Search results

This study investigates the impact of internal expectations, such as fantasy of success and fear of failure and external factors, such as social environment and past experiences, on entrepreneurial choice.

Based on achievement motivation and social cognitive theories, the authors construct hypotheses and use secondary data from the Global Entrepreneurship Monitor (GEM) database and Economic Freedom Index report to empirically test the hypotheses. The authors also use propensity score matching to solve the endogeneity issue and test the robustness.

Internal expectations (fantasy of success and fear of failure) on business outcomes inversely affect entrepreneurial choices, with a vibrant business environment amplifying and past failure experience mitigating these effects.

Due to the economic recession, governments encourage small businesses. Thus, the complexity of individual entrepreneurial motivations and influencing factors necessitate deeper exploration. This study is one of the first research offering insights into entrepreneurial motivations from combined dimensions and providing theoretical support for strategies promoting public entrepreneurship.

Steam explosions are a major safety concern in many modern furnaces. The explosions are sometimes caused by water ingress into the furnace from leaks in its high-pressure (HP) cooling water system, coming into contact with molten matte. To address such safety issues related to steam explosions, risk based inspection (RBI) is suggested in this paper. RBI is presently one of the best-practice methodologies to provide an inspection schedule and ensure the mechanical integrity of pressure vessels. The application of RBIs on furnace HP cooling systems in this work is performed by incorporating the proportional hazards model (PHM) with the RBI approach; the PHM uses real-time condition data to allow dynamic decision-making on inspection and maintenance planning.

To accomplish this, a case study is presented that applies an HP cooling system data with moisture and cumulated feed rate as covariates or condition indicators to compute the probability of failure and the consequence of failure (CoF), which is modelled based on the boiling liquid-expanding vapour explosion (BLEVE) theory.

The benefit of this approach is that the risk assessment introduces real-time condition data in addition to time-based failure information to allow improved dynamic decision-making for inspection and maintenance planning of the HP cooling system. The work presented here comprises the application of the newly proposed methodology in the context of pressure vessels, considering the important challenge of possible explosion accidents due to BLEVE as the CoF calculations.

This paper however aims to optimise the inspection schedule on the HP cooling system, by incorporating PHM into the RBI methodology, as was recently proposed in the literature by Lelo et al. (2022). Moisture and cumulated feed rate are used as covariate. At the end, risk mitigation policy is suggested.

In this paper, the proposed methodology yields a dynamically calculated quantified risk, which emphasised the imperative for mitigating the risk, as well as presents a number of mitigation options, to quantifiably affect such mitigation.

The paper follows Jason Cope's (2011) vision of a holistic perspective on the failure-based learning process. By analyzing the research since Cope's first attempt, which is often fragmentary in nature, and providing novel empirical insights, the paper aims to draw a new comprehensive picture of all five phases of entrepreneurial learning and their interplay.

The study features an interpretative phenomenological analysis of in-depth interviews with 18 failed entrepreneurs. Findings are presented and discussed in line with experiential learning theory and Cope's conceptual framework of five interrelated learning timeframes spanning from the descent into failure until re-emergence.

The study reveals different patterns of how entrepreneurs experience failure, ranging from abrupt to gradual descent paths, different management and coping behaviors, and varying learning effects depending on the new professional setting (entrepreneurial vs non-entrepreneurial). Analyzing the entrepreneurs' experiences throughout the process shows different paths and connections between individual phases. Findings indicate that the learning timeframes may overlap, appear in different orders, loop, or (partly) stay absent, indicating that the individual learning process is even more dynamic and heterogeneous than hitherto known.

The paper contributes to the field of entrepreneurial learning from failure, advancing Cope's seminal work on the learning process and -contents by providing novel empirical insights and discussing them in the light of recent scientific findings. Since entrepreneurial learning from failure is a complex and dynamic process, using a holistic lens in the analysis contributes to a better understanding of this phenomenon as an integrated whole.

Bolted joint is the most important connection method in aircraft composite/metal stacked connections due to its large load transfer capacity and high manufacturing reliability. Aircraft components are subjected to complex hybrid variable loads during service, and the mechanical properties of composite/metal bolted joint directly affect the overall safety of aircraft structures. Research on composite/metal bolted joint and their mechanical properties has also become a topic of general interests. This article reviews the current research status of aeronautical composite/metal bolted joint and its mechanical properties and looks forward to future research directions.

This article reviews the research progress on static strength failure and fatigue failure of composite/metal bolted joint, focusing on exploring failure analysis and prediction methods from the perspective of the theoretical models. At the same time, the influence and correlation mechanism of hole-making quality and assembly accuracy on the mechanical properties of their connections are summarized from the hole-making processes and damage of composite/metal stacked structures.

The progressive damage analysis method can accurately analyze and predict the static strength failure of composite/metal stacked bolted joint structures by establishing a stress analysis model combined with composite material performance degradation schemes and failure criteria. The use of mature metal material fatigue cumulative damage models and composite material fatigue progressive damage analysis methods can effectively predict the fatigue of composite/metal bolted joints. The geometric errors such as aperture accuracy and holes perpendicularity have the most significant impact on the connection performance, and their mechanical responses mainly include ultimate strength, bearing stiffness, secondary bending effect and fatigue life.

Current research on the theoretical prediction of the mechanical properties of composite/metal bolted joints is mainly based on ideal fits with no gaps or uniform gaps in the thickness direction, without considering the hole shape characteristics generated by stacked drilling. At the same time, the service performance evaluation of composite/metal stacked bolted joints structures is currently limited to static strength and fatigue failure tests of the sample-level components and needs to be improved and verified in higher complexity structures. At the same time, it also needs to be extended to the mechanical performance research under more complex forms of the external loads in more environments.

The mechanical performance of the connection structure directly affects the overall structural safety of the aircraft. Many scholars actively explore the theoretical prediction methods for static strength and fatigue failure of composite/metal bolted joints as well as the impact of hole-making accuracy on their mechanical properties. This article provides an original overview of the current research status of aeronautical composite/metal bolted joint and its mechanical properties, with a focus on exploring the failure analysis and prediction methods from the perspective of theoretical models for static strength and fatigue failure of composite/metal bolt joints and looks forward to future research directions.

Adhesive bonding is critical to the effectiveness and structural integrity of 3D printed components. The purpose of this study is to investigate the effect of joint configuration on failure loads to improve the design and performance of single lap joints (SLJs) in 3D printed parts.

In this study, adherends were fabricated using material extrusion 3D printing technology with polyethylene terephthalate glycol (PETG). A toughened methacrylate adhesive was chosen to bond the SLJs after adherend printing. In this study, response surface methodology (RSM) was used to examine the effect of the independent variables of failure load, manufacturing time and mass on the dependent variable of joint configuration; adherend thickness (3.2, 4.0, 4.8, 5.6, 6.4, and 7.2 mm) and overlap lengths (12.7, 25.4, 38.1, and 50.8 mm) of 3D printed PETG SLJs.

The strength of the joints improved significantly with the increase in overlap length and adherend thickness, although the relationship was not linear. The maximum failure load occurred with a thickness of 7.2 mm and an overlap of 50.8 mm, whilst the minimum failure load was determined with a thickness of 3.2 mm and an overlap of 12.7 mm. The RSM findings show that the optimum failure load was achieved with an adherend thickness of 3.6 mm and an overlap length of 37.9 mm for SLJ.

This study provides insight into the optimum failure load for 3D printed SLJs, reducing SLJ production time and mass, producing lightweight structures due to the nature of 3D printing, and increasing the use of these parts in load-bearing applications.

While the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail expectations and may even pose negative impacts on user experience. The purpose of the study is to empirically explore the negative user experience with chatbots and understand how users respond to service failure caused by chatbots.

This study adopts a qualitative research method and conducts thematic analysis of 23 interview transcripts.

It identifies common areas where chatbots fail user expectations and cause service failure. These include their inability to comprehend and provide information, over-enquiry of personal or sensitive information, fake humanity, poor integration with human agents, and their inability to solve complicated user queries. Negative emotions such as anger, frustration, betrayal and passive defeat were experienced by participants when they interacted with chatbots. We also reveal four coping strategies users employ following a chatbots-induced failure: expressive support seeking, active coping, acceptance and withdrawal.

Our study extends our current understanding of human-chatbot interactions and provides significant managerial implications. It highlights the importance for organizations to re-consider the role of their chatbots in user interactions and balance the use of human and chatbots in the service context, particularly in customer service interactions that involve resolving complex issues or handling non-routinized tasks.

While third-party food delivery continues to increase in popularity, surveys suggest nearly a quarter of deliveries suffer from service failures. With the limited research on third-party food delivery, we explore the important questions of (1) where customers place blame in the case of service failures with third-party food delivery (i.e. the platform or the restaurant) and (2) does this depend on the type of service failure? Drawing on blame attribution theory, signaling theory, and an exploratory study, we demonstrate that customers typically perceive such mishaps to be the responsibility of the restaurant rather than the delivery platform itself. We also examine the effect of visible service failure preventative actions taken by the restaurant on blame attribution and re-order intention.

We conducted two online scenario-based studies to explore customer blame attribution in the case of third-party food delivery service failure. First, an exploratory study approach (N_{Study1 = 512}) was taken to provide additional support for the hypothesis development. An experiment (N_{Study2 = 252}) was then conducted to examine the hypothesized effects.

First, the results of an exploratory study demonstrate that customers attribute service failures such as wrong items, missing items, cold food, or leaking containers to restaurants over third-party food delivery platforms. Second, the results of an experimental study suggest inclusion of an observable cue indicating preventative action, such as time-stamp information indicating when an order was received and packaged for delivery, increases customer re-order intention through the underlying mechanism of blame attribution.

We contribute to the underexplored area of third-party food delivery service failure and to our understanding of blame attribution in service failure scenarios. Further, we demonstrate a practical method to shift the blame away from restaurants for service failures that are outside of the establishment’s control.

This paper continues the reporting of the Unit's work on a method of investigating how often readers fail to find what they are looking for on the shelves, why they fail, and what particular books they fail to find. The method used is a slight modification of the earlier one—readers were asked to record on a slip provided the details of the book or periodical they were looking for, or subject area they were looking in, their academic status, and the date, and then to place the slip in an adjoining box. From analysis of these ‘failure’ slips it was possible to determine the cause of the reader's failure, the individual book the reader was looking for, and the pattern of failure for different groups of volumes. A new development was surveys of samples of readers, carried out during the fortnight of peak demand. Answers to these surveys provided information on the effect of failure on a reader's work, what action he took after failing, whether he found adequate substitutes, how many books he found by browsing, and what proportion of the books he consulted he borrowed. Some of these answers could be checked by a direct count of the books used in, and borrowed from, the library. The investigations were carried out in three University libraries, each with its own characteristic library structure and teaching patterns. These differences were reflected in the results obtained. It is hoped that the methods of investigation used can be employed by librarians to investigate the effectiveness of some of the services in their own libraries.

Describes research into the problem of estimating the distribution parameters of failure data of industrial equipment subject to two failure modes. It is assumed that time to failure data are available, but it is unknown for each failure which of the two main failure modes caused the event. A loglikelihood method was developed and tested on generated mixed failure mode data. Shortcomings of this method triggered the development of two additional methods, MINESS and MINESS+, which minimize the error sum of squares of the reliability function, after separating the failure data into two sets.

In the design of anisotropic materials, such as advanced fibre‐reinforced composites, the failure envelope has a crucial role. A geometrical representation of the envelope is of particular value to investigate the highly anisotropic nature of the strength of the material. This provides the design engineer with a visualization of the failure envelope by graphically representing it using a CAD package. Use is made of both stress‐based and strain‐based polynomial failure criteria and examples are given of changes in the failure envelope due to changes in fibre orientation for a high strength graphite/epoxy composite lamina.