Site icon Hotel Tellemark

Low-carbon information quality dimensions and random forest algorithm evaluation model in digital marketing

Low-carbon information quality dimensions and random forest algorithm evaluation model in digital marketing
  • Agnew, M. D., Pettifor, H. & Wilson, C. Lifestyle, an integrative concept: Cross-disciplinary insights for low-carbon research. Wiley Interdiscip. Rev. Energy Environ.12(6). (2023).

  • Díaz-Padilla, V. T., Travar, I., Acosta-Rubio, Z. & Parra-López, E. Tourism competitiveness versus sustainability: Impact on the world economic forum model using the. Rasch Methodol. Sustain.15(18), 13700. (2023).

    Article 

    Google Scholar 

  • Jain, P., Chou, M. C., Fan, F. & Santoso, M. P. Embedding sustainability in the consumer goods innovation cycle and enabling tools to measure progress and capabilities. Sustainability13(12), 6662. (2021).

    Article 

    Google Scholar 

  • Wei, J., Zhang, L., Yang, R. & Song, M. A new perspective to promote sustainable low-carbon consumption: The influence of informational incentive and social influence. J. Environ. Manag.327, 116848. (2023).

    Article 

    Google Scholar 

  • Zhang, J., Lyu, Y., Li, Y. & Geng, Y. Digital economy: an innovation driving factor for low-carbon development. Environ. Impact Assess. Rev.96, 106821. (2022).

    Article 

    Google Scholar 

  • Denga, E. M., Vajjhala, N. R. & Rakshit, S. The role of digital marketing in achieving sustainable competitive advantage. Digit. Transform. Int. Strateg. Organ. 44–60. (2022).

  • Lewis, J. I. & Nemet, G. F. Assessing learning in low carbon technologies: Toward a more comprehensive approach. Wiley Interdiscip. Rev. Clim. Change12(5), e730. (2021).

    Article 

    Google Scholar 

  • Miguel, A. & Miranda, S. The role of digital platforms in promoting pro-sustainable behavior and conscious consumption by brands. Ecocycles9(2), 37–48. (2023).

    Article 

    Google Scholar 

  • Yang, S., Jahanger, A. & Hossain, M. R. Does China’s low-carbon city pilot intervention limit electricity consumption? An analysis of industrial energy efficiency using time-varying DID model. Energy Econ.121, 106636. (2023).

    Article 

    Google Scholar 

  • Castro-Santa, J., Drews, S. & Bergh, J. Nudging low-carbon consumption through advertising and social norms. J. Behav. Exp. Econ.102, 101956. (2023).

  • Wang, T., Shen, B., Springer, C. H. & Hou, J. What prevents us from taking low-carbon actions? A comprehensive review of influencing factors affecting low-carbon behaviors. Energy Res. Social. Sci.71, 101844. (2021).

    Article 

    Google Scholar 

  • Wu, Z., Duan, C., Cui, Y. & Qin, R. Consumers’ attitudes toward low-carbon consumption based on a computational model: Evidence from China. Technol. Forecast. Soc. Chang.186, 122119. (2023).

    Article 

    Google Scholar 

  • Zhang, L. et al. A data-driven approach to objective evaluation of urban low carbon development performance. J. Clean. Prod.368, 133238. (2022).

  • Wang, R. Y. & Strong, D. M. Beyond accuracy: What data quality means to data consumers. J. Manag. Inform. Syst.12(4), 5–33. (1996).

    Article 

    Google Scholar 

  • Del Rio, D. D. F., Sovacool, B. K. & Griffiths, S. Culture, energy and climate sustainability, and smart home technologies: A mixed methods comparison of four countries. Energy Clim. Change2, 100035. (2021).

  • Cheng, X., Wu, F., Li, W., Yang, J. & Long, R. What maintains low-carbon consumption behaviors: Evidence from China. Renew. Sustain. Energy Rev.189, 114050. (2024).

    Article 

    Google Scholar 

  • Peterson, R. A., Balasubramanian, S. & Bronnenberg, B. J. Exploring the implications of the internet for consumer marketing. J. Acad. Mark. Sci.25, 329–346. (1997).

    Article 

    Google Scholar 

  • Sağkaya Güngör, A. & Ozansoy Çadırcı, T. Understanding digital consumer: A review, synthesis, and future research agenda. Int. J. Consum. Stud.46(5), 1829–1858. (2022).

    Article 

    Google Scholar 

  • Hofacker, C., Golgeci, I., Pillai, K. G. & Gligor, D. M. Digital marketing and business-to-business relationships: A close look at the interface and a roadmap for the future. Eur. J. Market.54(6), 1161–1179. (2020).

  • Xu, Y. & Li, C. M. Digital transformation, firm boundaries, and market power: Evidence from china’s listed companies. Systems11(9), 479. (2023).

  • Sanbella, L., Van Versie, I. & Audiah, S. Online marketing strategy optimization to increase sales and e-commerce development: An integrated approach in the digital age. Startupreneur Bus. Digit.3(1), 54–66. (2024).

    Article 

    Google Scholar 

  • Liu, Y., Suo, X. K., Du, X. H., Wu, H. Q. & Lin, H. Corporate digital innovation and stock price crash risk. Finance Res. Lett.66, Article 105690. (2024).

  • Yang, R. Q. & Jiang, H. C. Digital marketing management control system based on blockchain under the internet background. Soft. Comput. (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chintalapati, S. & Pandey, S. K. Artificial intelligence in marketing: A systematic literature review. Int. J. Market Res.64(1), 38–68. (2022).

    Article 

    Google Scholar 

  • Varzaru, A. A. Assessing digital transformation acceptance in public organizations’ marketing. Sustainability15(1), Article 265. (2023).

  • Sahli, A. & Lichy, J. The role of augmented reality in the customer shopping experience. Int. J. Organ. Anal. (2024).

    Article 

    Google Scholar 

  • Jocevski, M. Blurring the lines between physical and digital spaces: Business model innovation in retailing. Calif. Manag. Rev.63(1), 99–117. (2020).

    Article 

    Google Scholar 

  • Bradač Hojnik, B. & Huđek, I. Small and medium-sized enterprises in the digital age: Understanding characteristics and essential demands. Information14(11), 606. (2023).

    Article 

    Google Scholar 

  • Peng, H., Bumailikaimu, S. & Feng, T. The power of market: venture capital and enterprise digital transformation. North Am. J. Econ. Finance74, Article 102218. (2024).

  • Dahiya, R. & Gayatri A research paper on digital marketing communication and consumer buying decision process: An empirical study in the Indian passenger car market. J. Global Mark.31(2), 73–95. (2018).

    Article 

    Google Scholar 

  • Kim, J., Kang, S. & Lee, K. H. Evolution of digital marketing communication: Bibliometric analysis and network visualization from key articles. J. Bus. Res.130, 552–563. (2021).

    Article 

    Google Scholar 

  • Wang, H., Wu, D. L. & Zeng, Y. M. Digital economy, market segmentation and carbon emission performance. Environ. Dev. Sustain. (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Surbakti, F. P. S., Wang, W., Indulska, M. & Sadiq, S. Factors influencing effective use of big data: A research framework. Inf. Manag.57(1), 103146. (2020).

    Article 

    Google Scholar 

  • Bovee, M., Srivastava, R. P. & Mak, B. A conceptual framework and belief-function approach to assessing overall information quality. Int. J. Intell. Syst.18(1), 51–74. (2003).

    Article 

    Google Scholar 

  • Rane, N. L., Achari, A. & Choudhary, S. P. Enhancing customer loyalty through quality of service: Effective strategies to improve customer satisfaction, experience, relationship, and engagement. Int. Res. J. Modernization Eng. Technol. Sci.5(5), 427–452. (2023).

    Article 

    Google Scholar 

  • Lăzăroiu, G., Neguriţă, O., Grecu, I., Grecu, G. & Mitran, P. C. Consumers’ decision-making process on social commerce platforms: Online trust, perceived risk, and purchase intentions. Front. Psychol.11, 890. (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Godoy, M. P., Rusu, C., Hatibovic, F., Granollers, T. & Ugalde, J. Addressing information consumer experience through a user-centered information management system in a Chilean university. Sustainability15 (22), Article 15998. (2023).

  • Kim, H. & Niehm, L. S. The impact of website quality on information quality, value, and loyalty intentions in apparel retailing. J. Interact. Mark.23(3), 221–233 (2009). https://www.taylorfrancis.com/chapters/edit/10.4324/9780429293276-3

    Article 

    Google Scholar 

  • Venkatesh, V., Brown, S. A. & Bala, H. Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Q. 21–54. (2013).

  • Al-Fraihat, D., Joy, M. & Sinclair, J. Evaluating E-learning systems success: An empirical study. Comput. Hum. Behav.102, 67–86. (2020).

    Article 

    Google Scholar 

  • Du, H. L. S., Xu, J. H., Tang, H. & Jiang, R. X. Repurchase intention in online knowledge service: The brand awareness perspective. J. Comput. Inform. Syst.62(1), 174–185. (2022).

    Article 

    Google Scholar 

  • Naim, A. & Alahmari, F. Reference model of e-learning and quality to establish interoperability in higher education systems. Int. J. Emerg. Technol. Learn.15(2), 15–28. (2020).

    Article 

    Google Scholar 

  • Struijk, M., Angelopoulos, S., Ou, C. X. & Davison, R. M. Navigating digital transformation through an information quality strategy: Evidence from a military organization. Inform. Syst. J.33(4), 912–952. (2023).

    Article 

    Google Scholar 

  • Rai, A., Tang, X., Yin, Z. & Du, S. Gaining customer loyalty with tracking information quality in B2B logistics. J. Manag. Inform. Syst.39(2), 307–335. (2022).

    Article 

    Google Scholar 

  • Alterkait, M. A. & Alduaij, M. Y. Impact of information quality on satisfaction with e-learning platforms: Moderating role of instructor and learner quality. SAGE OPEN14(1), Article 21582440241233400. (2024).

  • Cao, Q., Zhou, Y., Du, H., Ren, M. & Zhen, W. Carbon information disclosure quality, greenwashing behavior, and enterprise value. Front. Psychol.13, 892415. (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zha, D., Zhang, C., Jiang, P. & Wang, F. What makes energy consumption behavior visible? Conceptualization, scale development and validation of customized information feedback. J. Bus. Res.182, 114761. (2024).

    Article 

    Google Scholar 

  • Mills, J., Bonner, A. & Francis, K. The development of constructivist grounded theory. Int. J. Qual. Methods5(1), 25–35. (2006).

    Article 

    Google Scholar 

  • Lin, W., Wu, Z., Lin, L., Wen, A. & Li, J. An ensemble random forest algorithm for insurance big data analysis. IEEE Access5, 16568–16575. (2017).

    Article 

    Google Scholar 

  • Gustavsson, E. & Elander, I. Behaving clean without having to think green? Local eco-technological and dialogue-based, low-carbon projects in Sweden. J. Urban Technol.24(1), 93–116. (2017).

    Article 

    Google Scholar 

  • Sturges, J. E. & Hanrahan, K. J. Comparing telephone and face-to-face qualitative interviewing: A research note. Qualitative Res.4(1), 107–118. (2004).

    Article 

    Google Scholar 

  • Hurwitz, L. B. et al. Content analysis across new media platforms: Methodological considerations for capturing media-rich data. New. Media Soc.20(2), 532–548. (2018).

    Article 

    Google Scholar 

  • Roumeliotis, K. I. & Tselikas, N. D. A machine learning python-based search engine optimization audit software. In Informatics10(3), 68. (2023).

  • Pamulaparty, L., Rao, C. G. & Rao, M. S. Critical review of various near-duplicate detection methods in web crawl and their prospective application in drug discovery. Int. J. BioMed. Eng. Technol.25(2–4), 212–226. (2017).

    Article 

    Google Scholar 

  • Sun, J. Improving Quality of Programming and Software Through Knowledge Graph Construction and Application. Doctoral dissertation (The Australian National University, 2023). https://www.proquest.com/openview/5b5e3c1260a05ccb1313dace42759240

  • Peters, H. C. A-methodological saturation: A grounded theory analysis. Couns. Psychol.51(7), 933–969. (2023).

    Article 

    Google Scholar 

  • Biau, G. & Scornet, E. A random forest guided tour. Test25, 197–227. (2016).

    Article 
    MathSciNet 

    Google Scholar 

  • Gupta, S., Aga, D., Pruden, A., Zhang, L. & Vikesland, P. Data analytics for environmental science and engineering research. Environ. Sci. Technol.55(16), 10895–10907. (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Jantunen, E., Campos, J., Sharma, P. & McKay, M. Open source analytics solutions for maintenance. In 5th International Conference on Control, Decision and Information Technologies (CoDIT), vol. 8, 688–693. (IEEE, 2018). https://doi.org/10.1109/CoDIT.2018.8394819.

  • Yin, Y., Alqahtani, Y., Feng, J. H., Chakraborty, J. & McGuire, M. P. Classification of eye tracking data in visual information processing tasks using convolutional neural networks and feature engineering. SN Comput. Sci.2, 1–26. (2021).

    Article 

    Google Scholar 

  • Kadiyala, A. & Kumar Applications of python to evaluate the performance of bagging methods. Environ. Prog. Sustain. Energy37(5), 1555–1559. (2018).

    Article 
    CAS 

    Google Scholar 

  • Farnaaz, N. & Jabbar, M. A. Random forest modeling for network intrusion detection system. Procedia Comput. Sci.89, 213–217. (2016).

    Article 

    Google Scholar 

  • Yin, H. et al. A deep learning-based data-driven approach for predicting mining water inrush from coal seam floor using micro-seismic monitoring data. IEEE Trans. Geosci. Remote Sens. (2023).

    Article 

    Google Scholar 

  • Zermane, A., Tohir, M. Z. M., Zermane, H., Baharudin, M. R. & Yusoff, H. M. Predicting fatal fall from heights accidents using random forest classification machine learning model. Saf. Sci.159, 106023. (2023).

    Article 

    Google Scholar 

  • Yin, H. T., Wen, J. & Chang, C. P. Going green with artificial intelligence: The path of technological change towards the renewable energy transition. Oecon. Copernic.14(4), 1059–1095. (2023).

    Article 

    Google Scholar 

  • Lăzăroiu, G. et al. Environmentally responsible behavior and sustainability policy adoption in green public procurement. Sustainability12(5), 2110. (2020).

    Article 

    Google Scholar 

  • Bai, T. et al. Paths to low-carbon development in China: The role of government environmental target constraints. Oecon. Copernic.14(4), 1139–1173. (2023).

    Article 

    Google Scholar 

  • link

    Exit mobile version