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This study aims to examine the correlation between the readability of financial statements and the likelihood of future stock price crashes in nonfinancial companies listed on the Egyptian Stock Exchange. It further explores the possible moderating effect of audit quality on this relationship.

The study uses ordinary least squares regression, generalized least squares estimation and two-stage least squares methodology to examine and validate the research hypotheses. The sample comprises 107 nonfinancial companies registered on the Egyptian Stock Exchange from 2016 to 2019.

The results reveal a significant negative association between the readability of financial statements and stock price crash risk. This suggests that companies with more complex financial statements tend to experience higher future crash risks. Additionally, the study identifies audit quality as a significant moderating factor. Higher audit quality, often indicated by engagements with Big-4 audit firms, strengthens the influence of financial statements readability on stock price crash risk. This implies that while high audit quality enhances investor confidence and market stability, it also accentuates the negative consequences of complex financial statements.

The findings of this paper have significant implications for regulators and standard-setting bodies in Egypt. They should consider refining and revising existing standards to emphasize the importance of enhancing the readability of financial reports. Additionally, auditing firms should actively engage in efforts to ensure clearer and more transparent financial reporting. These actions are vital for boosting investor confidence, strengthening Egypt’s capital market and mitigating potential risks associated with information opacity and complexity.

This study represents a pioneering endeavor within the Arab and Egyptian financial environments. To the best of the author’s knowledge, it is the first examination of the association between the readability of financial statements and stock price crash risk in these contexts. Furthermore, it explores factors such as audit quality that may influence this connection.

This paper aims to explore the audit quality and fee implications of joining a global accounting firm network and association (“AF N&A”).

A hand-collected sample focusing upon the pre- and post-periods around the time when an independent audit firm joins an AF N&A is developed. A propensity score-matched sample is created to address the endogeneity and self-selection bias. OLS regression is used on a sample of around 2,000 firm-year observations from 2003 to 2014.

Membership with an AF N&A is associated with higher levels of audit quality and audit fees. Furthermore, audit quality and fee increases are more pronounced for audit firms that become members of a larger, more formal AF N&A.

This paper provides additional insights into the conflicting results regarding the audit quality implications of membership with AF N&As in China. This paper also extends the discussion by exploring the audit quality and fee differentials among the non-Big Four AF N&As. These findings have significant implications for independent audit firms pursuing membership with an AF N&A and regulators seeking to reduce market concentration around the Big Four.

International construction joint ventures (ICJVs) will fully realize their potential for success and effectively monitor performance when an adequate and suitable performance benchmark is established. However, existing studies fall short of adequately providing a mutually acceptable benchmark for assessing the performance of ICJVs. This study aims to develop an adequate and suitable performance measurement framework for ICJVs.

A twofold structured questionnaire survey, supplemented by semi-structured interviews, was used to collect data from the practitioners of ICJVs hosted in the developing country of Ghana. The data were analyzed by using descriptive statistics, confirmatory factor analysis (CFA) and a hybrid-fuzzy logic approach.

A list of 30 performance indicators (PIs), defined by project performance, perceived satisfaction, company/partner performance, socio-environmental performance and performance of ICJV management, was validated and proved to be significant. Only 22 out of the 30 PIs, focusing on project efficiency, societal improvement and organizational goals are realized by the ICJV practitioners. Further, suitable determinants and viable quantitative ranges for measuring each PI are established to prevent different interpretations of the meanings of PIs and objectively express the level of success in quantitative terms. The results call for further investigation of the convergence between the practice of and research into some PIs (e.g. socio-environmental performance) and a range of different performance levels (PLs) in a more scientific manner.

This study not only advances the knowledge base and practice of performance measurement in ICJVs but could also assist stakeholders and decision-makers to assess, compare and monitor the performance of different ICJV projects on common grounds objectively.

This study not only comprehensively assessed PIs – what to measure – but also systematically determined suitable determinants – how to measure – for each PI.

This study aims to theoretically hypothesize and empirically examine the impact of economic policy uncertainty (EPU) on firms' innovation performance as well as the contingency conditions of this relationship.

This study collects and combines secondary longitudinal data from multiple sources to test for a direct impact of EPU on firms' innovation performance. It further examines the moderating effects of firms' operational and marketing capabilities. A series of robustness checks are performed to ensure the consistency of the findings.

In contrast to the common belief that EPU reduces the innovativeness of firms, the authors find an inverted-U relationship between EPU and innovation performance, indicating that a moderate level of EPU actually promotes innovation. Further analysis suggests that firms' operational and marketing capabilities make the inverted-U relationship steeper, further enhancing firms' innovation performance at a moderate level of EPU.

This study adds to the emerging literature that investigates the operational implications of EPU, which enhances our understanding of the potential bright side of EPU and broadens the scope of operational risk management.

To understand how Indian first-time mothers in STEM (Science, Technology, Engineering and Math) organisations returning to work cope with the perceived ideological psychological contract breaches from a work–home resources perspective.

This paper utilises interpretative phenomenological analysis (IPA) within the work–home resources (W-HR) model to analyse returning first-time mothers' lived experiences.

This study shows that significant life/work events such as childbirth/lack of career growth can trigger resource depletion at work and home and materialise in first-time mothers perceiving ideological psychological contract breaches at work. It has also been observed that key resource usage and macro support structures aid employees in attenuating work–home conflict by balancing contextual demands and personal resources. This study's participant accounts reveal that the recovery of volatile resources was possible by psychologically detaching and being silent.

The study offers a distinctive perspective by investigating the ideological PC breach experienced by first-time Indian mothers upon their organisational re-entry from a work–home resource model lens. Situated in a unique socio-cultural space and bringing forth the rich lived experiences of women working in the Indian STEM field, this paper explores how key resources shape the coping responses of first-time mothers in this context.

In the 21st century, libraries are experiencing a significant decline in users due to shifting reading habits and the impact of technology, necessitating library transformation and a heightened emphasis on library marketing. Special libraries, in particular, rely heavily on patrons for survival, often due to their private ownership and limited resources. This paper aims to discuss the aforementioned objectives.

This paper examines the Taste Library, a special library in Hong Kong, and analyzes its current practices based on an interview with its founder, website content, and social media presence. The 7Ps Marketing Mix model is employed to assess the strengths and weaknesses of the library's current market position.

The Taste Library's existing practices exhibit limitations in attracting young patrons. To address this issue, we propose marketing strategies focused on enhancing social network presence, offering digitized content, and engaging in school outreach.

By concentrating on youth marketing, this study offers valuable insights for special libraries in developing strategic plans for transitioning and maintaining sustainability.

Few studies concentrate on marketing small special libraries, particularly in the East, within today's digitized economy.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

This conceptual, multi-voiced paper aims to collectively explore and theorize family entrepreneuring, which is a research stream dedicated to investigating the emergence and becoming of entrepreneurial phenomena in business families and family firms.

Because of the novelty of this research stream, the authors asked 20 scholars in entrepreneurship and family business to reflect on topics, methods and issues that should be addressed to move this field forward.

Authors highlight key challenges and point to new research directions for understanding family entrepreneuring in relation to issues such as agency, processualism and context.

This study offers a compilation of multiple perspectives and leverage recent developments in the fields of entrepreneurship and family business to advance research on family entrepreneuring.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.