{"title":"Understanding the failure process of ventures: a perspective of the behavioral strategy","authors":"Fatemeh Sajjadian, Mirahmad Amirshahi, Neda Abdolvand, Bahman Hajipour, Shib Sankar Sana","doi":"10.1108/jm2-07-2023-0141","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>This study aims to endeavor to shed light on the underlying causal mechanisms behind the failure of startups by examining the failure process in such organizations. To achieve this goal, the study conducted a comprehensive review of the literature on the definition of failure and its various dimensions, resulting in the compilation of a comprehensive list of causes of startup failure. Subsequently, the failure process was analyzed using a behavioral strategy approach that encompasses rationality, plasticity and shaping, as well as the growth approach of startups based on dialectic, teleology and evolution theories.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>The proposed research methodology was a case study using process tracing, with the sample being a failed platform in the ride-hailing technology sector. The causal mechanism was further explicated through the combined application of the behavioral strategy approach and interpretive structural modeling analysis.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The findings of the study suggest that the failure of startups is a result of interlinked causes and effects, and growth in these organizations is driven by dialectic, teleology and evolution theories.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The outcomes of the research can assist startups in formulating an effective strategy to deliver the right value proposition to the market, thereby reducing the chances of failure.</p><!--/ Abstract__block -->","PeriodicalId":16349,"journal":{"name":"Journal of Modelling in Management","volume":"18 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Modelling in Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jm2-07-2023-0141","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
This study aims to endeavor to shed light on the underlying causal mechanisms behind the failure of startups by examining the failure process in such organizations. To achieve this goal, the study conducted a comprehensive review of the literature on the definition of failure and its various dimensions, resulting in the compilation of a comprehensive list of causes of startup failure. Subsequently, the failure process was analyzed using a behavioral strategy approach that encompasses rationality, plasticity and shaping, as well as the growth approach of startups based on dialectic, teleology and evolution theories.
Design/methodology/approach
The proposed research methodology was a case study using process tracing, with the sample being a failed platform in the ride-hailing technology sector. The causal mechanism was further explicated through the combined application of the behavioral strategy approach and interpretive structural modeling analysis.
Findings
The findings of the study suggest that the failure of startups is a result of interlinked causes and effects, and growth in these organizations is driven by dialectic, teleology and evolution theories.
Originality/value
The outcomes of the research can assist startups in formulating an effective strategy to deliver the right value proposition to the market, thereby reducing the chances of failure.
期刊介绍：
Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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