In the dynamic landscape of today's digitized markets, organizations harness the power of vast and swiftly accessible data to glean invaluable insights. A significant portion of this data emanates from user behavior on business websites. Unraveling the intricacies of this user behavior has become paramount for businesses, serving as the compass guiding the adaptation and evolution of their digital marketing strategies. Embarking on an exploration of this digital frontier, our study delves into the virtual domains of enterprises entrenched in the supply chain sector of the Greek economy. The spotlight falls upon four dominant transportation firms of the Greek supply chain sector, to unravel the relationship between their website activities and the prediction of their stock market prices. Our analytical tools, adorned with sophisticated statistical methodologies, embracing normality tests, correlations, ANOVA, linear regressions and the utilization of regression residual tests were deployed with precision. As the analytical methodology was deployed, a revelation emerged: The digital footprints left by customers on the virtual domains of supply chain firms provided the ability to predict and influence stock prices. Metrics such as bounce rates, the influx of new visitors and the average time on websites emerged as important factors, that could predict the fluctuations in the stock prices of these Greek supply chain firms. Web analytics have been discerned as a determining factor for predicting the course of transportation firms' stock prices. It serves as a clarion call for global scrutiny, inviting scholars and practitioners alike to scrutinize analogous firms on a global canvas. In this convergence of virtual footprints and financial trajectories lies not just a revelation for today but a harbinger of insights that resonate far beyond the digital borders of the Hellenic transportation sector.
Citation: Nikolaos T. Giannakopoulos, Damianos P. Sakas, Nikos Kanellos, Christos Christopoulos. Web analytics and supply chain transportation firms' financial performance[J]. National Accounting Review, 2023, 5(4): 405-420. doi: 10.3934/NAR.2023023
In the dynamic landscape of today's digitized markets, organizations harness the power of vast and swiftly accessible data to glean invaluable insights. A significant portion of this data emanates from user behavior on business websites. Unraveling the intricacies of this user behavior has become paramount for businesses, serving as the compass guiding the adaptation and evolution of their digital marketing strategies. Embarking on an exploration of this digital frontier, our study delves into the virtual domains of enterprises entrenched in the supply chain sector of the Greek economy. The spotlight falls upon four dominant transportation firms of the Greek supply chain sector, to unravel the relationship between their website activities and the prediction of their stock market prices. Our analytical tools, adorned with sophisticated statistical methodologies, embracing normality tests, correlations, ANOVA, linear regressions and the utilization of regression residual tests were deployed with precision. As the analytical methodology was deployed, a revelation emerged: The digital footprints left by customers on the virtual domains of supply chain firms provided the ability to predict and influence stock prices. Metrics such as bounce rates, the influx of new visitors and the average time on websites emerged as important factors, that could predict the fluctuations in the stock prices of these Greek supply chain firms. Web analytics have been discerned as a determining factor for predicting the course of transportation firms' stock prices. It serves as a clarion call for global scrutiny, inviting scholars and practitioners alike to scrutinize analogous firms on a global canvas. In this convergence of virtual footprints and financial trajectories lies not just a revelation for today but a harbinger of insights that resonate far beyond the digital borders of the Hellenic transportation sector.
| [1] | Akhil NSB, Kumar V, Raj R, et al. (2023) Adoption of human resource sourcing strategies for managing supply chain performance during COVID-19 crisis: evidence from manufacturing companies. Int J Product Perform Manag. https://doi.org/10.1108/IJPPM-06-2023-0292 |
| [2] |
Amado A, Cortez P, Rita P, et al. (2018) Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. Eur Res Manag Bus Econ 24: 1–7. https://doi.org/10.1016/j.iedeen.2017.06.002 doi: 10.1016/j.iedeen.2017.06.002
|
| [3] |
Barbosa MW, Vicente ADLC, Ladeira MB, et al. (2018) Managing supply chain resources with big data analytics: a systematic review. Int J Logist Res Appl 21: 177–200. https://doi/10.1080/13675567.2017.1369501 doi: 10.1080/13675567.2017.1369501
|
| [4] |
Barrales-Molina V, Martínez-López FJ, Gázquez-Abad JC (2014) Dynamic marketing capabilities: Toward an integrative framework. Int J Manag Rev 16: 397–416. https://doi.org/10.1111/ijmr.12026 doi: 10.1111/ijmr.12026
|
| [5] |
Cao G, Duan Y, Banna ΑΕ (2019) A dynamic capability view of marketing analytics: Evidence from UK firms. Ind Mark Manag 76: 72–83. https://doi.org/10.1016/j.indmarman.2018.08.002 doi: 10.1016/j.indmarman.2018.08.002
|
| [6] | Chase CW (2013) Demand-driven forecasting: A structured approach to forecasting. New Jersey: John Wiley & Sons. https://doi.org/10.1002/9781118691861 |
| [7] |
Chen D, Preston DS, Swink M (2015) How the use of big data analytics affects value creation in supply chain management. J Manag Inf Syst 32: 4–39. 10.1080/07421222.2015.1138364 doi: 10.1080/07421222.2015.1138364
|
| [8] |
Chowdhury LAM, Rana T, Akter M, et al. (2018) Impact of intellectual capital on financial performance: evidence from the Bangladeshi textile sector. J Account Organ Change 14: 429–454. https://doi.org/10.1108/JAOC-11-2017-0109 doi: 10.1108/JAOC-11-2017-0109
|
| [9] |
DuHadway S, Carnovale S, Hazen B (2019) Understanding risk management for intentional supply chain disruptions: risk detection, risk mitigation, and risk recovery. Ann Oper Res 283: 179–198. 10.1007/s10479-017-2452-0 doi: 10.1007/s10479-017-2452-0
|
| [10] |
Erevelles S, Fukawa N, Swayne L (2016) Big Data consumer analytics and the transformation of marketing. J Bus Res 69: 897–904. 10.1016/j.jbusres.2015.07.001 doi: 10.1016/j.jbusres.2015.07.001
|
| [11] |
Fagan JC (2014) The Suitability of Web Analytics Key Performance Indicators in the Academic Library Environment. J Acad Librariansh 40: 25–34. http://dx.doi.org/10.1016/j.acalib.2013.06.005 doi: 10.1016/j.acalib.2013.06.005
|
| [12] |
Fan SK, Lau RYK, Zhao JL (2015) Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix. Big Data Res 2: 28–32. https://doi.org/10.1016/j.bdr.2015.02.006 doi: 10.1016/j.bdr.2015.02.006
|
| [13] | Gangaraju PK, Raj R, Kumar V, et al. (2023) Financial performance in Industry 4.0 agile supply chains: evidence from manufacturing companies. TQM J. https://doi.org/10.1108/TQM-07-2023-0214 |
| [14] |
Germann F, Lilien GL, Rangaswamy A (2013) Performance implications of deploying marketing analytics. Int J Res Mark 30: 114–128. 10.1016/j.ijresmar.2012.10.001 doi: 10.1016/j.ijresmar.2012.10.001
|
| [15] |
Ghadge A, Kaklamanou M, Choudhary S, et al. (2017) Implementing environmental practices within the Greek dairy supply chain: Drivers and barriers for SMEs. Ind Manag Data Syst 117: 1995–2014. https://doi.org/10.1108/IMDS-07-2016-0270 doi: 10.1108/IMDS-07-2016-0270
|
| [16] | Hassani H, Huang X, Silva E (2018) Digitalisation and big data mining in banking. Big Data Cogn Comput 2: 18. https://doi.org/10.3390/bdcc2030018 |
| [17] |
Hajli N, Tajvidi M, Gbadamosi A, et al. (2020) Understanding market agility for new product success with big data analytics. Ind Mark Manag 86: 135–143. https://doi.org/10.1016/j.indmarman.2019.09.010 doi: 10.1016/j.indmarman.2019.09.010
|
| [18] | Hedin H, Hirvensalo I, Vaarnas M (2014) The Handbook of Market Intelligence: Understand, Compete and Grow in Global Markets. New Jersey: John Wiley & Sons. |
| [19] |
Hung JL, He W, Shen J (2020) Big data analytics for supply chain relationship in banking. Ind Mark Manag 86: 144–153. https://doi.org/10.1016/j.indmarman.2019.11.001 doi: 10.1016/j.indmarman.2019.11.001
|
| [20] |
Jabbar A, Akhtar P, Dani S (2020) Real-time big data processing for instantaneous marketing decisions: A problematization approach. Ind Mark Manag 90: 558–569. https://doi.org/10.1016/j.indmarman.2019.09.001 doi: 10.1016/j.indmarman.2019.09.001
|
| [21] |
Jacobs A (2009) The pathologies of big data. Commun ACM 52: 36–44. https://doi.org/10.1145/1536616.1536632 doi: 10.1145/1536616.1536632
|
| [22] |
Jeon S, Hong B, Chang V (2018) Pattern graph tracking-based stock price prediction using big data. Future Gener Comput Syst 80: 171–187. http://dx.doi.org/10.1016/j.future.2017.02.010 doi: 10.1016/j.future.2017.02.010
|
| [23] |
Jha A, Sharma RRK, Kumar V, et al. (2022) Designing Supply Chain Performance System: A Strategic Study on Indian Manufacturing Sector. Supply Chain Manag 27: 66–88. https://doi.org/10.1108/SCM-05-2020-0198 doi: 10.1108/SCM-05-2020-0198
|
| [24] |
Kaufman BE (2015) The RBV theory foundation of strategic HRM: critical flaws, problems for research and practice, and an alternative economics paradigm. Hum Resour Manag J 25: 516–540. https://doi.org/10.1111/1748-8583.12085 doi: 10.1111/1748-8583.12085
|
| [25] |
Kaur P, Arora S (2015) Regression and Endogeneity Bias in Big Marketing Data. 4th International Conference on Eco-friendly Computing and Communication Systems, Procedia Computer Science 70: 41–47. 10.1016/j.procs.2015.10.025 doi: 10.1016/j.procs.2015.10.025
|
| [26] |
Kumar A, Shankar R, Aljohani NR (2020a) A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Ind Mark Manag 90: 493–507. https://doi.org/10.1016/j.indmarman.2019.05.003 doi: 10.1016/j.indmarman.2019.05.003
|
| [27] |
Kumar V, Verma P, Jha A, et al. (2020) Dynamics of a medium value consumer apparel supply chain key parameters. Int J Product Perform Manag 71: 445–476. https://doi.org/10.1108/IJPPM-10-2019-0501 doi: 10.1108/IJPPM-10-2019-0501
|
| [28] |
Kumar V, Verma P, So TC, et al. (2022) Managing supply chains during COVID-19 outbreak: a case of Hong Kong toy manufacturing company. J Humanit Logist Supply Chain Manag 12: 502–531. https://doi.org/10.1108/JHLSCM-10-2021-0109 doi: 10.1108/JHLSCM-10-2021-0109
|
| [29] |
Kumar V, Raj R, Verma P, et al. (2023) Assessing Risk and Sustainability Factors in Spice Supply Chain Management. Oper Manag Res, 1–20. https://doi.org/10.1007/s12063-023-00424-6 doi: 10.1007/s12063-023-00424-6
|
| [30] |
Lambert DM, Cooper MC, Pagh JD (1998) Supply chain management: implementation issues and research opportunities. Int J Logist Manag 9: 1–20. https://doi.org/10.1108/09574099810805807 doi: 10.1108/09574099810805807
|
| [31] |
Leeflang PS, Verhoef PC, Dahlstrφm P, et al. (2014) Challenges and solutions for marketing in a digital era. Eur Manag J 32: 1–12. https://doi.org/10.1016/j.emj.2013.12.001 doi: 10.1016/j.emj.2013.12.001
|
| [32] |
Lilien GL (2016) The B2B knowledge gap. Int J Res Mark 33: 543–556. https://doi.org/10.1016/j.ijresmar.2016.01.003 doi: 10.1016/j.ijresmar.2016.01.003
|
| [33] | Mikalef P, Krogstie J, Pappas IO, et al. (2019) Exploring the relationship between big data analytics capability and competitive performance: the mediating roles of dynamic and operational capabilities. Inf Manag 57: 103169. https://doi.org/10.1016/j.im.2019.05.004 |
| [34] |
Morgan NA (2012) Marketing and business performance. J Acad Mark Sci 40: 102–119. https://doi.org/10.1007/s11747-011-0279-9 doi: 10.1007/s11747-011-0279-9
|
| [35] |
Ngai EW, Xiu L, Chau DC (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36: 2592–2602. https://doi.org/10.1016/j.eswa.2008.02.021 doi: 10.1016/j.eswa.2008.02.021
|
| [36] | Raj R, Kumar V, Shah B (2023) Big data analytics adaptive prospects in sustainable manufacturing supply chain. Benchmarking: An International Journal. https://doi.org/10.1108/BIJ-11-2022-0690 |
| [37] | Raguseo E, Vitari C, Pigni F (2020) Profiting from big data analytics: The moderating roles of industry concentration and firm size. Int J Prod Econ 229: 107758. https://doi.org/10.1016/j.ijpe.2020.107758 |
| [38] |
Sachan S, Kumar V, Vardhan S, et al. (2023) Key supply chain strategies for post-COVID-19 recovery: evidence from an Indian smart furniture industry. Int J Emerg Mark 18: 1378–1396. https://doi.org/10.1108/IJOEM-12-2021-1926 doi: 10.1108/IJOEM-12-2021-1926
|
| [39] | Sakas DP, Giannakopoulos NT, Trivellas P (2023a) Exploring affiliate marketing's impact on customers' brand engagement and vulnerability in the online banking service sector. Int J Bank Mark. https://doi.org/10.1108/IJBM-01-2023-0009 |
| [40] | Sakas DP, Giannakopoulos NT, Nasiopoulos DK, et al. (2023b) Assessing the Efficacy of Cryptocurrency Applications' Affiliate Marketing Process on Supply Chain Firms' Website Visibility. Sustainability 15: 7326. https://doi.org/10.3390/su15097326 |
| [41] | Sakas DP, Giannakopoulos NT, Kanellos N, et al. (2023) Supply Chain Firms' Financial Performance Connection with Digital Marketing Website Data. Economic and Social Development: Book of Proceedings, 116–124. |
| [42] | Sakas DP, Giannakopoulos NT, Margaritis M, et al. (2023d) Modeling Supply Chain Firms' Stock Prices in the Fertilizer Industry through Innovative Cryptocurrency Market Big Data. Int J Financial Stud 11: 88. https://doi.org/10.3390/ijfs11030088 |
| [43] | Sathi M (2014) Engaging customers using big data: how Marketing analytics are transforming business. New York: Palgrave Macmillan. |
| [44] | Sena V, Ozdemir S (2020) Spillover effects of investment in big data analytics in B2B relationships: what is the role of human capital? Ind Mark Manag 86: 77–89. https://doi.org/10.1016/j.indmarman.2019.05.016 |
| [45] | Sharda R, Delen D, Turban E (2015) Business intelligence and analytics: Systems for decision support 10. London: Pearson. |
| [46] |
Sharma R, Mithas S, Kankanhalli A (2014) Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organizations. Eur J Inf Syst 23: 433–441. https://doi.org/10.1057/ejis.2014.17 doi: 10.1057/ejis.2014.17
|
| [47] |
Sivarajah U, Irani Z, Gupta S, et al. (2020) Role of big data and social media analytics for business to business sustainability: a participatory web context. Ind Mark Manag 86: 163–179. https://doi.org/10.1016/j.indmarman.2019.04.005 doi: 10.1016/j.indmarman.2019.04.005
|
| [48] |
Story V, O'Malley L, Hart S (2011) Roles, role performance, and radical innovation competencies. Ind Mark Manag 40: 952–966. https://doi.org/10.1016/j.indmarman.2011.06.025 doi: 10.1016/j.indmarman.2011.06.025
|
| [49] |
Tan W, Blake MB, Saleh I, et al. (2013) Social-network-sourced big data analytics. IEEE Internet Comput 17: 62–69. 10.1109/MIC.2013.100 doi: 10.1109/MIC.2013.100
|
| [50] |
Tang O, Musa SN (2011) Identifying risk issues and research advancements in supply chain risk management. Int J Prod Econ 133: 25–34. https://doi.org/10.1016/j.ijpe.2010.06.013 doi: 10.1016/j.ijpe.2010.06.013
|
| [51] | Trivellas P, Malindretos G, Reklitis P (2020) Implications of Green Logistics Management on Sustainable Business and Supply Chain Performance: Evidence from a Survey in the Greek Agri-Food Sector. Sustainability 12: 10515. https://doi.org/10.3390/su122410515 |
| [52] |
Troisi Ο, Maione G, Grimaldi M, et al. (2020) Growth hacking: Insights on data-driven decision-making from three firms. Ind Mark Manag 90: 538–557. https://doi.org/10.1016/j.indmarman.2019.08.005 doi: 10.1016/j.indmarman.2019.08.005
|
| [53] |
Tseng PH, Liao CH (2015) Supply Chain Integration, Information Technology, Market Orientation and Firm Performance in Container Shipping Firms. Int J Logist Manag 26: 82–106. https://doi.org/10.1108/IJLM-09-2012-0088 doi: 10.1108/IJLM-09-2012-0088
|
| [54] | Verma P, Kumar V (2017) Conquering the Emerging Markets: Enhance Supply Chain and Diversified Firm Performance. LAP LAMBERT Academic Publishing. |
| [55] |
Vorhies DW, Morgan NA (2005) Benchmarking marketing capabilities for sustainable competitive advantage. J Mark 69: 80–94. https://doi.org/10.1509/jmkg.69.1.80.55505 doi: 10.1509/jmkg.69.1.80.55505
|
| [56] |
Vorhies DW, Orr LM, Bush VD (2011) Improving customer-focused marketing capabilities and firm financial performance via marketing exploration and exploitation. J Acad Mark Sci 39: 736–756. https://doi.org/10.1007/s11747-010-0228-z doi: 10.1007/s11747-010-0228-z
|
| [57] | Waller MA, Fawcett SE (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J Bus Logist 34: 77–84. https://doi.org/10.1111/jbl.12010 |
| [58] |
Wamba SF, Gunasekaran A, Akter S, et al. (2017) Big data analytics and firm performance: Effects of dynamic capabilities. J Bus Res 70: 356–365. http://dx.doi.org/10.1016/j.jbusres.2016.08.009 doi: 10.1016/j.jbusres.2016.08.009
|
| [59] |
Wedel M, Kannan PK (2016) Marketing analytics for data-rich environments. J Mark 80: 97–121. https://doi.org/10.1509/jm.15.0413 doi: 10.1509/jm.15.0413
|
| [60] |
Wu X, Zhu X, Wu G, et al. (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26: 97–107. https://doi.org/10.1109/TKDE.2013.109 doi: 10.1109/TKDE.2013.109
|
| [61] |
Zaridis A, Vlachos I, Bourlakis M (2021) SMEs strategy and scale constraints impact on agri-food supply chain collaboration and firm performance. Prod Plan Control 32: 1165–1178. https://doi.org/10.1080/09537287.2020.1796136 doi: 10.1080/09537287.2020.1796136
|
| [62] | Zheng K, Zhang Z, Song B (2020) E-commerce logistics distribution mode in big-data context: a case analysis of JD. COM. Ind Mark Manag 86: 154–162. https://doi.org/10.1016/j.indmarman.2019.10.009 |
| [63] |
Zokaei K, Hines P (2007) Achieving consumer focus in supply chains. Int J Phys Distrib Logist Manag 37: 223–247. https://doi.org/10.1108/09600030710742434 doi: 10.1108/09600030710742434
|
Nikolaos T. Giannakopoulos, Damianos P. Sakas, Nikos Kanellos, Christos Christopoulos. Web analytics and supply chain transportation firms' financial performance[J]. National Accounting Review, 2023, 5(4): 405-420. doi: 10.3934/NAR.2023023
DownLoad: