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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 examine the critical failure factors (CFFs) linked to various types of process improvement (PI) projects such as Kaizen, Lean, Six Sigma, Lean Six Sigma and Agile. Proposing a mitigation framework accordingly is also an aim of this study.

This research undertakes a systematic literature review of 49 papers that were relevant to the scope of the study and that were published in four prominent databases, including Google Scholar, Scopus, Web of Science and EBSCO.

Further analysis identifies 39 factors that contribute to the failure of PI projects. Among these factors, significant emphasis is placed on issues such as “resistance to cultural change,” “insufficient support from top management,” “inadequate training and education,” “poor communication” and “lack of resources,” as primary causes of PI project failures. To address and overcome the PI project failures, the authors propose a framework for failure mitigation based on change management models. The authors present future research directions that aim to enhance both the theoretical understanding and practical aspects of PI project failures.

Through this study, researchers and project managers can benefit from well-structured guidelines and invaluable insights that will help them identify and address potential failures, leading to successful implementation and sustainable improvements within organizations.

To the best of the author’s knowledge, this paper is the first study of its kind to examine the CFFs of five PI methodologies and introduces a novel approach derived from change management theory as a solution to minimize the risk associated with PI failure.

This paper aims to examine the factors that lead to the failure of startups in India and proposes a ‘Four Dimensional (4D) Strategic Framework’ to drive success.

This study is exploratory and uses a narrative analysis methodology to analyse the accounts of key startup stakeholders – founders, investors, former employees and consumers; to identify their failure factors. A conveniently selected sample of 165 startups was studied to understand better the reasons for their failure within a thematic framework developed from David Feinleib’s (2012) handbook “Why Startups Fail”.

Results indicate that a dearth of capital or running out of money and inadequate sales and marketing strategy, which leads businesses to fall behind rivals and lose money on each transaction, are the most common factors for startup failure in India.

“Startups” are substantial for emerging economies like India because they fuel technological innovation and economic progress and provide for the modern workforce’s needs and aspirations. However, they seem to be typically unprofitable, with a modest probability of survival. Subsisting studies mainly focus primarily on success factors and very few on why startups fail, with significant disagreement on an appropriate methodology. To the best of the authors’ knowledge, this is the first study that analyses failure factors of Indian startups using narrative analysis of its key stakeholders. It aims to aid the conception of profitable entrepreneurship by reducing the failure frequency in the startup and small business ecology.

This study aims to develop and test a framework for studying the failure of new women entrepreneurs in the small- and medium-sized enterprises (SMEs) sector.

Using a sample of 114 unsuccessful entrepreneurs in Kigali, Rwanda, this study aimed to identify key failure factors of women-owned SMEs. This study used mixed methods where quantitative data were analysed using the principal component approach with Varimax rotation to reduce the variables to only three clusters.

The study findings revealed that the failure of women-owned SMEs results from the entrepreneur’s inability followed by the enterprise incompetence, which are both internally controllable factors and the inauspicious business environment. These findings contribute to the validity of the dynamic capability theory by explaining how well internal and external factors must stay glued together to avoid failure among women-owned SMEs, something that was not yet previously well documented so far.

New SMEs are considered a noteworthy constituent of Rwandan development. Unfortunately, most new SMEs, in general, do not grow; their failure rate is high (70%), which raised many worries for both researchers and policymakers as to why this occurs at this stage of business growth. Therefore, to the best of the authors’ knowledge this paper is the first to analyse the reasons for the failure of Rwandan women-owned SMEs in the service sector. These findings are important because they suggest that policies designed to reduce the incidence of SMEs’ failure should take account of the two main factors influencing failure among women entrepreneurs.

This study aims to present the results of a comparative study on the most key reasons for the failure of sustaining activities of operational improvement (OI) methodologies from the different types of manufacturing enterprises that located in Vietnam.

This study presents survey results from 30 local manufacturing enterprises and 21 foreign direct investment (FDI) enterprises located in Vietnam – a developing country. The authors utilize a combination method to collect data, including online and direct survey. The targeted interviewees selected to answer the questionnaire are manufacturing managers and top managers working in productivity, quality, engineering and other departments in respective firms. The developed questionnaire is verified by five experts to ensure the validity and soundness.

The study uncover that 21 local enterprises (70%) have not issued standard forms for OI deployment and supervision, while 17 foreign enterprises (81%) have adopted a systematic management and clearly indicators for evaluation of OI outputs. In addition, the top three reasons for OI failure are differences between local and foreign enterprises. In term of OI methodologies, most of participated enterprises implemented Lean tools and principles while only 7.8% of the participated enterprises applied Six Sigma. Three vital findings are uncovered, including, first, 30% of local enterprises standardize and supervise forms of OI.

There is a limitation in sample size, with the number of participants of 51 enterprises. Among the participants, 27% of local enterprises are small and medium-sized enterprises (SMEs) while 60% of FDI enterprises from large-sized group. This was limited to making a generated conclusion in the comparison of failure factors between two types of enterprises. This partly affects the radical comparison of failure factors between local and FDI enterprises.

To the best of the authors' view, this is the first empirical study that compares the reasons for the failure for sustaining OI between local and foreign enterprises in a developing country. The result from this study will make contributions for further research in considering OI failure factors and then enhance effectiveness of OI methodologies in manufacturing companies.

Joint ventures (JVs) serve as a viable tool in mitigating some of the challenges posed to the effective delivery of construction projects. However, JVs are highly susceptible to failure in most developing countries. Therefore, this study seeks to unravel the critical factors influencing the failure of JVs in the South African construction industry.

A quantitative approach was adopted for the study using a well-structured questionnaire as the instrument for data collection. Respondents for the study were built environment professionals in Gauteng province in South Africa. Data elicited from respondents were analyzed using a four-pronged process which included descriptive statistics, one sample t-test, exploratory factor analysis and confirmatory factor analysis.

Resulting from the analysis conducted, four critical components emerged as the major factors influencing the failure of JVs in the South African construction industry, which are inefficient financial framework, divergent organizational culture, poor project governance and inadequacies from project stakeholders.

The outcome of this study presents a roadmap for stakeholders in the construction industry with the requisite knowledge of the critical factors leading to the failure of JVs, consequently providing a clear path for the successful delivery of JV mandates.

Evidence from literature suggests that several studies have been conducted on the various aspects of JVs in the South African construction industry; however, none has focused on the leading factors attributed to the failure of JVs. Also, the findings of this study cultivate a good theoretical platform for future studies on JVs.

The purpose of this global study is to investigate the critical failure factors (CFFs) in the deployment of operational excellence (OPEX) programs as well as the key performance indicators (KPIs) that can be used to measure OPEX failures. The study also empirically analyzes various OPEX methodologies adopted by various organizations at a global level.

This global study utilized an online survey to collect data. The questionnaire was sent to 800 senior managers, resulting in 249 useful responses.

The study results suggest that Six Sigma is the most widely utilized across the OPEX methodologies, followed by Lean Six Sigma and Lean. Agile manufacturing is the least utilized OPEX methodology. The top four CFFs were poor project selection and prioritization, poor leadership, a lack of proper communication and resistance to change issues.

This study extends the current body of knowledge on OPEX by first delineating the CFFs for OPEX and identifying the differing effects of these CFFs across various organizational settings. Senior managers and OPEX professionals can use the findings to take remedial actions and improve the sustainability of OPEX initiatives in their respective organizations.

This study uniquely identifies critical factors leading to OPEX initiative failures, providing practical insights for industry professionals and academia and fostering a deeper understanding of potential pitfalls. The research highlights a distinctive focus on social and environmental performance metrics, urging a paradigm shift for sustained OPEX success and differentiating itself in addressing broader sustainability concerns. By recognizing the interconnectedness of 12 CFFs, the study offers a pioneering foundation for future research and the development of a comprehensive management theory on OPEX failures.

This study's main objective is to examine the effect of learning from entrepreneurial failure on performance, with a type of failure as a moderator variable. Interactions between internal and external causes of failure and learning from entrepreneurial failure are also investigated, as well as entrepreneurs' aspects (i.e. age, experience and education) and organisational contextual factors (i.e. size, sector and location).

This study employed a hypothetico-deductive approach through a survey of 250 purposively sampled entrepreneurs who had suffered business failures. The survey data were subjected to regression analysis and moderated regression using WarpPLS software and an independent sample t test for an in-depth analysis.

The results indicated that learning from entrepreneurial failure positively affected business performance, an effect moderated by the type of failure, particularly with large failures. Only perceived internal causes of failure exerted a positive effect on learning from entrepreneurial failure; the external causes did not. The effect of failure on business performance was stronger on entrepreneurs who were older and experienced, had non-university educations and operated small- and medium-sized enterprises (SMEs) outside Java–Bali islands.

This study's findings provide empirical evidence that supports the experiential learning theory and attribution theory in explaining the interaction between learning and failure, its cause, its consequences and its magnitude as perceived by entrepreneurs of SMEs in Indonesia, where the rate of failure is relatively high. The authors’ study also emphasises the roles of the entrepreneur and organisational contextual factors, which matter in learning to improve performance.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

The purpose of this paper is to propose an improved multi-objective optimization model for the condition-based maintenance (CBM) of single-component systems which considers periodic imperfect maintenance and ecological factors.

Based on the application of non-periodic preventive CBM, two recursion models are built for the system: hazard rate and the environmental degradation factor. This paper also established an optimal multi-objective model with a normalization process. The multiple-attribute value theory is used to obtain the optimal preventive maintenance (PM) interval. The simulation and sensitivity analyses are applied to obtain further rules.

An increase in the number of the occurrences could shorten the duration of a maintenance cycle. The maintenance techniques and maintenance efficiency could be improved by increasing system availability, reducing cost rate and improving degraded condition.

In reality, a variety of environmental situations may occur subsequent to the operations of an advanced manufacturing system. This model could be applied in real cases to help the manufacturers better discover the optimal maintenance cycle with minimized cost and degraded condition of the environment, helping the corporations better fulfill their CSR as well.

Previous research on single-component condition-based predictive maintenance usually focused on the maintenance costs and availability of a system, while ignoring the possible pollution from system operations. This paper proposed a modified multi-objective optimization model considering environment influence which could more comprehensively analyze the factors affecting PM interval.