International Journal for Quality Research, 14(4), 1279-1290. https://doi.org/10.24874/IJQR14.04-19
  63. Ramanathan, U., & Ramanathan, R. (2021). Information Sharing and Business Analytics in Global Supply Chains. In International Encyclopedia of Transportation (pp. 71-75). Elsevier. https://doi.org/10.1016/B978-0-08-102671-7.10222-2
  64. Robertson, M., & Swan, J. (2003). "Control - What Control?" Culture and Ambiguity Within a Knowledge Intensive Firm*. Journal of Management Studies, 40(4), 831-858. https://doi.org/10.1111/1467-6486.00362
  65. Rogers, E. M. (2000). Informatization, globalization, and privatization in the new Millenium. Asian Journal of Communication, 10(2), 71-92. https://doi.org/10.1080/01292980009364785
  66. Rouibah, K., Dihani, A., & Al-Qirim, N. (2020). Critical success factors affecting information system satisfaction in public sector organizations: A perspective on the mediating role of information quality. Journal of Global Information Management 28(3), 77-98. https://doi.org/10.4018/JGIM.2020070105
  67. Sahal, R., Breslin, J. G., & Ali, M. I. (2020). Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. Journal of Manufacturing Systems, 54, 138-151. https://doi.org/10.1016/j.jmsy.2019.11.004
  68. Schmidt, L., Falk, T., Siegmund-Schultze, M., & Spangenberg, J. H. (2020). The Objectives of Stakeholder Involvement in Transdisciplinary Research. A Conceptual Framework for a Reflective and Reflexive Practise. Ecological Economics, 176, 106751. https://doi.org/10.1016/j.ecolecon.2020.106751
  69. Schoenherr, T., & Speier-Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120-132. https://doi.org/10.1111/jbl.12082
  70. Senir, G. (2021). Comparison of domestic logistics performances of Turkey and European union countries in 2018 with an integrated model. Logforum, 17(2), 193-204. https://doi.org/10.17270/J.LOG.2021.576
  71. Soh, K. L., Wong, W. P., & Tang, C. F. (2021). The role of institutions at the nexus of logistic performance and foreign direct investment in Asia. The Asian Journal of Shipping and Logistics, 37(2), 165-173. https://doi.org/10.1016/j.ajsl.2021.02.001
  72. Sousa, D. (2014). Validation in Qualitative Research: General Aspects and Specificities of the Descriptive Phenomenological Method. Qualitative Research in Psychology, 11(2), 211-227. https://doi.org/10.1080/14780887.2013.853855
  73. Srinivasan, R., & Swink, M. (2018). An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective. Production and Operations Management, 27(10), 1849-1867. https://doi.org/10.1111/poms.12746
  74. Suarez-Moreno, J. D., Garcia-Castillo, J., Castaneda-Velasquez, A. M., & Cardenas-Hurtado, A. F. (2019). Making horizontal collaboration among shippers feasible through the application of an ITS. 2019 2nd Latin American Conference on Intelligent Transportation Systems (ITS LATAM), 1-6. https://doi.org/10.1109/ITSLATAM.2019.8721342
  75. Tan, K. H., Wong, W. P., & Chung, L. (2016). Information and Knowledge Leakage in Supply Chain. Information Systems Frontiers, 18(3), 621-638. https://doi.org/10.1007/s10796-015-9553-6
  76. The World Bank. (2018a). Connecting to Compete 2018- Trade Logistics in the Global Economy. http://documents1.worldbank.org/curated/en/576061531492034646/pdf/128355-WP-P164390-PUBLIC-LPIfullreportwithcover.pdf
  77. The World Bank. (2018b). LPI Global Rankings 2018. International LPI. https://lpi.worldbank.org/international/global/2018
  78. Todella, E., Lami, I. M., & Armando, A. (2018). Experimental Use of Strategic Choice Approach (SCA) by Individuals as an Architectural Design Tool. Group Decision and Negotiation, 27(5), 811-826. https://doi.org/10.1007/s10726-018-9567-9
  79. Tsai, F.-S., Kuo, C.-C., & Lin, J. L. (2020). Knowledge Heterogenization of the Franchising Literature Applying Transaction Cost Economics. Economies, 8(4), 106. https://doi.org/10.3390/economies8040106
  80. Tushman, M. L., & Nadler, D. A. (1978). Information Processing as an Integrating Concept in Organizational Design . Academy of Management Review, 3(3), 613-624. https://doi.org/10.5465/amr.1978.4305791
  81. Voss, K. E., Johnson, J. L., Cullen, J. B., Sakano, T., & Takenouchi, H. (2006). Relational exchange in US-Japanese marketing strategic alliances. International Marketing Review, 23(6), 610-635. https://doi.org/10.1108/02651330610712139
  82. Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics 176, 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014
  83. Wong, W. P., Sinnandavar, C. M., & Soh, K.-L. (2021). The relationship between supply environment, supply chain integration and operational performance: The role of business process in curbing opportunistic behaviour. International Journal of Production Economics, 232, 107966. https://doi.org/10.1016/j.ijpe.2020.107966
  84. Xu, J., Pero, M. E. P., Ciccullo, F., & Sianesi, A. (2021). On relating big data analytics to supply chain planning: towards a research agenda. International Journal of Physical Distribution & Logistics Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/IJPDLM-04-2020-0129
  85. Yan, Z., Ismail, H., Chen, L., Zhao, X., & Wang, L. (2019). The application of big data analytics in optimizing logistics: a developmental perspective review. Journal of Data, Information and Management, 1(1-2), 33-43. https://doi.org/10.1007/s42488-019-00003-0
  86. Zaheer, N., & Trkman, P. (2017). An information sharing theory perspective on willingness to share information in supply chains. The International Journal of Logistics Management, 28(2), 417-443. https://doi.org/10.1108/IJLM-09-2015-0158
  87. Zhang, D. Y., Cao, X., Wang, L., & Zeng, Y. (2012). Mitigating the risk of information leakage in a two-level supply chain through optimal supplier selection. Journal of Intelligent Manufacturing, 23(4), 1351-1364. https://doi.org/10.1007/s10845-011-0527-3
  88. Zhang, D. Y., Zeng, Y., Wang, L., Li, H., & Geng, Y. (2011). Modeling and evaluating information leakage caused by inferences in supply chains. Computers in Industry, 62(3), 351-363. https://doi.org/10.1016/j.compind.2010.10.002
  89. Zhang, J., Yarom, O. A., & Liu-Henke, X. (2020). Decentralized, Self-optimized Order-acceptance Decision of Autonomous Guided Vehicles in an IoT-based Production Facility. International Journal of Mechanical Engineering and Robotics Research, 10(1), 1-6. https://doi.org/10.18178/ijmerr.10.1.1-6
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ISSN
1895-2038
Language
eng
URI / DOI
http://dx.doi.org/10.17270/J.LOG.2022.664
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