Search results

This paper studies the impact of societal trust on the conservative financing policy puzzle, aiming to cover a gap in the relationship between cultural values and the conservative financing policy.

We use a sample of 14,509 privately held medium-sized manufacturing firms from 26 European countries between 2015 and 2020 and rely on logistic regression methods controlling for firm-specific and macroeconomic factors.

We show that societal trust decreases the odds of being a zero-leverage or almost zero-leverage firm. Also, the probability of being a conservatively financed firm increases for older and more profitable firms and decreases with tangibility. In more trustworthy national environments, firms are less averse to debt as a source of financing. Our results are robust to the specific measure of trust, estimation methods, sampling procedures, and annual financial constraint status. Moreover, we show that the effect is noticed both in the long-term debt and the short-term debt with a lower economic impact in the latter situation and that increased societal trust attenuates (reinforces) the effect of being a financially constrained (unconstrained) firm on the odds of adopting a conservative financing policy.

Societal trust strategically impacts debt financing policy and could help foster firms’ growth, particularly for those facing heavier financial constraints.

Novel evidence on the impact of societal trust on the conservative financing policy, for privately held medium-sized European firms.

Does societal trust influence short-term financial management? Recent papers uncover the importance of societal trust for financial management in specific countries and large firms. Our paper aims to provide a comprehensive analysis of the impact of societal trust on short-term financial policies of SMEs, namely working capital management and cash holdings.

We rely on a sample of 14,711 privately owned medium-sized manufacturing firms from 26 European countries with a sample period between 2014 and 2020. For estimation, we use pooled OLS and hierarchical linear models and control for several firm-specific and country-specific known determinants of short-term financial management. Moreover, our results are robust to the specific measurement of trust, financial constraints, and corruption.

We show a positive relationship between trust and working capital requirements investment and a negative relationship between trust and the level of cash holdings. Moreover, we show that trust attenuates the negative impact of being a financially constrained firm and the positive impact of national perceptions of corruption. Finally, in higher trustworthy environments, firms operate with relatively higher inventories and relatively lower trade credit granted and obtained.

Results suggest that policies supporting societal trust may also foster business development and that when dealing with clients or suppliers from different trustworthy environments, firms may have to adapt their business models to incorporate trust differences between business environments.

Firstly, the comprehensive analysis of the impact of trust on working capital management and cash holdings while controlling for different firm-level and country-level known determinants of short-term financial management. Secondly, it addresses a European sample of unquoted, medium-sized firms. Thirdly, it studies the combined effect of trust and financial constraints and trust and corruption.

The main goal of this paper is to study the specific characteristics of the performance of companies in the metallurgical sector, in the northern region of Portugal.

To achieve this aim, the authors have used data from 325 companies manufacturing metal products, except machinery and equipment (CAE Rev.3 25) and 27 companies that manufacture machinery and equipment (CAE Rev. 3 28). The models were estimated by using the panel data methodology for the period between 2011 and 2019. Specifically, the estimation method of the generalized method of moments system (GMM system) proposed by Arellano and Bover (1995) and Blundell and Bond (1998) was used.

The results show that the main decisions on the performance of metallurgical companies in Northern Portugal depend on the dimensions of sales in the domestic market (SDM), sales in the community market (SCM), and sales in the foreign market (SFM) and also highlight that the signal and significance of the specific variables depends on how the different stakeholders understand performance.

As far as the authors know, this is the first study to comparatively analyze the two metallurgical databases in Portugal. Despite the huge difference in the size of the sample, this study’s results show that in an era of paradigm shift about what business objectives should be, stakeholders are still not environmentally aware and the social dimension is only considered by shareholders, but not yet by the manager and the general community.

This paper aims to examine the effects on firms' outcomes of a new government regulation on the private security industry that aimed to enhance the selection and training processes for armed-private security officers.

By using human capital theory and using a data set built from various public sources, this study analyzes the effects of a new regulation implemented in 2013–2014 in Brazil mandating psychological assessments for hiring private security armed officers. Firm-level data and a Difference-in-Differences (DiD) identification strategy are used to investigate the effects on turnover and human capital outcomes.

The study identifies substantial changes resulting from the new government regulation in private security firms. While it has led to increased turnover rates, the regulation has also facilitated firms in enhancing the human capital composition of their workforce by enabling the recruitment of more experienced personnel.

This research informs to current debates on the effects of policy interventions on firm's outcomes by showing how regulations aimed to improve the configuration of human capital can generate win-win situations for both firms and citizens, despite the short-term trade-offs between higher turnover rates and improved human capital outcomes.

Refining selection and training processes can enhance the workforce in private security firms by replacing less capable professionals with more experienced ones. Insights from this study offer guidance to policymakers and industry practitioners in shaping effective business and public policies.

This study underscores the role of training and psychological assessments in enhancing the composition of human capital in the private security industry.

By highlighting the role of policy interventions in establishing barriers to unskilled workers engaging in hazardous activities, this study contributes to the burgeoning literature in strategic management on the interaction between policy interventions and firm outcomes.

The increased utilization of artificial intelligence-enabled applications (AI-ETs) across the customer journey has transformed customer experience (CX), introducing entirely new forms of the concept. This paper aims to explore existing academic research on the AI-enabled customer experience (AICX), identifying gaps in literature and opportunities for future research in this domain.

A systematic literature review (SLR) was conducted in March 2022. Using 16 different keyword combinations, literature search was carried across five databases, where 98 articles were included and analysed. Descriptive analysis that made use of the Theory, Characteristics, Context, Methods (TCCM) framework was followed by content analysis.

This study provides an overview of available literature on the AICX, develops a typology for classifying the identified AI-ETs, identifies gaps in literature and puts forward opportunities for future research under five key emerging themes: definition and dynamics; implementation; outcomes and measurement; consumer perspectives; and contextual lenses.

This study establishes a fresh perspective on the interplay between AI and CX, introducing the AICX as a novel form of the experience construct. It also presents the AI-ETs as an integrated and holistic unit capturing the full range of AI technologies. Remarkably, it represents a pioneering review exclusively concentrating on the customer-facing dimension of AI applications.

随着人工智能应用程序 (AI-ET)在旅途中的使用不断增加, 消费者体验 (CX)得以转变, 引入了全新的概念形式。 本文旨在探索有关人工智能客户体验（AICX）的现有学术研究, 从中找出文献中的空白以及该领域未来研究的机会。

本系统性文献综述（SLR）于2022 年 3 月开工。基于16 个不同的关键词组合, 本综述统共收录并分析了来自 5 个数据库98 篇文献, 采用理论-特征-背景-方法 (TCCM) 框架先后进行描述性分析和内容分析。

该研究概述了 AICX 的现有文献, 开发了对已识别的 AI-ET 进行分类的类型学, 确定了现有文献中的空白, 并在 5 个关键新兴主题下提出了未来研究的机会：1. 定义和动态, 2 . 实施, 3. 结果和衡量, 4. 消费者视角, 5. 情境视角。

本研究建立了全新的视角看待 AI 和 CX 之间的相互作用, 引入了 AICX 这种新颖的体验构造形式, 还将 AI-ET 展示为一个集成了全方位人工智能技术的整体单元。 值得一提的是, 本文代表了一项专门关注人工智能应用面向客户维度的开创性综述。

La creciente utilización de aplicaciones habilitadas por inteligencia artificial (AI-ET) a lo largo del recorrido del cliente han transformado la experiencia del cliente (CX), introduciendo formas totalmente nuevas del concepto. Este artículo pretende explorar la investigación académica existente sobre la experiencia del cliente a través de la IA (AICX), identificando las lagunas en la literatura y las oportunidades para futuras investigaciones en este ámbito.

En marzo de 2022 se llevó a cabo una revisión bibliográfica sistemática (SLR). Utilizando 16 combinaciones diferentes de palabras clave, se realizó una búsqueda bibliográfica en 5 bases de datos en las que se incluyeron y analizaron 98 artículos. El análisis descriptivo que hizo uso del marco Teoría, Características, Contexto, Métodos (TCCM) fue seguido del análisis de contenido.

El estudio ofrece una visión general de la bibliografía disponible sobre la AICX, desarrolla una tipología para clasificar las AICX detectadas, identifica lagunas en la literatura y plantea oportunidades para futuras investigaciones bajo cinco temas emergentes claves: 1. Definición y dinámica, 2. Implementación, 3. Resultados y medición, 4. Perspectivas del consumidor, 5. Lentes contextuales.

El estudio establece una nueva perspectiva sobre la interacción entre la IA y la CX, introduciendo la AICX como una forma novedosa del constructo experiencia. También presenta las AICX como una unidad integrada y holística que capta toda la gama de tecnologías de la IA. Notablemente, representa una revisión pionera que se concentra exclusivamente en la dimensión orientada al cliente de las aplicaciones de la IA.

The purpose of this study is to examine the moderating effect of an exogenous corporate governance shock that curbs Chief Executive Officers’ (CEOs) power on the relationship between CEO narcissism and earnings management practices.

The authors performed a quasi-experiment using a differences-in-differences approach to examine Brazil’s duality split regulatory change on 101 Brazilian public firms during the period 2010–2022.

The main findings indicate that the introduction of duality split curtails the positive influence of CEO narcissism on earnings management, suggesting that this corporate governance regulation may act as a complementary corporate governance mechanism in mitigating the negative consequences of powerful narcissistic CEOs. Further robustness checks indicate that the results remain consistent after using entropy balancing and alternative measures of CEO narcissism.

In emerging markets, where governance systems are frequently perceived as less than optimal, policymakers and regulatory authorities can draw insights from this enforcement to shape governance systems, reducing CEO power and, consequently, improving the quality of financial reporting.

To the best of the authors’ knowledge, this is the first study to examine whether a duality split mitigates the influence of CEO narcissism on earnings management. Thus, this study contributes to the corporate governance literature that calls for research on the effectiveness of external corporate governance mechanisms in emerging markets as well as the CEO narcissism literature that calls for research on moderating factors that could curtail negative consequences of narcissistic CEO behavior.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

This study aims to identify the effect of soft skills as a driver of audit quality and their moderating role in the relationship between stress and the propensity for auditors to engage in reduced audit quality practices (RAQP).

This study uses a sample of 130 auditors, whose data were collected through an electronic questionnaire. The results were derived from the partial least squares-structural equation modelling method.

The findings show that the propensity to incur RAQP increases when auditors are under job stressors but decreases when individuals have resilience and time management skills. Moreover, the results suggest that the moderating effect of these two soft skills can effectively reduce the auditors’ propensity to engage in dysfunctional actions and judgments in auditing. Emotional intelligence and self-efficacy skills are shown not to affect RAQP.

This study adds to previous research on auditors’ drivers for supplying audit quality, by providing evidence of auditor characteristics as a critical input to audit quality. The results emphasize the importance of researchers including in models the moderating effect of soft skills on the relationship between audit quality and determinants associated with audit firms, clients or the regulatory framework.