طراحی مدل پویای مدیریت کسب و کارهای نوپا بر اساس پویایی‌شناسی سیستم
محورهای موضوعی : مدیریت فناوری و اطلاعاتحمید عدل دوست 1 , محمود البرزی 2 , سید عبداله امین موسوی 3
1 - دانشجوی دکتری گروه مدیریت فناوری اطلاعات، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - دانشیار گروه مدیریت فناوری اطلاعات، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
3 - استادیار گروه مدیریت فناوری اطلاعات، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه:
چکیده مقاله :
هدف این پژوهش ایجاد یک مدل پویایی‌شناسی سیستم، برای مدیریت دوره حیات کسب و کارهای نوپا -بر اساس عوامل شناسایی شده مؤثر در شکست و موفقیت این کسب و کارها و بررسی تأثیرات این عوامل در حلقه‌های مختلف است. روش این پژوهش در بخش شناسایی عوامل از نوع تئوری زمینه ای بوده و در بخش مدل سازی بر اساس پویایی‌شناسی سیستم می‌باشد. بر اساس مطالعات انجام شده، نرخ موفقیت کسب و کارهای نوپا (استارت‌آپ ها) در سراسر جهان بسیار پایین و طبق نتایج پژوهش‌ها کمتر از 10 درصد می‌باشد. لذا شناسایی عوامل مؤثر بر موفقیت و شکست استارت‌آپ‌ها و استخراج یک مدل پویا از این عوامل، می‌تواند به مدیریت استارت‌آپ‌ها و افزایش احتمال موفقیت بینجامد. جهت استخراج عوامل شکست و موفقیت در این پژوهش، 25 مصاحبه با فعالان حوزه کسب و کارهای نوپا در تهران، در چهارچوب روش نظریه زمینه‌ای ساخت گرا صورت پذیرفته است و پس از شناسایی عوامل مذکور شامل 87 مفهوم، 32 مقوله و 7 مقوله کلی، ابتدا نمودار‌های علی-حلقوی در حوزه‌های مختلف ترسیم شده و سپس یک مدل بر اساس پویایی‌شناسی سیستم، شامل 13 متغیر حالت از عوامل مؤثر ایجاد گردیده است. مدل حاصل، با تست‌های متعدد بررسی شده و نتایج نشان از امکان پیش‌بینی روند رشد و یا شکست استارت‌آپ‌ها از طریق مدل‌سازی و تعیین ضرایب مربوطه دارد.
The purpose of this research is to create a system dynamics model to manage the life cycle of start-up businesses based on the identified factors that are effective in the failure and success of these businesses and to examine the effects of these factors in different circles. The method of this research is grounded theory in the identification of factors and in the modeling part it is based on system dynamics. According to the studies, the success rate of startups around the world is very low, less than 10%. Therefore, identifying the factors affecting the success and failure of start-ups and designing a dynamic model based on these factors can lead to the management of start-ups and increase the probability of success. In order to extract failure and success factors in this research, 25 interviews were conducted with the activists of start-up businesses in Tehran, within the framework of the constructionist grounded theory method. The identified factors included 87 concepts, 32 categories and 7 general categories. To design the model, first, causal loops diagrams were drawn in different areas and then a model, based on system dynamics, was designed which included 13 stocks of effective factors. The resulting model was checked with numerous tests, the results of which revealed the possibility of predicting the growth or failure of startups through modeling and determining the relevant coefficients.
Key Words: Business Dynamics, Startup, Success and Failure Factors, System Dynamics
1.Introduction
The definition of "startup" is not uniformly agreed upon, and researchers find it difficult to provide an accurate description of it. Startups are recognized as the main drivers of economic growth and development. The influential role of these new elements in the economy and social development has led researchers and scientists to search for factors affecting the efficiency of these elements. Various researches in this field have been conducted based on different methods and strategies, including quantitative and qualitative research with different scopes. In these studies, definitions of business success and research methods vary greatly. Despite numerous studies on factors influencing the success and failure of startups, and even examining these factors specifically in various fields of startup businesses, few studies have investigated the impact of each of these factors on growth stages, the effect of each factor on other factors, and the creation of various influential loops. Additionally, given the formation of startups within communities with different characteristics, examining the factors affecting their outcomes can yield different results in various contexts. Startup life cycle is another topic that has been studied in various articles, each with a different approach. In each of the mentioned periods, multiple factors influence startups, which can lead to their failure or success. Apart from these factors, defining the concept of success is also challenging, and there are different definitions for it. Therefore, it seems that the success of a startup depends on the subjective definition of its owner(s) and that a startup must go through multiple stages for its lifespan and to remain in business.
2.Literature Review
Various studies have been conducted to examine the challenges in the formation and growth of startups around the world, for instance the research done by Vieira et al. (2015) in Brazil who investigated two startups from the perspective of attracting external resources, motivation and background. To examine the factors influencing the success of startups, studies such as Klabunde (2015) on the level of trust between entrepreneurs and investors, Rasmussen and Tanev (2015) on the successful organizational structure and innovation networks, and Wallin et al. (2016) on the growth of technology startups in Finland can be mentioned. Regarding the growth stages of startups, studies have identified different models. Wing ki et al. (2015) found five stages: preparation for startup, the process of obtaining support, exit policies, supervision care, and severing ties with the supporter. Yoon-Jun (2010) categorized the stages into three: support, growth, and maturity. Pirloo and Presutti (2010) described the stages as emergence and early growth. Additionally, studies have examined the factors affecting startup success, such as Spyros et al. (2012) identifying nine factors and Bocken (2015) identifying 14 factors. A comprehensive study by Santisteban and Mauricio (2017) reviewed 1013 articles on this topic. Zimmerman and Chu (2013) examined the motivations, successes, and challenges of entrepreneurs in Venezuela, and Chittithaworn et al. (2011) conducted a similar study in Thailand. In Iran, Arasti et al. (2014) researched the failure and success factors of small and medium-sized businesses, and Mashhadi (2018) introduced 6 main factors of business failure. Other studies on the factors affecting startups in Iran include those by Nadafi and Ahmadvand (2017) and Tari and Porhelm (2020).
3.Methodology
This research consists of two phases, the first one to identify the factors affecting the failure and success of startups, and the second one to create an initial model of the relationships between controllable factors. Therefore, in terms of purpose, it is of a fundamental research, and in terms of data collection, it is qualitative. In the first phase, to identify the effective factors, the study was based on the grounded theory research strategy with a constructivist approach (Charmaz, 2006). The research participants were a number of coaches, managers, and founders of startups located in Tehran. Each individual has been active in a startup for at least 3 months. Due to the limitations of physical presence during the quarantine period and physical distancing due to the COVID-19 epidemic, the interviews with the participants were conducted through internet calling applications. Each interview was implemented and coded according to the standard grounded theory research model, and then analyzed. The codings were of three types: open, axial, and selective (Lee, 2001). The researcher repeatedly referred to the interview texts during the analysis to reach theoretical saturation or sufficiency. After determining the factors resulting from the first phase using the grounded theory method, the process of creating the initial model began using the system dynamics method. The general steps of creating the model using the business dynamics method include: problem framing (selecting the boundary), formulating the dynamic hypothesis, formulating the simulation model, testing, and designing and evaluating the policy. The main tools used in the system dynamics analysis are causal loop diagrams and stock-flow diagrams. These tools are the best choice in cases such as quickly reaching a hypothesis about the causes of dynamics, inferring and understanding the mental models of individuals or groups, and relating important feedbacks that you believe are effective in the problem.
4.Result
In the research process, 25 participants, including five women and 20 men, who were coaches, managers, and founders of startups, took part. All the participants had higher education, including eight with bachelor's degrees, 11 with master's degrees, and six with doctoral degrees. Finally, at three levels from part to whole, 87 concepts, 32 categories, and seven main categories were obtained using the constructivist grounded theory coding method (Mills et al., 2006). By processing the concepts extracted from the interview coding with a common basis and similar concepts, a category was formed, and the categories with a common basis or foundation created a main category. The main categories include "Business Model," "Attracting Capital and Support," "Market Knowledge and Management," "Founder's Managerial Capabilities," "Product/Service Characteristics," "Team Characteristics," and "Legal and Regulatory Issues". Each of these main categories includes several components, and each component is derived from several concepts extracted from the interview texts using Charmaz's constructivist approach. Causal Loop Diagrams resulting from the analysis of the interaction of research variables were obtained through the identification of the effective factors in the success and failure of startups. In this regard, six causal loop diagrams were identified, each showing the interaction of the main variables of the system. The identified loops are marked as reinforcing (R) and balancing (B) loops. According to the results, there are 13 state variables, including quality, innovation, after-sales service, price, customer satisfaction level, sales orders, production and sales human resource capacity, support human resource capacity, research and development human resource capacity, ongoing marketing activities, ongoing research and development activities, and capital. The evaluation and testing of the model were also performed based on the following tests:
- Model boundary adequacy test
- Structure test
- Extreme condition test
- Dimensional consistency test
- Accumulation error test
5.Discussion
The results were examined in two parts. In the first part, the results and data obtained from the interviews with the startup business community were presented in the form of grounded theory, which had a lot of overlap with the results found in the literature review. The main categories identified were almost consistent with the factors identified in the literature review. However, the results of this research have provided more details in the form of subcategories and concepts. In the second part, the results of the first part were depicted in the form of cause-and-effect diagrams, and the relationships governing the system variables were precisely identified. Then, the overall stock-flow diagram of the system was extracted, and the dynamic modeling was based on it, which includes 13 state variables. The extracted model was examined and validated through model boundary adequacy test, extreme condition test, structure test, and dimensional consistency test. In response to the main research question, in the stage of discovering the factors affecting success and failure, after the interviews and coding of factors and concepts, a total of 7 main categories, 32 subcategories, and 87 concepts were extracted. Then, by examining the main categories and their subcategories, six areas of influence were selected, which were analyzed using causal loop diagrams to examine the interaction of variables and factors. Finally, by studying the causal loop diagrams, in response to the second part of the main research question, a stock-flow diagram was created with a focus on product characteristics, marketing, and customers. It was shown that multiple models can be designed from different perspectives, and that the effect of decision-making in each area and on the change of each system variable can be examined. Furthermore, the research and analysis sessions revealed that causal loop diagrams can significantly help to explain the actual model of the system from the perspective of experts in this field. These diagrams were almost uniformly accepted by the experts, despite the significant differences in the functional areas of startups, due to the lack of the need for extensive specialized knowledge. In addition, the research showed that apart from the factors affecting the entire system, what can significantly affect the business model and results are the coefficients that need to be determined for the ratio between the interactions of transactions. For example, the effect coefficient of marketing activities on sales orders may be different in different industries, and the results will be completely different in the sensitivity analysis of this issue. Also, coefficients such as the conversion of R&D activities to innovation and the conversion coefficient of innovation to quality can be effective in customer satisfaction and, consequently, in sales orders, and this coefficient will be different in each industry. In the present research, the aim was to identify the factors of success and failure of startups, and then to separate the endogenous factors affecting startups and determine the effect of changing each variable. Another aspect of examining the factors can be to explain the effect of external factors, including the economic situation of the country (inflation and financial and political relations with other countries) and the stability of the workforce (the level of migration of human capital and problems such as military service). The results of such research can be provided to legislative and high-level decision-making bodies and can be helpful in the overall policymaking in the business sector. Another point is that startups, in their growth stages (startup, maturity, and exit), due to the inherent differences in conditions, should make different decisions regarding the variables affecting the system, such as marketing, innovation, and after-sales service. A separate study can analyze the impact of each variable in the different stages of the business life cycle and help in the selection of the business model at each stage of the startup's life.
