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Exploiting a unique measure of hostile takeover exposure principally based on the staggered adoption of state legislations, the authors investigate how external audit quality is influenced by the discipline of the takeover market. External auditors and the takeover market both function as important instruments of external corporate governance.

The authors execute a standard regression analysis and run a variety of robustness checks to minimize endogeneity, namely, propensity score matching (PSM), entropy balancing, an instrumental-variable analysis, Generalized method of moment (GMM) dynamic panel data analysis and Lewbel's (2012) heteroscedastic identification.

The authors’ immense sample spans half a century, encompassing nearly 180,000 observations and 17 takeover-related state legislations, one of the largest samples in the literature in this area. The authors’ results suggest that firms with more takeover exposure are significantly less likely to use Big N auditors. Therefore, a more active takeover market results in poorer external audit quality, corroborating the substitution hypothesis. The discipline of the takeover market substitutes for the necessity for a high-quality external auditor. Specifically, a rise in takeover susceptibility by one standard deviation lowers the probability of using a Big N auditor by 4.29%.

The authors’ study is the first to examine the effect of the takeover over market on audit quality using a novel measure of hostile takeover susceptibility mainly based on the staggered implementation of state legislation. Because the enactment of state legislation is beyond the control of any firm individually, it is plausibly exogenous. The authors’ results therefore probably reflect a causal influence rather than merely a correlation.

This study aims to investigate the relationship between chief executive officer (CEO) power and the level of tax avoidance of Tunisian listed companies. It also examines the moderating role of institutional ownership in this association.

The sample comprises 306 firm-year observations of companies listed on the Tunis Stock Exchange during the 2013–2020 period.

The results indicate that CEO power reduces tax avoidance levels. Moreover, the relationship between CEO power and tax avoidance is more pronounced in the presence of institutional ownership, suggesting that CEOs act less opportunistically when monitored by institutional investors, which results in a reduction in tax avoidance.

This study suggests that CEO power and institutional shareholders’ influence are important factors in determining firms’ avoidance behavior. This study has significant implications for shareholders and regulatory bodies. Indeed, shareholders apprehend the impact of appointing a powerful CEO on tax avoidance practices. This study may also provide regulators with new insights into the influence of CEO power dimensions and institutional ownership on tax aggressiveness.

This study fills the gap in the accounting literature by investigating how CEO power may impact tax avoidance behavior and provides empirical evidence on the moderating impact of institutional ownership on this relationship in an emerging economy context characterized by a weakly protected investor setting.

Recreational scuba diving is one of the fastest growing tourism industries around the world and has become a popular activity in Taiwan. Few studies focus on enduring involvement with a high risk or difficult activity specialization, so this study aims to explore the causal relationship between enduring involvement and specialization and seeks to explain sustained involvement in scuba diving activity as a recreation specialization.

This research selects four diving sites in Taiwan as survey locations, specifically Yeliou, Longdong, Kenting and Green Island. The questionnaire consists of three sections, including demographic information, enduring involvement and specialization. By analyzing the data collected from 810 scuba divers in Taiwan, structural equation modeling is used to examine the causal relationships among the variables.

The primary findings of this study are as follows: attraction positively affects divers’ commitment and lifestyle through joy, relaxation and sharing diving experiences; the results indicate that self-expression is associated with past experience of participating in scuba diving activity; and centrality indicates that participants’ daily life and recreation are related to each other and become central to their life. Results show that most theoretical hypotheses are supported, but there is no significant evidence of attraction impacting past experience or self-expression influencing commitment and lifestyle.

To the best of the authors’ knowledge, this study is the first to examine the causal influence of enduring involvement and specialization in the scuba diving context. The findings provide a solid theoretical basis for the study of sustained involvement motivation and behavior on specialization. Implications and recommendations for future research are discussed.

休闲水肺潜水是全球发展最快的旅游业之一, 已成为台湾的热门活动。很少有研究关注持续涉入高风险或困难活动的专门化, 因此本研究探讨持续涉入和专门化之间的因果关系, 并试图解释持续参与水肺潜水活动作为休闲专门化。

这项研究选择了台湾的四个潜水地点作为调查地点, 特别是野柳, 龙洞, 垦丁和绿岛。问卷分为三个部分, 包括人口统计信息, 持续参与和专门化。通过分析从台湾810名潜水员那里收集的数据, 使用结构方程模型检查变量之间的因果关系。

这项研究的主要发现是：（1）吸引力通过愉悦, 放松和分享潜水经历积极影响潜水员的承诺和生活方式; （2）结果表明自我表达与过去参加水肺潜水活动的经历有关（3）中心性表示参与者的日常生活和娱乐彼此相关, 并成为他们生活的中心。结果表明, 大多数理论假设均得到支持, 但没有显着证据表明吸引力会影响过去的经历或自我表达影响承诺和生活方式。

据我们所知, 本研究是第一个研究在潜水环境中持续涉入和专门化的因果关系影响的研究。研究结果为专门化持续参与动机和行为的研究提供了坚实的理论基础。 讨论了对未来研究的影响和建议。

El buceo recreativo es una de las industrias turísticas de más rápido crecimiento en todo el mundo y se ha convertido en una actividad popular en Taiwán. Pocos estudios se centran en la participación duradera con una especialización de actividad difícil o de alto riesgo, por lo que este estudio explora la relación causal entre la participación duradera y la especialización y busca explicar la participación sostenida en la actividad de buceo como una especialización recreativa.

Esta investigación selecciona cuatro sitios de buceo en Taiwán como lugares de estudio, específicamente Yeliou, Longdong, Kenting y Green Island. El cuestionario consta de tres secciones, que incluyen información demográfica, participación duradera y especialización. Al analizar los datos recopilados de 810 buzos en Taiwán, se utiliza un modelo de ecuaciones estructurales para examinar las relaciones causales entre las variables.

Los hallazgos principales de este estudio son: (1) la atracción afecta positivamente el compromiso y el estilo de vida de los buceadores a través de la alegría, la relajación y el intercambio de experiencias de buceo, (2) los resultados indican que la autoexpresión está asociada con la experiencia pasada de participar en actividades de buceo., y (3) la centralidad indica que la vida diaria y la recreación de los participantes están relacionadas entre sí y se vuelven centrales en su vida. Los resultados muestran que la mayoría de las hipótesis teóricas están respaldadas, pero no hay evidencia significativa de que la atracción afecte la experiencia pasada o la autoexpresión que influya en el compromiso y el estilo de vida.

Hasta donde sabemos, este estudio es el primero en examinar la influencia causal de la participación y la especialización duraderas en el contexto del buceo. Los hallazgos proporcionan una base teórica sólida para el estudio de la motivación y el comportamiento de participación sostenida en la especialización. Se discuten las implicaciones y recomendaciones para futuras investigaciones.

The role of customers has moved from reactive to proactive, and they require more control over digital touchpoints. The quest for authenticity is their response to the dark side of interactive marketing – forms of faking, manipulation and exploitation. Authenticity becomes a key topic in interactive marketing as it reflects how customers assess digital touchpoints. However, there is a lack of comprehensive knowledge of authenticity in the interactive environment.

This article consolidates the authenticity studies in various digital touchpoints using the entity-referent correspondence framework. This research employs bibliometric analysis and thematic analysis of 103 articles in the last 15 years.

Five research clusters are identified: (1) human, (2) brand-generated content, (3) user-generated content, (4) branded platforms and (5) new technologies-based touchpoints (artificial intelligence, augmented reality and virtual reality). Most interactive marketing studies focus on human and content authenticity, and new technologies-based touchpoints lack comprehensive conceptualization. The review synthesizes the types of authenticity used in each touchpoint and highlights the importance of true-to-creator-self and true-to-customer-self in customer evaluation. We further propose a research framework with four antecedent groups and outcomes.

Our research supports managers by highlighting the type of authenticity prioritized in each touchpoint's development.

To answer the call from interactive marketing researchers, this research highlights the distinct definitions of authenticity at various digital touchpoints rather than looking at the overall brand. Trends, gaps and future research agenda of the authenticity concept in technology adoption and customer behavior are discussed.

This study aims to investigate whether auditors compromise their independence for economically important clients in family business settings.

The authors empirically examine the research question based on China for the years 2011 to 2020. The dependent variable is the auditors’ propensity to issue modified audit opinions, which is a proxy for auditor independence. The authors use relative client audit fees as a proxy for client importance. To address endogeneity issues in the selection of family firms, the authors use the two-stage least squares regression model and, subsequently, the propensity score matching and Hausman firm fixed effect modeling.

This study reveals that the propensity to issue modified audit opinions is positively correlated with client importance. Big-N auditors are more likely to issue modified audit opinions for their economically important family firm clients, whereas such evidence is not found for non-Big-N auditors. Results are consistent and robust to endogeneity test and sensitivity analysis.

This study enriches the literature on auditor independence and the effect of family firms’ ownership structure factors on audit reporting behavior for their economically important clients. Findings may prove useful for managers and practitioners interested in family business.

This study aims to investigate why and how shared service centres (SSCs) are implemented as well as how they affect audit firm practice and audit quality.

In this qualitative study guided by the theoretical framework of institutional theory, the authors conducted 25 semi-structured interviews in seven European countries, including 16 interviews with audit partners from Big 4 firms, 6 with audit team members, 2 with interviewees from second-tier audit firms and 1 with a member of an oversight body.

The authors show that the central rationale for audit firms to implement SSCs is economic rather than external legitimacy. The authors find that SSC implementation has substantial effects on audit practices, particularly those related to standardisation, coordination and monitoring activities. The authors also highlight the potential impacts on audit quality.

By exploring the motivation for and effects of SSC implementation amongst audit firms, the authors offer insights into the best practices related to subsequent change processes and audit quality.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.

The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.

The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.

Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

This paper aims to study the economic effects of Cloud Computing for a sample of Israeli firms. The authors propose a framework that considers how this technology affects firm performance also introducing the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.

The model is estimated through structural equation modeling. The data set consists of the microdata of the survey of information and communication technologies uses and cyber protection in business conducted in Israel by the Central Bureau of Statistics.

The results point to Cloud Computing as a crucial technology to increase firm performance, presenting significant direct and indirect effects as the use of complementary technologies maximizes its impact. Firms that enjoy most direct economic gains from Cloud Computing appear to be the smaller ones, although larger enterprises seem more capable to assimilate complementary technologies, such as Big Data and Machine Learning. The total effects of cloud on firm performance are quite similar among manufacturing and service firms, although the composition of the different effects involved is different.

This paper is one of the very few analyses estimating the impact of Cloud Computing on firm performance based on country microdata and, to the best of the authors’ knowledge, the first one that contemplates the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.