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In this paper, the authors have tried to propose an organizational antecedent’s model of sustainable entrepreneurial orientation. It is believed that firms with high degree of entrepreneurial orientation are more plausible to prosper. Therefore, the authors are looking on how they can use organization factor to achieve sustainable entrepreneurial orientation.

This study uses a multiple case-study approach while incorporating content analysis and interpretive structural modeling. After reviewing previous studies on entrepreneurial orientation and corporate entrepreneurship, multiple case study including six firms from different industries in Iranian economic context were studied to discover the main organizational antecedents of sustainable entrepreneurial orientation using content analysis. Furthermore, interpretive structural modeling methods were used to determine the relationship between realized antecedents.

The findings show that the organizational antecedents of sustainable entrepreneurial orientation are coaching, succession plans, organizational learning, chief executive office, professional staff, organizational alignment and key resources and capabilities. The proposed structural model shows the relationship between organizational antecedents of entrepreneurial orientation and coaching is determined as the building stone of sustainable entrepreneurial orientation.

Researchers believe that findings will help managers and practitioners on how to design organizational factors in their organization. Hence, they can strengthen factors that promote entrepreneurial orientation and make it sustainable to enhance organizational performance.

While entrepreneurial orientation construct is a well-recognized concept in the previous studies, little is known about its organizational antecedents. Current research identified model organizational antecedents to achieve sustainable entrepreneurial orientation.

The purpose of this paper is to evaluate the relationship between strategy and customer lifetime value (CLV). A new model was proposed for defining customers’ values based on the RFM model and segmenting bank customers using the K-means algorithm. In addition, the authors combined a new category with the delta model in order to analyze the behavior of each cluster.

This case study was based on an applied method following its objectives and a descriptive-analytic method in terms of data collection. In this research, the AHP, data mining and K-means clustering methods, as well as the discriminant analysis were applied for computing the weights of the indices, examining the relationship between the identified variables, clustering the records and ensuring the clustering accuracy based on the RFM model, respectively.

The paper confirmed the relationship between the strategies and CLV. For a cluster whose strategy was the best product, customers had a minimal CLV. For a cluster whose strategy was based on total customer solutions, customers had a median CLV. For a cluster whose strategy was a lock-in system, customers had a maximal CLV. The results suggested that the delta model with these three strategies could act as the CLV developers in two stages: conversion of transient customers to attached customers and conversion of attached customers to locked-in customers.

One of the limitations of this study was the lack of access to all the bank accounts and assessment of only the strategy type, while highlighting the exact association between every component of the strategies (e.g. structure, environment, etc.) and CLV as a dependent variable deemed to be of a great necessity. Hence, it is recommended that several studies on the relationships presented in this paper be performed to provide further insights into and guidelines on this issue in the future.

This study emphasized the relevance of strategy and CLV. Managers should differently treat customers in distinct CLV and loyalty levels. In other words, managers must segment their customers based on CLV and apply appropriate strategies for each segment.

This research tried to fulfill an identified need to study how strategy can be effect CLV through the application of the delta model with three strategic options.

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.

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.

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.

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.

The purpose of this study is to address why managers enter the excessive market. A comparison of the facts and perceptions of entrants relative to success in the market shows that many entrants are confident about the viability of their businesses and enter the market. Accordingly, the authors simulate market entry decisions to detect behavioral biases.

The authors adapted the entry decisions simulation method, which is supported by the theoretical foundations of signal detection theory (SDT) and signaling theory. The simulation model is implemented on the Anaconda platform and written in Python 3.

The results of this study suggest that overestimation relates to excess market entry. Also, the proportion of excess entry under difficult conditions is always higher than under easy conditions.

This research helps managers and firms think about their and their competitors' abilities and evaluate them before entering the market. Policymakers and practitioners can also design programs such as experiential learning to help entrants assess their skills.

So far, no research has investigated the role of overconfidence under different market conditions. Accordingly, this study contributes to the current market entry literature by disentangling the debate between absolute and relative confidence and by considering the role of task difficulty.