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The purpose of this paper is to propose a dynamic capabilities framework in cybersecurity (DCCI) and explore its potential to explain organizations’ cybersecurity intelligence (CI). This study aims to develop and empirically test the DCCI framework, which encompasses key dimensions for enhancing CI across various business sectors.

The DCCI framework was designed through an extensive literature review and tested using data from 207 cybersecurity experts across diverse regions. Hierarchical regression analysis was used to examine the contribution of the DCCI framework’s components to CI.

Results indicate a positive association between the “doing” and “improving” dimensions of the DCCI framework and the development of CI. However, the “enabling” and “managing” dimensions did not exhibit a significant contribution to CI. These findings suggest that CI is a multifaceted concept shaped by certain dynamic capabilities within cybersecurity practices.

The DCCI framework introduces a structured approach to developing and enhancing CI, with practical applications for businesses and broader societal contexts aiming to improve cybersecurity strategies and resilience.

This study provides a novel framework for DCCI, offering valuable insights into how specific dynamic capabilities support CI. By addressing gaps in cybersecurity models, the framework adds practical and theoretical dimensions to understanding CI and the evolving needs of cybersecurity.

The objective of this article is to evaluate and compare the performance of two machine learning (ML) algorithms, i.e. support vector machines (SVMs) and random forests (RFs), when classifying seven states of operation of an electric motor using the Mel-frequency cepstral coefficients (MFCCs) as extracted representative features.

The extracted MFCCs are calculated using the motor’s vibration and audio signals separately.

After the training, the SVM model obtained a mean accuracy of 100% for the MFCCs obtained from database vibration signals and 69.6% for the database of audio signals.

The ML strategies and results reported are limited to the well-known data for industrial electric motors used in the evaluations, although it was performed tests and cross-validations with unseen data and the information from the confusion matrix.

The success of these methodologies in defect classification, where the RF presented a mean accuracy of 99.15% for the vibration signals and 63.82% for the audio signal, enables the use of this ML and extracted features as a predictive tool for failure and anomaly detection, lifetime predictions and online real-time monitoring.

It is the first time that the MFCCs are being used for anomaly detection in vibration and audio signals for electrical motors, as this extracted feature is usually used for human speech identification in the literature.

This study aims to identify the primary research areas of modern methods of construction (MMC) along with its current trends and developments.

A combination of bibliometric and qualitative analysis is adopted to examine 1,957 MMC articles in the Scopus database. With the support of CiteSpace 6.1.R6, the clusters, leading authors, journals, institutions and countries in the field of MMC are examined.

Offsite construction, inter-modular connections, augmenting output, prefabricated concrete beams and earthquake-resilient prefabricated beam–column steel joints are the top five research areas in MMC. Among them, offsite construction and inter-modular connections are significantly focused, with many research articles. The potential for collaboration, among prominent authors such as Wang, J., Liu, Y. and Wang, Y., explains the recent rapid growth of the MMC field of research. With a total of 225 articles, Engineering Structures is the journal that has published the most articles on MMC. China is the leading country in this field, and the Ministry of Education China is the top institution in MMC.

The findings of this study bear significant implications for stakeholders in academia and industry alike. In academia, these insights allow researchers to identify research gaps and foster collaboration, steering efforts toward innovative and impactful outcomes. For industries using MMC practices, the clarity provided on MMC techniques facilitates the efficient adoption of best practices, thereby promoting collaboration, innovation and global problem-solving within the construction field.

The paper tries to investigate the impact of applications of the resource-based view (RBV) theory in the management field to improve the firm’s profitability. Global firms are innovating and adopting new technology, paving the way to improve their performance.

We have adopted RBV in management practices such as marketing, strategy, finance, and human resources.

RBV has gained researchers' attention with the growing competitive world and new challenges to retaining customers and achieving their pre-defined targets. We attempt to identify the issues related to the usage of RBV in management.

Using RBV in management may help researchers create a competitive mindset and be prepared for uncertain challenges in the business world.

This study addresses the global landscape of offsite construction, highlighting its variable adoption patterns and the challenge posed by the prevalent use of suboptimal decision-making methods. In response, the decision-making model seeks to equip decision-makers with tools for well-informed decisions on concrete construction systems, tailored to the unique characteristics of each project, in contrast to the persisting reliance on expert knowledge, checklists or similar tools.

The study extracts decision-making criteria through literature reviews, pilot studies and surveys amongst Australian construction professionals. A comprehensive comparison of four concrete systems against each identified criterion is conducted, followed by the application of an integrated decision model (Entropy-TOPSIS) to rank the systems, considering all criteria simultaneously. Real-world case studies validate the practical applicability of the model.

An analysis of 15 criteria demonstrated the multifaceted nature of selecting concrete construction systems, emphasising evolving industry priorities like time efficiency, environmental considerations and logistical constraints. The enduring appeal of in-situ concrete in complex projects underscores the significance of traditional methods. The integration of the Entropy-TOPSIS model proved to be a robust decision-making tool, enabling professionals to simultaneously consider all criteria and make well-informed, customised decisions.

The study’s originality lies in its comprehensive approach, considering diverse criteria and presenting a flexible decision-making model suitable for the dynamic demands of the construction industry.

In the field of digital entrepreneurship, achieving high growth in digital start-ups is crucial. This paper aims to explore how causal conditions at the individual, organizational and environmental levels interact in the growth process of digital start-ups. Furthermore, it uses a configurational approach to conduct a holistic analysis of the cross-level interaction mechanisms that enable digital start-ups to achieve high growth.

Based on a holistic perspective, the data collection included 57 representative cases of digital start-ups in China in the past decade (2014–2023). A fuzzy set qualitative comparative analysis was conducted to explore the cross-level interaction mechanism of the three-dimensional causal conditions of the environment, organization and individual entrepreneurs on high growth among digital start-ups.

This study found three models for digital start-ups to achieve high growth. These models − the resource network orchestration model, the innovation resource development model and the entrepreneurial spirit coherence model, respectively, reflect the multiple paths of high growth achieved by various digital start-ups based on their entrepreneurial environment, organizational capability and the personality of the founder. This reflects that digital start-ups face different conditions and entrepreneurial situations, with differing cross-level interaction mechanisms for achieving high growth.

First, digital start-ups should focus on strengthening their digital resource integration capabilities to lay the foundation for their subsequent high-growth path selection. Second, entrepreneurs need to adopt a holistic perspective to change or strengthen the causal conditions required to achieve high growth. Third, emphasis should be placed on the coherence between the entrepreneur’s need for achievement and the internal entrepreneurial culture.

First, this study contributes to the literature on high growth among digital start-ups by introducing a cross-level interaction mechanism comprising environmental, organizational and entrepreneurial causal conditions. Second, it reveals that individual factors at the environmental, organizational and entrepreneurial levels are not necessary conditions for high entrepreneurial growth and that digital resource integration capabilities play a more universal role in achieving high digital start-up growth. Finally, the study provides a cross-level holistic explanation of how digital start-ups can achieve high growth and reveals various configuration models conducive to high growth, thus offering practical insights for businesses.

Wine consumer behavior has long been a topic of discussion among scholars and industry professionals aiming to understand the underlying predictors of key behavioral outcomes. To help explain wine consumer behavior, concepts such as involvement, expertise, loyalty, satisfaction and perceived risk are often examined. The overarching objective of this study is to determine the relationship between these predictors and their impact on wine purchase intention utilizing a meta-analytical structural equation modeling (MASEM) technique.

As MASEM provides substantive evidence regarding the relationships between theoretical constructs through the combination of multiple studies, the researchers’ aim is to make definitive statements about the predictors of purchase intention.

Findings revealed several relationships that support previous research but also identified relationships that contradict previous literature. This study contributes valuable insights into consumer behavior that wine brands can utilize to improve their marketing efforts.

Wine marketers with a greater understanding of the stronger predictors of purchase intention should be able to create marketing plans that drive wine sales.

Despite the abundance of research that has utilized these theoretical constructs to demonstrate their propensity for determining behavioral outcomes such as purchase intention, no previous attempts have synthesized this body of literature through the use of meta-analysis.

E-commerce companies employ deceptive techniques known as dark patterns (DPs) to manipulate customer decisions on online platforms. DPs are known to annoy and generate negative emotions in the consumers, which might be detrimental to a brand. Consequently, this study examines the influence of DPs on brand experience (BE) and consumer-based brand equity (CBBE) from the lens of stimulus-organism-response theory in an e-commerce organization.

Survey data was obtained from 419 respondents who frequently make transactions on e-commerce platforms. Subsequently, the authors used partial least squares-based structural equation modeling (PLS-SEM) via the R programming language to analyze the relationships.

It was found that DPs negatively impact the BE and CBBE of an e-commerce firm. Furthermore, BE positively impacts CBBE as well as mediates the relationship between DPs and CBBE. Additionally, no significant moderating influence of shopping frequency was discovered for the relation between DPs and BE.

The study investigates the connection between DPs, BE and CBBE in a general e-commerce context. Results may differ across specialized e-commerce sectors.

The study’s insights could motivate e-commerce managers to reevaluate their use of DPs. The study could help online platforms reorganize their business strategies and adopt a customer-centric approach, which might create a long-term positive relationship with customers. The results might spark discussions over redressing customer relations after previous exploitation via DPs.

To the best of the authors’ knowledge, this study is the first to empirically assess the impact of DPs on crucial brand parameters like BE and CBBE.

Available studies on anticorruption practices in the construction industry are exploratory with a very limited theoretical basis. This paper aims to provide a solid theoretical foundation to examine situational factors that influence the corruption intentions of individuals and organizations in the construction industry.

This paper conducts a systematic literature review to synthesize construction management literature that suggests anticorruption practices. The identified practices are then examined using two theoretical frameworks: the fraud diamond theory and Lange’s corruption control framework.

The results of this research demonstrate how the four elements of the fraud diamond theory may trigger corrupt behavior in construction projects. The results also highlight conceptual distinctions among different means of corruption controls based on Lange’s corruption control framework. In addition, the findings of this research suggest that anticorruption practices should address (1) the incentives to act corruptly, (2) the normative means of corruption control, (3) the internal means of preventing corruption and (4) the shift in organizational culture.

This paper departs from prior research on corruption in construction projects by (1) identifying a large spectrum of anticorruption practices, (2) presenting a detailed theoretical interpretation of these practices and (3) viewing anticorruption practices as multidimensional constructs, which, in turn, leads to novel ways of examining these practices in construction projects.

This research analyses the impact of experiential marketing events on word-of-mouth for destination brands while considering the moderating role of brand familiarity and the mediating role of brand equity.

The study is based on a survey of 844 participants in a real event organised by a leisure Spanish destination with a large inflow of national and international attendees, in conjunction with the city government where it is located.

Attendees’ evaluation of the event exerted a significant positive effect on word-of-mouth. However, brand equity – which is built over time via past marketing actions – mediates this relationship. Meanwhile, brand familiarity moderates the relationship between people’s overall evaluation of the event, brand equity and positive recommendations.

These results offer guidance to destination managers regarding reoccurring experiential marketing events. Participants with high appraisal of events serve to create buzz and amplify the event experience for others.

The study is a novel attempt to analyse the influence of visitors’ experiences on brand destination outcomes by considering the moderating role of brand familiarity – an issue that, to our knowledge, has not been assessed in the context of events. The results make clear that mere exposure to a brand does not necessarily lead to brand equity and WOM. Instead, managers must design experiential events carefully – not only to convert sporadic visitors into frequent customers but also to keep consistent clients from losing interest.