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The purpose of this research is the exploration of online complainants' revenge based on their consumer-brand relationship strength and received webcare. The authors introduce inter-failures (i.e. the perceived number of earlier independent service failures that a customer has experienced with the same brand involved in the current service failure) as the central frame condition.

To test our hypotheses, both a scenario-based online experiment (n = 316) and an online survey (n = 492) were conducted.

With an increasing number of inter-failures, online complainants with a high-relationship strength move from the “love is blind” effect (no inter-failures) to the “love becomes hate” effect (multiple inter-failures), when they ultimately become more revengeful than their low-relationship strength counterparts. In addition, the authors show that in the case of no or few inter-failures, accommodative webcare has a lasting positive effect over no/defensive webcare for both low- and high-relationship complainants. More importantly, however, when consumers have experienced multiple inter-failures, accommodative webcare becomes ineffective (for low-relationship complainants) or boomerangs by cultivating revenge towards the brand (among high-relationship complainants), but not strategic avoidance.

The findings have pronounced implications for the literature on customer–brand relationships following service failures and the literature, which predominantly emphasizes the unconditionally positive effects of accommodative webcare.

This study is the first that simultaneously considers the prior customer–brand relationship, inter-failures and webcare to explain online complainants' revenge.

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.

Prior research has advanced several explanations for social media influencers' (SMIs’) success in the burgeoning computer-mediated marketing environments but leaves one key topic unexplored: the moderating role of SMIs in service failure and recovery strategies.

Drawing on a social constructivist perspective and an inductive approach, 59 in-depth interviews were conducted with millennials from three European countries (Italy, France and the United Kingdom). Building on social influence theory and commitment-trust theory, this study conceptualises four distinct pathways unifying SMIs' efforts in the service failure recovery process.

The emergent model illustrates how source credibility and message content moderate service failure severity and speed of recovery. The insights gained from this study model contribute to research on the pivotal uniqueness of SMIs in service failure recovery processes and offer practical explanations of variations in the implementation of influencer marketing. This study examines a perspective of SMIs that considers the cycle of their influence on customers through service failure and recovery.

The study suggests that negative reactions towards service failure and recovery are reduced if customers have a relationship with influencers prior to the service failure and recovery compared with the reactions of customers who do not have a relationship with the influencer.

This study implements the fourth-order phase field method (PFM) for modeling fracture in brittle materials. The weak form of the fourth-order PFM requires C¹ basis functions for the crack evolution scalar field in a finite element framework. To address this, non-Sibsonian type shape functions that are nonpolynomial types based on distance measures, are used in the context of natural neighbor shape functions. The capability and efficiency of this method are studied for modeling cracks.

The weak form of the fourth-order PFM is derived from two governing equations for finite element modeling. C⁰ non-Sibsonian shape functions are derived using distance measures on a generalized quad element. Then these shape functions are degree elevated with Bernstein-Bezier (BB) patch to get higher-order continuity (C¹) in the shape function. The quad element is divided into several background triangular elements to apply the Gauss-quadrature rule for numerical integration. Both fourth-order and second-order PFMs are implemented in a finite element framework. The efficiency of the interpolation function is studied in terms of convergence and accuracy for capturing crack topology in the fourth-order PFM.

It is observed that fourth-order PFM has higher accuracy and convergence than second-order PFM using non-Sibsonian type interpolants. The former predicts higher failure loads and failure displacements compared to the second-order model due to the addition of higher-order terms in the energy equation. The fracture pattern is realistic when only the tensile part of the strain energy is taken for fracture evolution. The fracture pattern is also observed in the compressive region when both tensile and compressive energy for crack evolution are taken into account, which is unrealistic. Length scale has a certain specific effect on the failure load of the specimen.

Fourth-order PFM is implemented using C¹ non-Sibsonian type of shape functions. The derivation and implementation are carried out for both the second-order and fourth-order PFM. The length scale effect on both models is shown. The better accuracy and convergence rate of the fourth-order PFM over second-order PFM are studied using the current approach. The critical difference between the isotropic phase field and the hybrid phase field approach is also presented to showcase the importance of strain energy decomposition in PFM.

In this study, we question: how do the social costs of failure interact with gendered institutions to affect the early stage entrepreneurship activity? We address this question by employing the institutional theory and a unique dataset of 286,989 entrepreneurs across 35 countries.

To test our hypotheses, we use a multilevel modeling analysis that nests individual entrepreneurs within the countries. To capture individual and country-level variables, we constructed a unique dataset that combines data from the Global Entrepreneurship Monitor (GEM), European Flash Barometer (EUFB), World Bank Development Indicator (WDI), World Bank Doing Business Report (WBDB) and World Economic Forum (WEF).

Our analysis confirms that higher levels of the country-level gender equality positively correlate with the early-stage entrepreneurship activity of women. Moreover, we find that this positive relationship is amplified in institutional environments with high social costs of failure, suggesting that societal intolerance for failure can exacerbate the negative effect of gender inequality on the participation of women in entrepreneurship.

Our research contributes to academic interest on the role of legitimacy in women entrepreneurship and is of particular interest to international business scholars, seeking a better understanding of multidimensional construction of institutional frameworks across countries. In this study, we set out to address an important research question: how do the social costs of failure interact with gendered institutions to affect entrepreneurship activity? Our study provides a comprehensive portrait of gendered institutions by including the framework conditions of education, healthcare and political power. We found that in societies with gender equality, the likelihood of individuals engaging in the early-stage entrepreneurship activity is higher and that the positive relationship is strengthened in national environments with high social costs of failure.

Our study findings underscore the need for government policies addressing global gender gaps in economic empowerment. In particular, policies assisting women in obtaining education in high-growth industries like information technology or providing funding to women-dominated industries may foster activity for women seeking to do business in such industries. Such policies connect the early-stage entrepreneurship activities with gender equality concerns and initiatives.

Regarding the social costs of failure construct, specifically, prior studies generally focus narrowly on the context of failed entrepreneurs. We cast a wider net on men and women entrepreneurs’ entry decisions (irrespective of prior experience with business failure) and provide new views on the effects of social costs of failure on entrepreneurial ecosystems. We also extend the research on the legitimacy of women as entrepreneurs with the gender equality construct.

Unlike previous studies, which often focus on the “3Ms” of market, money and management, our research adopts a more holistic perspective. We recognize that the opportunities and challenges faced by entrepreneurs are shaped not only by individual skills and resources but also by the broader macroenvironment. By incorporating the framework conditions of education, healthcare and political power, alongside the intricate interplay of social costs and norms, our study paints a comprehensive picture of the landscape of female entrepreneurship.

The study of shame has a long tradition in intra- and inter-personal psychology. This paper aims to investigate whether consumers can experience brand shame after self-relevant consumption incidents. Specifically, this research proposes that consumers follow a complex shame-inducing process in the aftermath of unpleasant experiences involving their favorite brand. The moderating role of relational tie strength between consumers and their favorite brand existing prior to symbolic failures is examined.

A scenario-based, online survey (n = 660) among consumers who have recently experienced a self-relevant failure with their favorite brand was conducted. Confirmatory factor analysis ensured the reliability and validity of the measurement model. For testing the conceptual model, data was analyzed by means of a moderated mediation analysis. The proposed model was tested against, among others, common method bias and alternative models. The findings were cross-validated with a scenario-based online experiment (n = 1,616).

Results show that brand shame is a key mediator between customer dissatisfaction and brand anger when self-relevant, symbolic failures happen. Moreover, strong consumer-brand identification triggers brand-detrimental effects. It is shown to influence the connection between consumers’ inward- (i.e. brand shame) and resulting outward-directed (i.e. brand anger) negative emotions on brands, which lead to consumer vengeance.

To the best of the authors’ knowledge, this research is the first to introduce the concept of situational brand shame to the literature on favorite brands. Furthermore, it shows that consumer-brand identification moderates the direct and indirect (via brand shame) unfavorable effects of failure-induced dissatisfaction on brand anger. This research adds insights to the investigation of the “love-becomes-hate” effect arising after self-relevant failures involving consumers’ most preferred brand.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

Reliability centered maintenance (RCM) is a process used to determine activities to be taken to ensure an asset continues to perform asset's function in asset's present operating context by identifying asset's function, failure modes that could preclude performing asset's intended function, prioritizing failure modes and determining effective preventative maintenance tasks that can be cost effectively and efficiently implemented to reduce the likelihood of a failure.

A comprehensive survey of literature was undertaken to examine the current industry state of practice. Various industries were examined to better understand applications of RCM within the various industry sectors and determine those industries that RCM has not historically been readily adopted. A case study example of RCM applied to radial gates for water control in open channel canals for water conveyance is presented to demonstrate a civil infrastructure application.

The results found that RCM has been used since RCM's inception in the airline industry during the 1960s to reduce the cost of maintaining aircrafts. Over the past 40 years, an assortment of industries has begun implementing cost effective preventative maintenance tasks identified during RCM analysis. However, there is a noticeable lack of civil assets being analyzed by RCM, such as water conveyance systems and other civil infrastructure systems vital to the health and well-being of today's societies.

The comprehensive literature review of the current state of practice will provide a better understanding of the various applications of RCM to facilitate RCM's application to other industries, thereby reducing failure due to early identification of maintenance tasks. An example RCM demonstrates the application to a radial gate, used in water conveyance for the drinking water and irrigation sectors, which have not historically used RCM for developing maintenance strategies.

The interaction between rock mass structural planes and dynamic stress levels is important to determine the stability of rock mass structures in underground geotechnical engineering. In this work, the authors aim to focus on the degradation effects of fracture geometric parameters and unloading stress paths on rock mechanical properties.

A three-dimensional Particle Flow Code (PFC3D) was used for a systematic numerical simulation of the strength failure and cracking behavior of granite specimens containing prefabricated cracks under conventional triaxial compression and triaxial unilateral unloading. The authors demonstrated the unique mechanical response of prefabricated fractured rock under two conditions. The crack initiation, propagation, and coalescence process of pre-fissured specimens were analyzed in detail.

The authors show that the prefabricated cracks and unilateral unloading conditions not only deteriorate the mechanical strength but also have significant differences in failure modes. The degrading effect of cracks on model strength increases linearly with the decrease of the dip angle. Under the condition of true triaxial unilateral unloading, the deterioration effect of peak strength of rock is very significant, and unloading plays a role in promoting the instability failure of rock after peak, making the rock earlier instability failure. Associating with the particle vector diagram and crack coalescence process, the authors find that model failure mode under unilateral loading conditions is obviously distinct from that in triaxial loading. The peak strain in the unloading direction increases sharply, resulting in a new shear slip.

This study is expected to improve the understanding of the strength failure and cracking behavior of fractured rock under unilateral unloading.

To meet energy demand and tackle the challenges posed by global warming, Bagasse-based Cogeneration Power Generation (BCPG) plant in sugar mills have tremendous potential due to large-scale supply of renewable fuel called bagasse. To meet this goal, an integrated framework has been proposed for analyzing performance issues of BCPG.

Intuitionistic Fuzzy Lambda-Tau (IFLT) approach was implemented to compute various reliability parameters. Intuitionistic Fuzzy Failure Mode and Effect Analysis (IF-FMEA) approach has been implemented for studying risk issues results in decrease in plant's availability. Moreover, IF- Technique for Order Performance by Similarity to Ideal Solution (IF-TOPSIS) is implemented to verify accuracy of IF-FMEA approach.

For membership and non-membership functions, availability decreases to 0.0006% and 0.0020% respectively for spread ±15% to ±30%, and further decreases to 0.0127% and 0.0221% for spread ±30% to ±45%. Under risk assessment failure causes namely Storage tank (ST3), Valve (VL6), Transfer pump (TF8), Deaerator tank (DT11), High pressure heater and economiser (HP15), Boiler drum and super heater (BS22), Forced draft and Secondary air fan (FS25), Air preheater (AH29) and Furnace (FR31) with Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) based output scores – 0.8988, 0.9752, 0.9400, 0.8988, 0.9267, 1.1131, 1.0039, 0.8185, 1.0604 were identified as the most critical failure causes.

Reliability and risk analysis results derived from IFLT and IF-FMEA approaches respectively, to address the performance issues of BCPG is based on the quantitative and qualitative data collected from the industrial experts and maintenance log book. Moreover, to take care of hesitation in expert's knowledge, IF theory-based concept is incorporated so as to achieve more accuracy in analysis results. Reliability and risk analysis results together will be helpful in analyzing the performance characteristics and diagnosis of critical failure causes, which will minimize frequent failure in BCPG.

The framework will help plant managers to frame optimal maintenance policy in order to enhance the operational aspects of the considered unit. Moreover, the accurate and early detection of failure causes will also help managers to take prudent decision for smooth operation of plant.

The results obtained ensure continuous operation of plant by utilizing the bagasse as fuel in boiler and also mitigate the wastages of fuel. If this bagasse (green fuel) is not properly utilized, there remains a dependency on coal-based power plants to meet the power demand. The results obtained are useful for decreasing dependency on coal, and promoting bagasse as the green, and alternative fuel, the emission by burning of these fuels are not harmful for environment and thereby contribute in preventing the environment from harmful effect of GHGs gases.

IFLT approach has been implemented to develop reliability modeling equations of the BCPG unit, and furthermore to compute various reliability parameters for both membership and non-membership function. The ranking results of IF-FMEA are compared to IF-TOPSIS approach. Sensitivity analysis is done to check stability of proposed framework.