Measuring the impact of factors affecting reverse e-logistics' performance in the electronic industry in Lebanon and Syria

Main Article Content

Mohamad AL Majzoub
Vida Davidavičienė
Ieva Meidute-Kavaliauskiene

Abstract

Reverse e-logistics has proven to have high significance in terms of profits, customer satisfaction, competition, and performance’s efficiency. However, several firms in the Business-to-Consumer (B2C) e-commerce field, especially in developing countries such as those in the Middle East, still neglect its importance for the survival of the firm because they don’t know how to improve reverse e-logistics (REL) performance. Therefore, the objective of this article is to identify the main factors that impact reverse e-logistics’ performance and to analyze their effect. The methods used in this article are: scientific literature review, synthesis, questionnaires, and structural equation modelling. The study is done in Lebanon and Syria with a sample of 459 companies in the electronic industry who are engaged in B2C e-commerce and is faced with reverse e-logistics’ challenges. The estimated results prove the significant impact of the identified factors: customer satisfaction, guarantee, and organization structure on reverse e-logistics’ performance, which in turn has a significant impact on the efficiency of the performance of B2C companies engaged in reverse e-logistics activities as well.

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Author Biographies

Mohamad AL Majzoub, Vilnius Gediminas Technical University

Business management faculty, Department of Business Technologies and Enterpreneurship

Vida Davidavičienė, Vilnius Gediminas Technical University

Prof., Business management faculty, Department of Business Technologies and Enterpreneurship

Ieva Meidute-Kavaliauskiene, Vilnius Gediminas Technical University

Prof. Business management faculty, Department of Business Technologies and Enterpreneurship 

References

AGRAWAL, S.; SINGH, R. K.; MURTAZA, Q. (2016) Triple bottom line performance evaluation of reverse logistics. Competitiveness Review, v. 26, n. 3, p. 289–310. DOI: 10.1108/CR-04-2015-0029.

AL MAJZOUB, M.; DAVIDAVIČIENĖ, V. (2019) Comparative analysis of reverse e-logistics’ solution in Asia and Europe. In: International Scientific Conference On Contemporary Issues In Business, Management And Education, Proceedings, https://doi.org/10.3846/cibmee.2019.091, Vilnius: CIBMEE, 2019.

ANG, A.; TAN, A. (2018) Designing reverse logistics network in an Omni-channel environment in Asia, Logforum, v. 14, n. 4, p. 519–533. DOI: 10.17270/J.LOG.2018.307

ASIAN, S.; POOL, J.; NAZARPOUR, A.; TABAEEIAN, R. (2019) On the importance of service performance and customer satisfaction in third-party logistics selection: An application of Kano model. Benchmarking: An International Journal, v. 26, n. 5, p. 1550-1564. DOI: 10.1108/BIJ-05-2018-0121

BAI, C.; SARKIS, J. (2019) Integrating and extending data and decision tools for sustainable third-party reverse logistics provider selection. Computers and Operations Research, v. 110, p. 188–207. DOI: 10.1016/j.cor.2018.06.005.

BAL, A.; SATOGLU, S. I. (2018) A goal programming model for sustainable reverse logistics operations planning and an application. Journal of Cleaner Production, v. 201, p. 1081–1091. DOI: 10.1016/j.jclepro.2018.08.104 .

BARROSO, R. M. R.; FERREIRA, F. A. F.; MEIDUTĖ-KAVALIAUSKIENĖ, I.; BANAITIENĖ, N.; FALCÃO, P. F.; ROSA, Á. A. (2019) Analyzing the determinants of e-commerce in small and medium-sized enterprises: a cognition driven framework. Technological and economic development of economy, v. 25, n. 3, p. 496-518. DOI: 10.3846/tede.2019.9386.

BOGATAJ, M.; GRUBBSTRÖM, R. W. (2013) Transportation delays in reverse logistics. International Journal of Production Economics, v. 143, n. 2, p. 395–402. DOI: 10.1016/j.ijpe.2011.12.007.

BOUZON, M.; SPRICIGO, R.; RODRIGUEZ, C. M. T.; DE QUEIROZ, A. A.; CAUCHICK, M. P. A. (2015) Reverse logistics drivers: Empirical evidence from a case study in an emerging economy. Production Planning and Control, v. 26, n. 16, p. 1368–1385. DOI: 10.1080/09537287.2015.1049239.

CANNELLA, S.; BRUCCOLERI, M.; FRAMINAN, J. M. (2016) Closed-loop supply chains: What reverse logistics factors influence performance? International Journal of Production Economics, v. 175, p. 35–49. DOI: 10.1016/j.ijpe.2016.01.012

CHINDA T. (2017) Examination of Factors Influencing the Successful Implementation of Reverse Logistics in the Construction Industry: Pilot Study. Procedia Engineering, v. 182, p. 99–105. Available: https://www.sciencedirect.com/science. Access: 20th January, 2020. DOI: 10.1016/j.proeng.2017.03.128 .

CHOI, Y.; MAI, D. Q. (2018) The sustainable role of the e-trust in the B2C e- commerce of Vietnam, Sustainability, v.10, n.1, p. 1-18. DOI: 10.3390/su10010291.

COOPER, A. L.; HUSCROFT, J. R.; OVERSTREET, R. E.; HAZEN, B. T.(2016) Knowledge management for logistics service providers: The role of learning culture. Industrial Management and Data Systems, v. 116, n. 3, p. 584–602. DOI: 10.1108/IMDS-06-2015-0262.

DA SILVEIRA GUIMARÃES, J. L.; SALOMON, V. A. P. (2015) ANP applied to the evaluation of performance indicators of reverse logistics in footwear industry. Procedia Computer Science, v. 55, p. 139–148. DOI: 10.1016/j.procs.2015.07.021.

DAUGHERTY, P.; BOLUMOLE, Y.; GRAWE, S. (2019) The new age of customer impatience: An agenda for reawakening logistics customer service research. International Journal of Physical Distribution & Logistics Management, v. 49, n. 1, p. 4-32. DOI: 10.1108/IJPDLM-03-2018-0143

DAVIDAVIČIENĖ, V.; MEIDUTĖ-KAVALIAUSKIENĖ, I.; PALIULIS, R. (2019) Research on the influence of social media on generation Y consumer purchase decisions. Marketing and management of innovations, n. 4, p. 39-49. DOI: 10.21272/mmi.2019.4-04.

DAVIDAVIČIENĖ, V.; PABEDINSKAITE, A.; DAVIDAVIČIUS, S. (2017) Social networks in B2B and b2c communication. Transformations in Business and Economics, v. 16, n. 1, p. 69-84.

EUCHI, J.; BOUZIDI, D.; BOUZID, Z. (2019) Structural analysis of acute success factors of performance of reverse logistics relative to customer satisfaction, International Journal of Combinatorial Optimization Problems and Informatics, v. 10, n. 2, p. 39–56.

GAMBOA, A. C. D.; RIVEROS, B. F. A. (2019) Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics. Inge Cuc, v. 14, n. 8, p. 137–146. DOI: 10.17981/ingecuc.14.2.2018.13

GOVINDAN, K.; BOUZON, M. (2018) From a literature review to a multi-perspective framework for reverse logistics barriers and drivers. Journal of Cleaner Production, v. 187, p. 318–337. DOI: 10.1016/j.jclepro.2018.03.040 .

HAN, H.; TRIMI, S. (2018) A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms. Expert Systems with Applications, v. 103, p. 133–145. DOI: 10.1016/j.eswa.2018.03.003 .

HUANG, Y. C.; RAHMAN, S.; WU, Y. C. J.; HUANG, C. J. (2015) Salient task environment, reverse logistics and performance. International Journal of Physical Distribution and Logistics Management, v. 45, n. 9, p. 979–1006. DOI: 10.1108/IJPDLM-08-2014-0182.

JIANU, I.; JIANU, I.; TURLEA, C. (2017) Measuring the company’s real performance by physical capital maintenance. Economic Computation and Economic Cybernetics Studies and Research, v. 51, n. 1, p. 37–57.

KAZEMI, N.; MODAK, N. M.; GOVINDAN, K. (2018) A review of reverse logistics and closed loop supply chain management studies published in IJPR: a bibliometric and content analysis. International Journal of Production Research, v. 1, n. 24, p. 4937-4960. DOI: 10.1080/00207543.2018.1471244.

KHAN, S.; KHAN, F.; ZHANG, B. (2012) Reverse e-Logistics for SMEs in Pakistan. In: WU Y. (eds) Software Engineering and Knowledge Engineering: Theory and Practice. Advances in Intelligent and Soft Computing, v. 115, Springer, Berlin, Heidelberg.

LARSEN, S.; MASI, D.; FEIBERT, D.; JACOBSEN, P. (2018) How the reverse supply chain impacts the firm’s financial performance: A manufacturer’s perspective. International Journal of Physical Distribution & Logistics Management, v. 48, n. 3, p. 284-307. DOI:10.1108/IJPDLM-01-2017-0031.

LI, Y. L.; YING, C. S.; CHIN, K. S.; YANG, H. T.; XU, J. (2018) Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production, v. 195, p. 573–584. DOI: 10.1016/j.jclepro.2018.05.213 .

LINTON, J. D.; KLASSEN, R. D.; JAYARAMAN, V. (2007) Sustainable supply chains: an introduction. Journal of Operations Management, v. 25, n. 6, p. 1075–1082. DOI: 10.1016/j.jom.2007.01.012.

MAHINDROO, A.; SAMALIA, H. V.; VERMA, P. (2018) Moderated influence of return frequency and resource commitment on information systems and reverse logistics strategic performance. International Journal of Productivity and Performance Management, v. 67, n. 3, p. 550–570. DOI: 10.1108/IJPPM-05-2016-0101

MERKEVIČIUS, J.; DAVIDAVIČIENĖ, V.; RAUDELIUNIENE, J.; BULECA, J. (2015) Virtual organization: Specifics of creation of personnel management system. E+M Ekonomie a Management, v. 18, n. 4, p. 200-211. DOI: 10.15240/tul/001/2015-4-014.

MORGAN, T. R.; TOKMAN, M.; RICHEY, R .G. (2018) Resource commitment and sustainability: a reverse logistics performance process model. International Journal of Physical Distribution & Logistics Management, v. 48, n. 2, p. 164–182. DOI: 10.1108/IJPDLM-02-2017-0068

OCHOCKA, J. (2019) Mobile technologies in logistic customer service as a tool for winning customers’ satisfaction. Scientific Journal of Logistics, v. 15, n. 3, p. 403–411. DOI: 10.17270/J.LOG.2019.338

PANDIAN, G. R. S.; ABDUL-KADER, W. (2017) Performance evaluation of reverse logistics enterprise–an agent-based simulation approach. International Journal of Sustainable Engineering, v. 10, n. 6, p. 384–398. DOI: 10.1080/19397038.2017.1370032.

PANIGRAHI, S. K.; KAR, F. W.; FEN, T. A.; HOE, L. K.; WONG, M. (2018) A Strategic Initiative for Successful Reverse Logistics Management in Retail Industry. Global Business Review, v. 19, n. 3, p. 151–175. DOI: 10.1177/0972150918758096

PRAKASH, C.; BARUA, M. K. (2015) Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. Journal of Manufacturing Systems, v. 37, p. 599–615. DOI: 10.1016/j.jmsy.2015.03.001.

RACHIH, H.; MHADA, F. Z.; CHIHEB, R. (2019) Meta-heuristics for reverse logistics: A literature review and perspectives. Computers and Industrial Engineering, v. 27, p. 45–62. DOI: 10.1016/j.cie.2018.11.058

ROGER, D.; TIBBEN-LEMBKE, R. (1998) Going backwards: Reverse logistics trends and practices. Reno, Nevada: Reverse Logistics Executive Council.

SANGWAN, K. S. (2017) Key Activities, Decision Variables and Performance Indicators of Reverse Logistics. Procedia CIRP, v. 6, n. 1, p. 257–262. Available: https://www.sciencedirect.com/science. Access: 19th January, 2020. DOI:10.1016/j.procir.2016.11.185

SHAIK, M. N.; ABDUL-KADER, W. (2018) A hybrid multiple criteria decision making approach for measuring comprehensive performance of reverse logistics enterprises. Computers and Industrial Engineering, v. 123, p. 9–25. DOI: 10.1016/j.cie.2018.06.007

SIRISAWAT, P.; KIATCHAROENPOL, T. (2016) Correlation of barriers to reverse logistics performance using structural equation modeling. In: IEEE International Conference on Industrial Engineering and Engineering Management, Proceedings, https://doi.org/10.1109/IEEM.2016.7797853, Bali: IEEE, 2016.

SKITSKO, V. I. (2016) E-logistics and m-logistics in information economy. Logforum, v. 12, n. 1, p. 7–16. DOI: 10.17270/J.LOG.2016.1.1

SREMAC, S.; STEVIĆ, Ž.; PAMUČAR, D.; ARSIĆ, M.; MATIĆ, B. (2018) Evaluation of a third-party logistics (3PL) provider using a rough SWARA-WASPAS model based on a new rough dombi aggregator. Symmetry, v. 10, n. 8, p. 1–25. DOI: 10.3390/sym10080305.

YOGI, K. (2015) Performance evaluation of reverse logistics: A case of LPG agency. Cogent Business and Management, v. 2, n. 1, p. 1–17. DOI: 10.1080/23311975.2015.1063229

SUDARTO, S.; TAKAHASHI, K.; MORIKAWA, K. (2017) Efficient flexible long-term capacity planning for optimal sustainability dimensions performance of reverse logistics social responsibility: A system dynamics approach. International Journal of Production Economics, v. 190, p. 45–59. DOI: 10.1016/j.ijpe.2017.06.017

TOSARKANI, B. M.; AMIN, S. H. (2018) A multi-objective model to configure an electronic reverse logistics network and third party selection, Journal of Cleaner Production, v. 198, p. 662–682. DOI: 10.1016/j.jclepro.2018.07.056

VLACHOS, I. P. (2016) Reverse logistics capabilities and firm performance: the mediating role of business strategy. International Journal of Logistics Research and Applications, v. 19, n. 5, p. 424–442. DOI: 10.1080/13675567.2015.1115471

WANG, F.; YU, Y.; WANG, X.; REN, H.; SHAFIE-KHAH, M.; CATALÃO, J. P. S. (2018) Residential electricity consumption level impact factor analysis based on wrapper feature selection and multinomial logistic regression. Energies, v. 11, n. 5, p. 1–26. DOI: 10.3390/en11051180.

WANG, H.; JIANG, Z.; ZHANG, H.; WANG, Y.; YANG, Y.; LI, Y. (2019) An integrated MCDM approach considering demands-matching for reverse logistics. Journal of Cleaner Production, v. 208, p. 199–210. DOI: 10.1016/j.jclepro.2018.10.131 .

WAQAS, M.; DONG, Q.-L.; AHMAD, N.; ZHU, Y.; NADEEM, M. (2018) Critical barriers to implementation of reverse logistics in the manufacturing industry: A case study of a developing country. Sustainability, v. 10, n. 11, p. 1–25. DOI: 10.3390/su10114202

WU, Y. C.; GOH, M.; YUAN, C. H.; HUANG, S. H. (2017) Logistics management research collaboration in Asia. International Journal of Logistics Management, v. 28, n. 1, p. 206–223. DOI: 10.1108/IJLM-09-2013-0104

XU, Y.; ZHANG, X.; CAO, J.; CHEN, Y.; YE, X. (2016) Collaboration and Evolution of E-Commerce and Express Delivery Industry Supply Chain. Discrete Dynamics in Nature and Society, p. 1-12, DOI: 10.1155/2016/3452037.

YADAV, D. K.; BARVE, A. (2015) Analysis of critical success factors of humanitarian supply chain: An application of Interpretive Structural Modeling. International Journal of Disaster Risk Reduction, v. 12, p. 213–225. DOI: 10.1016/j.ijdrr.2015.01.008.