نشریه علمی راهبردهای بازرگانی

شناسایی چارچوب گفتمانی راه‌کارهای مقابله با تغییرات اقلیمی مبتنی بر هوش مصنوعی: تحلیل اکتشافی از کسب-وکارهای B2B

نوع مقاله : مقاله مستخرج از رساله دکتری

نویسندگان

1 دانشجوی دکترای مدیریت بازرگانی، دانشکده اقتصاد، مدیریت و علوم اداری، دانشگاه سمنان، سمنان، ایران

2 استاد گروه مدیریت، دانشکده اقتصاد، مدیریت و علوم اداری، دانشگاه سمنان، سمنان، ایران

3 دانشیار گروه مدیریت، دانشکده اقتصاد، مدیریت و علوم اداری، دانشگاه سمنان، سمنان، ایران سمنان

4 استاد گروه مدیریت، دانشکده اقتصاد، مدیریت و علوم اداری، دانشگاه سمنان، سمنان. ایران

چکیده

پیچیدگی، عدم اطمینان و تغییرات محیطی از جمله تغییرات اقلیمی، کسب­وکارهای B2B را ملزم به شناسایی راه­کارهایی برای مقابله با آن­ها و بازنگری نسبت به استراتژی­های خود کرده است. از این رو پژوهش حاضر با هدف شناسایی چارچوب گفتمانی راه­کارهای مقابله با تغییرات اقلیمی مبتنی بر هوش مصنوعی در کسب­وکارهای B2B با استفاده از روش کیو انجام گرفت. چارچوب فلسفی این پژوهش، پارادایم تفسیری-اثبات­گرایی است که از منظر هدف، کاربردی شمرده می‌شود. مشارکت‌کنندگان پژوهش که به‌صورت هدفمند و با روش گلوله برفی انتخاب شدند، 9 نفر از مدیران کسب­وکارهای B2B بودند که کسب­وکار آن­ها متاثر از تغییرات اقلیمی می­باشد. دیدگاه‌های آن‌ها با تحلیل عاملی کیو و استفاده از نرم‌افزار SPSS 25 تجزیه­وتحلیل شد. نتایج نشان می­دهد که چارچوب گفتمانی راه­کارهای مقابله با تغییرات اقلیمی مبتنی بر هوش مصنوعی در سه دسته ذهنیت، ظرفیت­سازی آموزش، کاهش و ظرفیت­سازی ادراک، قرار دارد. تغییرات اقلیمی شدیدترین تهدیدی است که نوع بشر امروزی با آن مواجه و مقابله با آن نه تنها یک ضرورت فوری جهانی بلکه یک فرصت تجاری عظیم می­باشد. از سوی دیگر هوش مصنوعی قدرتمندترین ابزاری است که بشر در قرن بیست و یکم در اختیار دارد که با استفاده از این ابزار می­تواند با این تغییرات مقابله کند و و بهره­وری خود را حفظ و یا حتی ارتقا دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Identifying the discourse framework of solutions to deal with climate change based on artificial intelligence: An Exploratory Investigation of B2B Firms

نویسندگان [English]

  • Maryam Asgharinajib 1
  • Davood Feiz 2
  • Morteza Maleki MinBashRazgah 3
  • azim zarei 4

1 Ph.D. Candidate in Marketing Management, Faculty of Economics, Management and Administrative sciences, Semnan University, Semnan, Iran

2 Professor, Faculty of Economics, Management and Administrative Sciences, semnan university

3 Associate Prof., Department of Management, Faculty of Economics, Management and Administrative sciences, Semnan University, Semnan, Iran

4 Professor, Faculty of Economics, Management and Administrative Sciences, semnan university

چکیده [English]

Climate change is one of the most important challenges that has had an increasing impact on B2B businesses around the world. From this, B2B businesses are required to identify solutions to deal with climate change. One of the tools that can help these businesses in this direction is artificial intelligence. Therefore, the current research was conducted with the aim of identifying the discourse framework of solutions to deal with climate change based on artificial intelligence in B2B firms using the Q method. The philosophical framework of this research is the the interpretive-positivist paradigm (Q method), which is practical from the point of view of the goal. The research participants were 9 B2B business managers whose business is affected by climate change. Participants were selected purposefully. The views of the participants were analyzed by Q factor analysis and using SPSS 25 software. The results showed that the discourse framework of solutions to deal with climate change based on artificial intelligence is in three categories: education capacity-building, mitigation, and perception capacity-building.

کلیدواژه‌ها [English]

  • B2B firms
  • Climate change
  • Artificial intelligence
  • Q method
  1. DiBella J. The spatial representation of business models for climate adaptation: An approach for business model innovation and adaptation strategies in the private sector. Business Strategy & Development. 2020;3(2):245-60.
  2. IPCC. Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. https://www.ipcc.ch/sr15/2018.
  3. Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR. Global Warming of 1.5° C: IPCC Special Report on Impacts of Global Warming of 1.5° C above Pre-industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Poverty: Cambridge University Press; 2018.
  4. Pachauri RK, & Meyer, L. A. . Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 2014.
  5. FORBES. organizations are feeling pain of climate change here are five ways its affecting their business. https://b2n.ir/e340682021.
  6. Forino G, von Meding J. Climate change adaptation across businesses in Australia: interpretations, implementations, and interactions. Environment, Development and Sustainability. 2021;23(12):18540-55.
  7. Chandy R, Dowell G, Mayer C, Plambeck E, Serafeim G, Toffel M, et al. Management science—special issue on business and climate change. Management Science. 2019;65(7):3447-8.
  8. Hu Q, Lu Y, Pan Z, Gong Y, Yang Z. Can AI artifacts influence human cognition? The effects of artificial autonomy in intelligent personal assistants. International Journal of Information Management. 2021;56:102250.
  9. Guo H, Xu H, Tang C, Liu-Thompkins Y, Guo Z, Dong B. Comparing the impact of different marketing capabilities: Empirical evidence from B2B firms in China. Journal of Business Research. 2018;93:79-89.
  10. Chen P, Gao J, Ji Z, Liang H, Peng Y. Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities. Energies. 2022;15(15):5730.
  11. Huntingford C, Jeffers ES, Bonsall MB, Christensen HM, Lees T, Yang H. Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters. 2019;14(12):124007.
  12. Dwivedi YK, Ismagilova E, Hughes DL, Carlson J, Filieri R, Jacobson J, et al. Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management. 2021;59:102168.
  13. Ågerfalk PJ. Artificial intelligence as digital agency. European Journal of Information Systems. 2020;29(1):1-8.
  14. Ransbotham S, Gerbert P, Reeves M, Kiron D, Spira M. Artificial intelligence in business gets real. MIT sloan management review. 2018;60280.
  15. Mikalef P, Conboy K, Krogstie J. Artificial intelligence as an enabler of B2B marketing: A dynamic capabilities micro-foundations approach. Industrial Marketing Management. 2021;98:80-92.
  16. Duan Y, Edwards JS, Dwivedi YK. Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management. 2019;48:63-71.
  17. Chui M. Artificial intelligence the next digital frontier. McKinsey and Company Global Institute. 2017;47(3.6).
  18. Microsoft. The carbon benefits of cloud computing: a study on the Microsoft Cloud. https://www.microsoft.com/en-us/download/details.aspx?id=569502018.
  19. Cowls J, Tsamados A, Taddeo M, Floridi L. The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. Ai & Society. 2021:1-25.
  20. Dwivedi YK, Wang Y. Guest editorial: Artificial intelligence for B2B marketing: Challenges and opportunities. Elsevier; 2022. p. 109-13.

21 .Cortez RM, Johnston WJ. The future of B2B marketing theory: A historical and prospective analysis. Industrial Marketing Management. 2017;66:90-102.

  1. Webster Jr FE. Management Science in Industrial Marketing: A review of models and measurement techniques–new rigor, new sophistication. Journal of Marketing. 1978;42(1):21-7.
  2. Lilien GL, Petersen, A. J., & Wuyts, S. . Handbook of business-to-business marketing: Edward Elgar Publishing; 2022.
  3. Webster FE, Keller KL. A roadmap for branding in industrial markets. Journal of Brand Management. 2004;11(5):388-402.
  4. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine. 2006;27(4):12-.
  5. Huang M-H, Rust RT. Artificial intelligence in service. Journal of Service Research. 2018;21(2):155-72.
  6. Syam N, Sharma A. Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management. 2018;69:135-46.
  7. Davenport TH, Ronanki R. Artificial intelligence for the real world. Harvard business review. 2018;96(1):108-16.
  8. Kaplan A, Haenlein M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons. 2019;62(1):15-25.
  9. Akter S, Wamba SF, Mariani M, Hani U. How to build an AI climate-driven service analytics capability for innovation and performance in industrial markets? Industrial Marketing Management. 2021;97:258-73.
  10. Ransbotham S, Kiron D. Using analytics to improve customer engagement. MIT Sloan Management Review. 2018;59(3):1-20.
  11. Ouhamdouch S, Bahir M, Ouazar D, Carreira PM, Zouari K. Evaluation of climate change impact on groundwater from semi-arid environment (Essaouira Basin, Morocco) using integrated approaches. Environmental Earth Sciences. 2019;78(15):1-14.
  12. Martens P, McEvoy D, Chang CT. Climate change: responding to a major challenge for sustainable development. Sustainability science: Springer; 2016. p. 303-10.
  13. Mousavi A, Ardalan A, Takian A, Ostadtaghizadeh A, Naddafi K, Bavani AM. Climate change and health in Iran: a narrative review. Journal of Environmental Health Science and Engineering. 2020;18(1):367-78.
  14. Inderwildi O, Zhang C, Wang X, Kraft M. The impact of intelligent cyber-physical systems on the decarbonization of energy. Energy & Environmental Science. 2020;13(3):744-71.
  15. Sayed-Mouchaweh M. Artificial intelligence techniques for a scalable energy transition: advanced methods, digital technologies, decision support tools, and applications: Springer; 2020.
  16. Narciso DA, Martins F. Application of machine learning tools for energy efficiency in industry: A review. Energy Reports. 2020;6:1181-99.
  17. Paschen J, Wilson M, Ferreira JJ. Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons. 2020;63(3):403-14.
  18. Collins C, Dennehy D, Conboy K, Mikalef P. Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management. 2021;60:102383.
  19. Bag S, Pretorius JHC, Gupta S, Dwivedi YK. Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change. 2021;163:120420.
  20. Coombs C, Hislop D, Taneva SK, Barnard S. The strategic impacts of Intelligent Automation for knowledge and service work: An interdisciplinary review. The Journal of Strategic Information Systems. 2020;29(4):101600.
  21. Leone D, Schiavone F, Appio FP, Chiao B. How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem. Journal of Business Research. 2021;129:849-59.
  22. Rolnick D, Donti PL, Kaack LH, Kochanski K, Lacoste A, Sankaran K, et al. Tackling climate change with machine learning. ACM Computing Surveys (CSUR). 2022;55(2):1-96.

44 .Churruca K, Ludlow K, Wu W, Gibbons K, Nguyen HM, Ellis LA, et al. A scoping review of Q-methodology in healthcare research. BMC medical research methodology. 2021;21(1):1-17.

  1. Brown SR, editor The history and principles of Q methodology in psychology and the social sciences. Red at the British Psychological Society Symposium on, A Quest for a Science of Subjectivity: The Lifework of William Stephenson, University of London; 1997.
  2. Stenner P, Watts S, Worrell M. Q methodology. The SAGE handbook of qualitative research in psychology. 2008:215-39.
  3. Khoshgouyan fard A. Q Method: IRIB research (In Persian); 2007.
  4. Dennis KE. Q-methodology: New perspectives on estimating reliability and validity. Measurement of nursing outcomes. 1988;2:409-19.
  5. Carnelley JA. The Components of Marketing Capability: A framework and processes of knowledge integration for development: University of Pretoria; 2018.
  6. Surminski S. Private-sector adaptation to climate risk. Nature Climate Change. 2013;3(11):943-5.
  7. BCG. How AI Can Be a Powerful Tool in the Fight Against Climate Change. https://web-assets.bcg.com/ff/d7/90b70d9f405fa2b67c8498ed39f3/ai-for-the-planet-bcg-report-july-2022.pdf2022.
  8. Wamba-Taguimdje S-L, Wamba SF, Kamdjoug JRK, Wanko CET. Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal. 2020;26(7):1893-924.
  9. Balaban O, Gedikli B. Sustainability of Climate Policy at Local Level: The Case of Gaziantep City. Smart Resilient and Transition Cities Emerging Approaches and Tools for a Climate-Sensitive Urban Development Amsterdam: Elsevier. 2018.
  10. Abdalla AN, Nazir MS, Tao H, Cao S, Ji R, Jiang M, et al. Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview. Journal of Energy Storage. 2021;40:102811.
  11. Nazir MS, Wang Y, Bilal M, Sohail HM, Kadhem AA, Nazir HR, et al. Comparison of small-scale wind energy conversion systems: economic indexes. Clean Technologies. 2020;2(2):144-55.
  12. Engel H, Enkvist P-A, Henderson K. How companies can adapt to climate change. McKinsey com. 2015.
  13. CDP. Major Public Companies Describe Climate-Related Risks and Costs: A Review of Findings from CDP 2011–2013 Disclosures. 2014.
  14. Review HB. Machine Learning Can Help B2B Firms Learn More About Their Customers. https://hbr.org/2018/01/machine-learning-can-help-b2b-firms-learn-more-about-their-customers2018.
  15. CDP. Tackle your environmental risks, achieve your sustainability goals and make your business more resilient. https://www.cdp.net/en/supply-chain2021.
نشریه علمی راهبردهای بازرگانی
دوره 18، شماره 18
اسفند 1400
صفحه 183-201