بهینه سازی بازاریابی ویروسی در کسب و کارهای آنلاین با استفاده از درخت تصمیم مبتنی بر الگوریتم ژنتیک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مدیریت، دانشکده مدیریت و حسابداری، واحد چالوس، دانشگاه آزاد اسلامی، چالوس، ایران.

2 گروه علوم پایه، واحد چالوس، دانشگاه آزاد اسلامی، چالوس، ایران.

10.22105/dmor.2020.240196.1186

چکیده

هدف پژوهش حاضر شناسایی مولفه‌ها و توسعه یک الگو جهت ارائه قوانین بهینه بازاریابی ویروسی در کسب و کارهای آنلاین می‌باشد. یک پژوهش کاربردی و از نظر روش، آمیخته (کمی و کیفی) می‌باشد. جامعه آماری پژوهش در‌بخش کیفی شامل 15 نفر در نسلهای سه‌گانه X، Y و Z (نسل بازاریابی ملینیوم) و در‌بخش کمی شامل 460 نفر از خریداران آنلاین می‌باشد. ابزار گردآوری  داده‌ها در‌بخش کیفی تکنیک فرافکنی می‌باشد و از مصاحبه عمیق استفاده شده است. با استفاده از نرم‌افزار MAXQDA مصاحبه‌ها تحلیل و جمع‌بندی شده که از این طریق شش مولفه شناسایی گردید و سپس در‌بخش کمی از 12 خبره برای تعیین شاخص لاوشه استفاده شد و در ادامه تحلیل عاملی اکتشافی به‌وسیله نرم‌افزار SPSS انجام گرفت. از آن‌جا که انتخاب موثرترین مولفه‌های جدید بازاریابی ویروسی می‌تواند تاثیر زیادی در دقت مدل بازاریابی ویروسی در کسب‌وکارهای آنلاین داشته باشد، جهت شناسایی تاثیرگذارترین مولفه‌ها از الگوریتم فراابتکاری ژنتیک استفاده شد که نرم‌افزارهای به‌کارگرفته شده در این‌بخش WEKAو RAPIDMINERمی‌باشد. در نهایت با استفاده از روش درخت تصمیم قوانین بهینه‌سازی بازاریابی ویروسی شناسایی گردید. یافته‌ها ابتدا در‌بخش کیفی حاکی از آن است که ترغیب آنلاین، اعتماد آنلاین، پشتیبانی آنلاین، خدمات آنلاین، جذابیت آنلاین و ریسک‌پذیری آنلاین بعنوان مولفه‌های بازاریابی ویروسی می‌باشند. در ادامه در‌بخش کمی و الگوریتم ژنتیک نشان داد که مولفه‌ی ریسک‌پذیری آنلاین نمی‌تواند به‌عنوان مولفه اثرگذار جهت مدل‌سازی و استخراج قوانین بازاریابی ویروسی به‌کار گرفته شود، بنابراین از میان شش مولفه حذف گردید

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Optimization of viral marketing in online businesses using genetic algorithm based decision tree

نویسندگان [English]

  • Elham Fazelli Veisari 1
  • mohamad javad Taghipourian 1
  • Reza Tavoli 2
  • Ghydar Ghanbarzade 1
1 Department of Management, Faculty of Management and Accounting, Chalus Branch, Islamic Azad University, Chalus, Iran.
2 Department of Basic Sciences, Chalous Branch, Islamic Azad University, Chalous, Iran.
چکیده [English]

The purpose of this study is to identify the components and develop a model to provide rules for optimizing viral marketing in businesses. It is an applied research and in terms of method, it is mixed (quantitative and qualitative). The statistical population of the research in the qualitative part includes 15 people in the three generations X, Y and Z (Millennium marketing generation) and in the quantitative part includes 460 online buyers. Data collection tools were used in the qualitative part of projection technique and in-depth interview. Interviews were analyzed and summarized using MAXQDA software, through which six components were identified, and then in a small part of 12 experts were used to determine the index of CVR, and then exploratory factor analysis was performed by SPSS software. Because selecting the most effective new components of viral marketing can have a huge impact on the accuracy of the viral marketing model in online businesses, To identify the most effective components, genetic metaheuristic algorithm was used, which is the software used in this section, WEKA and RAPIDMINER. Finally, the rules of viral marketing optimization were identified using the decision tree method. Findings in the qualitative section indicate that online persuasion, online trust, online support, online services, online attractiveness and online risk-taking are components of viral marketing. In the quantitative section and genetic algorithm, it was shown that the online risk component could not be used as an effective component for modeling and extracting viral marketing rules, so it was removed from the six components.

کلیدواژه‌ها [English]

  • Viral Marketing
  • online businesses
  • optimization
  • meta-heuristic algorithms
  • decision trees
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دوره 5، شماره 2
تابستان 1399
صفحه 167-187
  • تاریخ دریافت: 29 فروردین 1399
  • تاریخ بازنگری: 13 تیر 1399
  • تاریخ پذیرش: 24 مرداد 1399