ارائه مدلی برای شبیه سازی عوامل مؤثر بر وفاداری برند در صنعت خرده فروشی آنلاین با رویکرد پویایی سیستم (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
با توجه به گسترش چشمگیر و روزافزون خرید آنلاین در صنعت خرده فروشی و تغییر رفتار مصرف کنندگان یکی از دغدغه های خرده فروشی های آنلاین در این صنعت ایجاد وفاداری و حس تعلق در مشتریان است. هدف از پژوهش حاضر ارائه مدلی پویا برای تدوین استراتژی وفاداری برند در صنعت خرده فروشی آنلاین است. بدین منظور با استفاده از روش پویایی سیستم عوامل اصلی مؤثر بر وفاداری به برند، شناسایی و روابط آنها به شکل نمودار علت و معلولی مشخص شد. درنهایت، نمودار جریان با استفاده از نمودار علت و معلولی و با استناد به نظر خبرگان و پیشینه پژوهش تهیه شد. شبیه سازی پویای پژوهش با استفاده از نرم افزار ونسیم نسخه 7.3.5 در افق زمانی 10 ساله از سال 1392 تا 1402 برای مصرف کنندگان خرده فروشی آنلاین صورت گرفته است. اعتبار مدل با اجرای آزمون های بازتولید رفتار و تحلیل حساسیت مدل تأیید و سپس سناریو رضایت مشتریان با افزایش 70 درصدی و دیگری با افزایش 30 درصدی و سناریو سیاست ها و استراتژی های بهبود با افزایش 50 درصدی بر اساس نظر های خبرگان پیشنهاد و نتیجه حاصل از اجرای این سناریوها شبیه سازی شد. یافته ها نشان داد که افزایش رضایت مشتریان و سیاست ها و استراتژی های بهبود موجب افزایش وفاداری به برند می شود که این خود نشان از رشد کسب وکار دارد. از میان این سه سناریو، افزایش 70 درصدی رضایت مشتریان تأثیر بیشتری بر وفاداری به برند داشت؛ بنابراین کسب وکارها با دو بازوی رضایت مشتریان و سیاست ها و استراتژی های بهبود می توانند وفاداری به برند را افزایش دهند و نیز کنترل کنند؛ زیرا این سناریو ها منجر به افزایش درآمد سالانه هر مشتری می شود.Presenting a Model for Simulating Factors Affecting Brand Loyalty in the Online Retail Industry with a System Dynamics Approach
Due to the significant and increasing expansion of online shopping in the retail industry and changing consumer behavior, one of the concerns of online retailers in this industry is to create loyalty and a sense of belonging among customers. The aim of this research is to provide a dynamic model for developing a brand loyalty strategy in the online retail industry. For this purpose, using the system dynamics method, the main factors affecting brand loyalty, recognition, and their relationships were determined in the form of a cause-and-effect diagram. Finally, using the cause-and-effect diagram and referring to the opinion of experts and the background of the research, a flow diagram was prepared. The dynamic simulation of the research has been done using Vensim software version 7.3.5 in the time horizon of 10 years, from 1392 (2013) to 1402 (2023) for online retail consumers. The validity of the model has been confirmed through the implementation of behavior reproduction tests and model sensitivity analysis. Then, three scenarios of a 70% increase in customer satisfaction, a 30% increase in customer satisfaction, and a 50% increase in improvement policies and strategies were proposed based on the opinions of experts, and the results of these scenarios were simulated. The findings showed that increasing customer satisfaction and improvement policies and strategies increase brand loyalty, which indicates business growth. Among these three scenarios, a 70% increase in customer satisfaction had a greater impact on brand loyalty. Therefore, businesses with the two arms of customer satisfaction and improvement policies and strategies can increase and control brand loyalty, which lead to an increase in the annual revenue of each customer. Keywords: Online Retail, Customer Loyalty, System Dynamics, Modeling and Simulation.IntroductionIt is essential for companies to project a reliable brand experience to prevent customers from shifting to rival brands (Arora & Neha, 2016). Brand loyalty is therefore one of the pivotal business concepts that have evolved in the recent decade. Brand loyalty, defined as repeated purchases and commitment to a brand (Dick & Basu 1994; Eelen et al., 2017), has been considered one of the most valuable assets across all marketing metrics. In a global study conducted by the CMO Club that surveyed nearly 70 chief marketing officers (CMOs), approximately half of the CMOs rated the creation and retention of brand loyalty as the most critical indicator of brand performance (Guo & Wang, 2024). As the advancement of digital technology is shaping people's shopping expectations and experiences, understanding how to maintain brand loyalty becomes even more critical because our knowledge of brand loyalty is challenged by the transformation of consumers’ shopping behavior in the digital era (Guo & Wang, 2024).An essential growing trend of technological changes in marketing has been the advent of online shopping. With the development of the Internet and commercial practices, online retailing has evolved into a globally interactive, effective, and cognitively challenging action (Zhu et al., 2024). Formally, online retailing (also known as online shopping) refers to selling goods over the Internet or at a distance as the final step in a transaction (Childers et al., 2001). Online retailing differs from its offline counterpart in its main operating channels (Verhoef et al., 2015). Online retailing shops attract more customers than offline shops thanks to the more available products, lower prices, and targeted marketing strategies (Zhu et al., 2024). With this backdrop, online retailers have a good chance to grow their business and establish a stable relationship with customers, especially in retaining existing customers. When a company's customer retention rate increases by 5 %, profits increase by 25% to 85% (Parasuraman et al., 1991). Understanding how to enhance customer loyalty is thus critical for companies. Despite the recognized need for a better understanding of this phenomenon, the existing research remains limited. To this end, this study was conducted with the aim of presenting a model for simulating factors affecting brand loyalty in the online retail industry with a system dynamics approach. By examining the factors affecting loyalty, this research sought to provide a deeper insight into the reasons why consumers accept brand loyalty. Materials and MethodsThe current applied research used the system dynamics approach for simulation. This technique, which originated from systems thinking, has a dynamic view and feedback to systems. One of the most important advantages of using the system dynamics approach is paying attention to all system elements at the same time. Our spatial domain in this research is brand loyalty. In this research, variables affecting brand loyalty were extracted from the research literature using the opinions of experts and experts. Next, by using historical system data and information, relationships between variables and their initial values were extracted. After that, it was tried to draw a cause-and-effect diagram for these variables and draw the relationships between them. Finally, we arrived at a comprehensive model for brand loyalty in the online retail industry. This model was proposed and implemented under three scenarios of customer satisfaction with a 70% increase, customer satisfaction scenario with a 30% increase, and improvement policies and strategies with a 50% increase based on the opinions of experts in order to check how many percent of brand loyalty can be in the standard mode increase. Therefore, the effect of policy implementation in increasing brand loyalty in the future was analyzed and investigated.Research FindingsThe results of the model simulation indicated that increasing customer satisfaction and improvement policies and strategies increase brand loyalty, which is an indication of business growth. Therefore, businesses can increase brand loyalty with the two arms of customer satisfaction and improvement policies and strategies. Discussion of Results and ConclusionsConsidering the increasing importance of ensuring survival and progress in the online retail industry in the turbulent environment in which it operates, the current research focused on factors affecting customer loyalty and providing appropriate solutions to increase it. For this purpose, after studying the background of the research, the components of customer loyalty were identified. However, because of the uncertainty of today's environment and due to the fact that these factors are changing day by day, to investigate its various dimensions, the approach of system dynamics is used in order to simulate this complex and dynamic system, and then by considering the interactive relationships between these factors, the relevant causal-disability model is used. The flow model was prepared with the system dynamics approach, and then the validity of the model was measured. After the model passed the related tests successfully, different scenarios were observed on the test model and their results. According to the leverage points, 3 policies were identified, which are analyzed as follows.According to the first leverage point, which is a 70% increase in customer satisfaction, it can be said that brand loyalty has greatly increased compared to the base case. The second leverage point increased customer satisfaction by 30% and it is evident that brand loyalty has grown compared to the base state, but the effect of this growth is less than the first policy. The third leverage point increased by a 50% improvement policies and strategies that show the growth of brand loyalty. However, according to the graph, it can be said that this scenario had a lower growth than the first policy. Another analysis that can be done with leverage points is that businesses can increase and control brand loyalty with their two arms, which are customer satisfaction and improvement policies and strategies, which lead to an increase in the annual revenue of each customer. In today's competitive markets with many offers for consumers, it is clear that building and maintaining brand loyalty is a key marketing objective. Therefore, online retailers should be customer-centric and create processes, programs, and practices that focus on customer interaction and brand communication. In addition, marketers should consider providing consumer satisfaction by increasing performance and operational indicators.
تبلیغات
