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Merely being known as a highly ethical or strong performer cannot shield a company from every kind of crisis. From product failures to environmental and social issues, a brand’s ability to manage crises and rapidly regain consumer trust is essential. This study aims to explore whether consumer perceptions of a brands’ prior commitments to two different areas of corporate responsibility (social and product responsibility) alleviate the postcrisis attribution of accountability and further build brand resilience, examining differences between two types of crisis situations – values versus performance crises.

A scenario-based online survey on product versus ethical labor issues was conducted. The data were collected from a highly valid, nationwide sample set of more than a thousand US consumers. Multigroup structural equation modeling was used as the primary data analysis method.

A brand’s precrisis commitment to social responsibility was found to decrease attribution of accountability across both types of crises. It also strengthened brand resilience, but this effect was more prevalent in a performance than a values crisis. The effects of precrisis commitment to product responsibility on brand resilience were minimal or insignificant across crisis types.

Previous research underexplores which types of corporate responsibility commitments provide a firm with a better protection against crises. This study significantly advances the knowledge regarding the type of commitments that can substantially increase brand resilience, which supports the rationale of making stronger commitments to social responsibility than to product responsibility. Practical insights are provided into how investments in corporate social responsibility help alleviate consumers’ negative perceptions during the outbreak of a brand crisis and build more brand muscle that enables resilience against future crises.

This paper seeks to address issues related to the development of a knowledge‐based engineering system for estimating manufacturing cost and weight of a composite structure at the conceptual stage of a design.

The system has been developed in the CATIA V5 knowledge environment and is applied to structures made of composite materials. At the conceptual stage of the design process, a structure is often represented by simple surfaces. The system adds the details necessary to accurately estimate weight and manufacturing cost using geometry and process‐based techniques. Knowledge captured from an expert was used to construct the knowledge base in the system.

It has been found that the system can provide continuous tracking of the weight and cost as the design evolves. Structural FEA and optimisation using MSC.NASTRAN have been integrated into the design process to enables the designer to conduct “what‐if” analyses to explore different design options involving geometry parameters such as the internal configuration of the structure.

The paper demonstrates that tools embedded in CAD systems can be expected to be able to facilitate the task of estimation of weight and manufacturing cost at the conceptual stage of the design process.

This paper aims to propose a data mining approach to evaluate a conceptual model in tourism, encompassing a large data set characterized by dimensions grounded on existing literature.

The approach is tested using a guest satisfaction model encompassing nine dimensions. A large data set of 84 k online reviews and 31 features was collected from TripAdvisor. The review score granted was considered a proxy of guest satisfaction and was defined as the target feature to model. A sequence of data understanding and preparation tasks led to a tuned set of 60k reviews and 29 input features which were used for training the data mining model. Finally, the data-based sensitivity analysis was adopted to understand which dimensions most influence guest satisfaction.

Previous user’s experience with the online platform, individual preferences, and hotel prestige were the most relevant dimensions concerning guests’ satisfaction. On the opposite, homogeneous characteristics among the Las Vegas hotels such as the hotel size were found of little relevance to satisfaction.

This study intends to set a baseline for an easier adoption of data mining to evaluate conceptual models through a scalable approach, helping to bridge between theory and practice, especially relevant when dealing with Big Data sources such as the social media. Thus, the steps undertaken during the study are detailed to facilitate replication to other models.

This paper investigates a retrospective on the Journal of Derivatives and Quantitative Studies (JDQS) on its 30th anniversary based on bibliometric. JDQSs yearly publications, citations, impact factors, and centrality indices grew up in early 2010s, and diminished in 2020. Keyword network analysis reveals the JDQS's main keywords including behavioral finance, implied volatility, information asymmetry, price discovery, KOSPI200 futures, volatility, and KOSPI200 options. Citations of JDQS articles are mainly driven by article age, demeaned age squared, conference, nonacademic authors and language. In comparison between number of views and downloads for JDQS articles, we find that recent changes in publisher and editorial and publishing policies have increased visibility of JDQS.

Although there have been a lot of studies on mobile business, it is hard to find ones in which an integrated perspective of mobile business based on both customers’ and companies’ needs is suggested. This research develops a mobile business model classification scheme and applies it to analysis of current status in mobile business. In the first case study on 65 firms in Korea, mobile solutions are categorized based on the classification scheme in order to analyze an overall market environment of mobile business. In the second case study on mobile service trend analysis, a set of evaluation indices including four general indices, ten effectiveness indices and 13 measurement indices were developed to analyze mobile service environment in the perspective of B2C (business to customer). For the second case study, 200 mobile heavy users were questioned on the mobile service‐related issues.